CN102572847B - Spectrum allocation method and system - Google Patents

Abstract

The invention relates to a spectrum allocation method and system. The spectrum allocation method includes the following steps: dividing the registered frequency band into multiple channels, obtaining the state of each channel in the current sensing cycle; predicting the state of each channel in the next sensing cycle according to the state of each channel in the current cycle Obtain the transmission demand of the cognitive user and the basic parameters of each channel, predict the expected transmission time of the cognitive user on each channel according to the transmission demand of the cognitive user and the basic parameters of each channel; calculate The total available transmission capacity of multiple channels in a sensing period; calculate the probability that the channel meets the transmission demand in the next sensing period; set constraints, and calculate the total bandwidth of multiple channels in the next sensing period under the constraints The minimum value is used to obtain the allocation results of multiple channels in the next sensing cycle. The above frequency spectrum allocation method and system can rationally allocate multiple channels and improve the utilization rate of frequency spectrum.

Description

Spectrum allocation method and system

【Technical field】

The present invention relates to the field of wireless communication, in particular to a frequency spectrum allocation method and system.

【Background technique】

Frequency is a non-renewable resource, so spectrum utilization has always been a concern of everyone. The current spectrum allocation mechanism is generally fixed. The advantage of this mechanism is that the communication equipment works in a certain fixed frequency band, and at the same time, the system parameters of each access network can be obtained, and the complexity of the equipment is small. However, the fixed spectrum allocation method makes a large amount of spectrum underutilized, because some spectrum can only be used in a specific geographical environment or time period, while spectrum resources are idle in other regions or other time periods. Therefore, if a new spectrum mechanism can be introduced to achieve dynamic spectrum allocation according to specific geographical conditions and real-time needs of users, the spectrum utilization rate will be greatly improved.

The traditional spectrum allocation method mainly uses the Markov chain (Markovian) model to predict whether the spectrum is available before data transmission. The bandwidth allocated to cognitive users is small, resulting in a serious imbalance in the allocation of spectrum resources.

【Content of invention】

Based on this, it is necessary to provide a spectrum allocation method to rationally allocate spectrum and improve spectrum utilization.

A spectrum allocation method, comprising the following steps:

Divide the registered frequency band into multiple channels, and obtain the status of each channel in the current sensing cycle;

predicting the state of each channel in the next sensing cycle according to the state of each channel in the current cycle;

Obtain the transmission demand of the cognitive user and the basic parameters of each channel, predict the expected transmission time of the cognitive user on each channel according to the transmission demand of the cognitive user and the basic parameters of each channel, the basic parameters Including the forward transmission rate and feedback transmission rate of the channel, the bandwidth of the channel, and the total amount of information transmitted on the channel;

According to the state of each channel in the predicted next sensing period, the bandwidth of each channel and the expected transmission time of cognitive users on each channel, calculate the available total transmission capacity of multiple channels in the next sensing period;

Calculate the probability that the channel meets the transmission requirements in the next sensing cycle;

According to the probability that the channel meets the transmission demand in the next sensing period and the available total transmission capacity of the multiple channels, the constraint condition is set, and under the constraint condition, the minimum of the total bandwidth of the multiple channels in the next sensing period is calculated value, and obtain the allocation results of multiple channels in the next sensing period.

Preferably, the constraint condition is that the probability that the channel meets the transmission requirement in the next sensing period is greater than a conditional threshold, and the total available transmission capacity of multiple channels in the next sensing period is greater than or equal to the transmission demand of the cognitive user.

Preferably, the specific step of predicting the state of each channel in the next sensing cycle according to the state of each channel in the current cycle includes:

Establishing a Markov chain model of the channel state, the channel state includes an idle state and an occupied state, and the idle state and the occupied state of the channel state are represented by an exponential distribution function;

Set the state threshold, calculate the available probability of each channel in the next sensing cycle according to the channel state in the current sensing cycle, compare the available probability of each channel with the state threshold, if the available probability of the channel is greater than or equal to the specified The state threshold, the channel state in the next sensing period is an idle state, otherwise the channel state in the next sensing cycle is an occupied state.

Preferably, the calculation of the probability that the channel in the next sensing period meets the transmission requirement specifically includes:

Comparing the expected transmission time of cognitive users in each channel to obtain the maximum expected transmission time, and calculating the probability that the idle state time of the channel is less than the maximum expected transmission time;

According to the calculated probability that the idle state time of the channel is less than the maximum expected transmission time, the probability that the channel meets the transmission requirement in the next sensing period is obtained.

Preferably, the specific steps of obtaining the forward transmission rate or feedback transmission rate of the channel are:

Set the time window and the interval between sending probe packets;

Calculate the number of probe packets sent according to the time window and interval;

Obtain the number of detection packets received within the set time window;

The ratio of the number of probe packets received to the number of probe packets sent is calculated to obtain the forward transmission rate or feedback transmission rate of the channel.

Preferably, the calculation formula of the expected transmission time of the channel is:

${\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

Among them, ETT _i is the expected transmission time of channel i, M _i is the total amount of information transmitted on channel i, B _i is the bandwidth of channel i, γ is the correction parameter of channel i capacity, is the forward transmission rate of channel i, is the feedback transmission rate of channel i, and SINR _i is the signal-to-noise ratio of channel i.

In addition, it is also necessary to provide a spectrum allocation system to rationally allocate spectrum and improve spectrum utilization.

A spectrum allocation system comprising:

The scanning module is used to divide the registered frequency band into multiple channels, and obtain the status of each channel in the current sensing period;

A prediction module, configured to predict the state of each channel in the next sensing cycle according to the state of each channel in the current cycle;

The parameter acquisition module is used to obtain the transmission demand of cognitive users and the basic parameters of each channel, the basic parameters include the forward transmission rate and feedback transmission rate of the channel, the bandwidth of the channel, and the total amount of information transmitted on the channel;

A calculation module, configured to calculate the expected transmission time of the cognitive user on each channel according to the transmission demand of the cognitive user and the basic parameters of each channel, and according to the state of each channel in the predicted next sensing period , the bandwidth of each channel and the expected transmission time of cognitive users in each channel, calculate the total available transmission capacity of multiple channels in the next sensing cycle, and calculate the probability that the channel meets the transmission demand in the next sensing cycle;

The channel allocation module is configured to set constraint conditions according to the probability that the channel meets the transmission demand in the next sensing period and the available total transmission capacity of multiple channels, and under the constraint conditions, calculate multiple channels in the next sensing period The minimum value of the total bandwidth of the channel is used to obtain the allocation result of multiple channels in the next sensing cycle.

Preferably, the constraint condition is that the probability that the channel in the next sensing period meets the transmission requirement is greater than a conditional threshold, and the total available transmission capacity of the multiple channels in the next sensing period is greater than or equal to the transmission demand of the cognitive user.

Preferably, the prediction module includes a modeling unit, a setting unit, a calculation unit, and a judging unit, the modeling unit is used to establish a Markov chain model of a channel state, and the channel state includes an idle state and an occupied state, so The idle state and the occupied state of the channel state are represented by an exponential distribution function; the setting unit sets a state threshold; the calculation unit calculates the available probability of each channel in the next sensing cycle according to the channel state in the current sensing cycle; The judging unit is used to compare the available probability of each channel with the state threshold, if the available probability of the channel is greater than or equal to the state threshold, the channel state in the next sensing cycle is idle state, otherwise the next During the sensing period, the channel state is occupied state.

Preferably, the calculation module is also used to compare the expected transmission time of cognitive users on each channel to obtain the maximum expected transmission time, calculate the probability that the idle state time of the channel is less than the maximum expected transmission time, and obtain according to the calculation The probability that the idle state time of the channel is less than the maximum expected transmission time is obtained, and the probability that the channel meets the transmission demand in the next sensing cycle is obtained.

Preferably, the parameter acquisition module includes a transmission rate processing unit, the transmission rate processing unit sets the time window and the interval time for sending probe packets, calculates the number of send probe packets according to the time window and interval time, and obtains the The number of probe packets received within the set time window is calculated to obtain the ratio of the number of received probe packets to the number of sent probe packets to obtain the forward transmission rate or feedback transmission rate of the channel.

Preferably, the formula for calculating the expected transmission time of the channel by the calculation module is:

${\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

Among them, ETT _i is the expected transmission time of channel i, M _i is the total amount of information transmitted on channel i, B _i is the bandwidth of channel i, γ is the correction parameter of channel i capacity, is the forward transmission rate of channel i, is the feedback transmission rate of channel i, and SINR _i is the signal-to-noise ratio of channel i.

The spectrum allocation method and system above adopts the method of obtaining the state of each channel in the current sensing cycle, predicting the state of each channel in the next sensing cycle, calculating the expected transmission time of the cognitive user on each channel, and then calculating the next cycle The available total transmission capacity of multiple channels and the probability that the channel meets the transmission demand, set constraints, and solve the minimum value of the total bandwidth of multiple channels under the constraints, so as to obtain the allocation of multiple channels in the next cycle As a result, in this way, a plurality of channels can be reasonably allocated, and the utilization rate of spectrum is improved.

【Description of drawings】

Fig. 1 is a flowchart of a spectrum allocation method in an embodiment;

Fig. 2 is a flow chart of the method for obtaining the forward transmission rate of the channel in one embodiment;

FIG. 3 is a schematic structural diagram of a spectrum allocation system in an embodiment;

FIG. 4 is a schematic diagram of the internal structure of the prediction module in FIG. 3 .

【Detailed ways】

The technical solution will be described in detail below in conjunction with specific embodiments and accompanying drawings.

As shown in Figure 1, in one embodiment, a spectrum allocation method includes the following steps:

Step S10, divide the registered frequency band into multiple channels, and obtain the status of each channel in the current sensing period.

In step S10, the frequency spectrum is a radio frequency spectrum resource, usually referring to long wave, medium wave, short wave, ultrashort wave and microwave, generally referring to the general term for radio frequencies emitting radio waves within the frequency range of 9KHz to 3000GHz (frequency unit: Hertz Hz). Part of the entire frequency spectrum has been registered for use, and this frequency spectrum is called a registered frequency band. The registered frequency band is divided into multiple channels, and the bandwidth of channel i is B _i . The multiple channels have been authorized to the primary user, and are registered channels. Registered users refer to authorized users who have priority to use the spectrum. Cognitive users refer to unauthorized users, who dynamically track the user's spectrum occupancy and communicate on the unused idle spectrum. When the registered user reuses the spectrum, the cognitive user needs to return the spectrum to the registered user. Cognitive users can scan multiple channels in the current sensing cycle to obtain the status of each channel in the current sensing cycle. Cognitive users only need to scan once to obtain the status of each channel in the current period, and the status of each channel in each subsequent period can be obtained through prediction. The perception cycle is the interval time for the cognitive user to regularly scan. The perception cycle can be set by the user at the beginning, and can be adjusted according to the transmission result after the actual data transmission.

Step S20, predicting the state of each channel in the next sensing cycle according to the state of each channel in the current cycle. Cognitive user scans to obtain the state of each channel in the current sensing cycle, and can predict the state of each channel in the next sensing cycle adjacent to the current sensing cycle.

In one embodiment, the specific steps of step S20 include:

(1) Set up the Markov chain model of channel state, this channel state comprises idle state and occupied state, and the idle state of channel state and occupied state adopts exponential distribution function to represent.

The idle state of the channel means that the channel is not dedicated to the registered user, and the occupied state means that the channel is dedicated to the registered user. The Markov chain model of the channel state is established to predict the conversion of the two states of the channel. The state of the channel is represented by S _i , S _i ∈ (0(BUSY), 1(IDLE)). The time length of the idle state and the occupied state of the channel is represented by a random variable, the idle time length in the idle state is represented by a random variable Y _i ; the occupied time length in the occupied state is represented by a random variable _Xi . The time durations of channel states are independently exponentially distributed, and this stochastic process forms a continuous-time Markov chain.

For each channel i, the time length y _i of each idle state (i.e. idle state) follows the mean value The exponential distribution of , such as formula (1):

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}& {\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

For each channel i, the time length x _i of each occupied state (busy state) follows the mean value The exponential distribution of , such as formula (2):

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

(II) Set the state threshold, calculate the available probability of each channel in the next sensing cycle according to the channel state in the current sensing cycle, compare the available probability of each channel with the state threshold, if the available probability of the channel is greater than or equal to the state threshold , then the state of the channel in the next sensing period is idle state, otherwise it is occupied state. Status thresholds can be set by the user, and then adjusted based on the results of experiments performed.

According to the static communication model of the registered users, the available probability of the channel is predicted. The available probability of the channel is the probability that the channel is idle. The probability of channel i being idle in the next sensing cycle is calculated as follows:

${{\mathrm{<span\; class="notranslate">\; P</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}_{\mathrm{<span\; class="notranslate">\; idle</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}& {\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; IDLE</span>}\\ \frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}& {\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; BUSY</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In formula (3), P ⁱ _idle is the probability of channel i being idle in the next sensing period, Δt _i is the interval between two adjacent observation time slots, S _i =IDLE or S _i =BUSY is the channel i in the current sensing period status.

Comparing the calculated available probability of the channel in the next sensing cycle with the state threshold, the state of the channel in the next sensing cycle can be obtained.

Step S30, obtain the cognitive user's transmission demand and the basic parameters of each channel, predict the cognitive user's expected transmission time in each channel according to the cognitive user's transmission demand and the basic parameters of each channel, the basic parameters Including the forward transmission rate and feedback transmission rate of the channel, the bandwidth of the channel, and the total amount of information transmitted on the channel.

In step S30, the transmission demand of the cognitive user is represented by M. The basic parameters of the channel include the forward transmission rate and feedback transmission rate of the channel, the bandwidth of the channel, and the total amount of information transmitted on the channel.

Among them, the forward transmission rate and the feedback transmission rate of the channel are the probability of successful transmission. When the cognitive user transmits a data packet, not every transmission will be successful, and multiple transmissions are required to succeed. Therefore, there is an expected value of transmission times (ETX), which refers to the expected value of the number of times required to successfully deliver a data packet. The expected value of the number of transmissions can be calculated by the forward transmission rate and the feedback transmission rate. The forward transmission rate d _f represents the probability that a data packet successfully arrives at the receiving end, and the feedback transmission rate d _r refers to the successful reception of the ACK (the confirmation flag in the header of the TCP data packet, which confirms the received TCP message) packet probability. Therefore, the probability that a data packet is successfully received and fed back is d _f *d _r , and if the sender does not receive the ACK packet, it will retransmit the data packet.

Each transmission attempt can be regarded as a Bernoulli experiment, and the expected value of the number of transmissions can be calculated by the following formula:

$\mathrm{<span\; class="notranslate">\; ETX</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In formula (4), ETX is the expected value of transmission times, d _f is the forward transmission rate, and d _r is the feedback transmission rate.

In a preferred embodiment, as shown in FIG. 2, obtaining the forward transmission rate d _f or the feedback transmission rate d _r of the channel specifically includes the following steps:

Step S300, setting a time window and an interval for sending detection packets. Set the time window w, the interval time τ between sending probe packets.

Step S310, calculate and obtain the number of sending detection packets according to the time window and the interval time. w/τ is the number of probe packets sent within the set time window.

Step S320, acquiring the number of probe packets received within the set time window. Within the set time window w, the number of probe packets received at the receiving end is Count(t-w, t).

Step S330, calculating the ratio of the number of received probe packets to the number of sent probe packets to obtain the forward transmission rate or feedback transmission rate of the channel. The calculation formula of channel forward transmission rate is as formula (5):

${\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; count</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Use the same method to obtain the feedback transmission rate.

The number of channels is set as N, and the bandwidth of channel i is B _i . The total amount of information transmitted on channel i is M _i , That is, the total amount of information transmitted on N channels is the transmission demand M of cognitive users.

According to the transmission demand of cognitive users and the basic parameters of each channel, the expected transmission time of cognitive users in each channel is predicted. The calculation formula of expected transmission time of cognitive users in each channel is shown in formula (6):

${\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; ETX</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}$

In formula (6), ETT _i is the expected transmission time of channel i, M _i is the total amount of information transmitted on channel i, B _i is the bandwidth of channel i, γ is the correction parameter of channel i capacity, is the forward transmission rate of channel i, is the feedback transmission rate of channel i, SINR _i is the signal-to-noise ratio of channel i, and B _i log(1+SINR _i ) is the Shannon formula for calculating channel capacity.

Step S40, according to the predicted state of each channel in the next sensing cycle, the bandwidth of each channel and the expected transmission time of cognitive users on each channel, calculate the total available transmission capacity of multiple channels in the next sensing cycle .

In step S40, according to the predicted state of each channel in the next sensing period, the bandwidth of each channel and the expected transmission time of cognitive users on each channel, the total available transmission capacity of N channels can be calculated . The calculation formula is as formula (7):

$\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In formula (7), J is the total transmission capacity of N channels.

Step S50, calculating the probability that the channel meets the transmission requirement in the next sensing cycle. Wherein, the probability that the channel satisfies the transmission requirement refers to the probability that the available transmission time of the channel meets the transmission requirement of the cognitive user.

The specific steps for calculating the probability that the channel meets the transmission requirement in step S50 include:

a. Comparing the expected transmission time of cognitive users in each channel to obtain the maximum expected transmission time, and calculating the probability that the idle state time of the channel is less than the maximum expected transmission time.

The maximum expected transmission time T _max is:

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Max</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

When the idle state time (idle time) of the licensed channel is less than the maximum expected transmission time, channel switching will occur. Therefore, spectrum switching occurs at when is the idle time of channel i.

The idle time of registered users can be estimated by an exponential distribution with parameter λ, so the channel switching probability can be expressed as:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; idl</span>}{\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

In formula (9), _Pi is the switching probability of channel i.

b. According to the calculated probability that the idle state time of the channel is less than the maximum expected transmission time, the probability that the channel meets the transmission requirement in the next sensing cycle is obtained.

The probability that the channel meets the transmission requirements, that is, the probability that the idle time of the channel is greater than or equal to the maximum expected transmission time, that is, the probability that spectrum switching is not required, is represented by Pa, and the calculation formula is as follows:

Pa=P(T _idel ≥ _{T max} )=1-P(T _idel <T _max ) (10)

Step S60, set the constraint condition according to the probability that the channel meets the transmission demand in the next sensing period and the total available transmission capacity of multiple channels, under the constraint condition, calculate the minimum of the total bandwidth of the multiple channels in the next sensing period value, and obtain the allocation results of multiple channels in the next sensing cycle.

In step S60, the constraint condition is that the probability that the channel meets the transmission demand in the next sensing period is greater than the condition threshold, and the total available transmission capacity of multiple channels in the next sensing period is greater than or equal to the transmission demand of the cognitive user. Among them, the formula for the probability Pa that the channel meets the transmission demand in the next sensing period is greater than the conditional threshold δ is as in formula (11):

Pa=P(T _idel ≥T _max )=1-P(T _idel <T _max )>δ (11)

The conditional threshold δ is set by the user, and then adjusted according to the results of channel usage.

The total available transmission capacity J of multiple channels in the next sensing period is greater than or equal to the transmission demand M of the cognitive user, and the calculation formula is as follows (12):

$\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

Under the two constraints, the minimum value of the total bandwidth of multiple channels is obtained, namely

After a plurality of channels are allocated, within the obtained sensing period, if the cognitive user is allocated a channel, data transmission will be performed; if no channel is allocated, the next sensing period adjacent to the next sensing period will continue to be predicted, Re-predict the channel and perform reallocation.

In one embodiment, as shown in FIG. 3 , a spectrum allocation system includes a scanning module 10 , a prediction module 20 , a parameter acquisition module 30 , a calculation module 40 and a channel allocation module 50 .

The scanning module 10 is used to divide the registered frequency band into multiple channels, scan the multiple channels, and obtain the status of each channel in the current sensing period. Part of the entire spectrum is allocated to registered users, and this spectrum is called a registered frequency band. The registered frequency band is divided into multiple channels, and the bandwidth of channel i is B _i . The multiple channels have been authorized to the primary user, and are registered channels. Registered users refer to authorized users who have priority to use the spectrum. Cognitive users refer to unauthorized users, who dynamically track the user's spectrum occupancy and communicate on the unused idle spectrum. When the registered user reuses the spectrum, the cognitive user needs to return the spectrum to the registered user. The cognitive user scans a plurality of channels through the scanning module 10 in the current sensing period, and can obtain the status of each channel in the current sensing period. The scanning module 10 only needs to scan once to obtain the state of each channel in the current cycle, and the state of each channel in each subsequent cycle can be obtained through prediction. The perception cycle is the interval time for the cognitive user to regularly scan. The perception cycle can be set by the user at the beginning, and can be adjusted according to the transmission result after the actual data transmission.

The prediction module 20 is used to predict the state of each channel in the next sensing cycle according to the state of each channel in the current cycle. The state of each channel in the current sensing period is acquired through scanning by the cognitive user, and the prediction module 20 may predict the state of each channel in the next sensing period adjacent to the current sensing period.

In a preferred embodiment, as shown in FIG. 4 , the forecasting module 20 includes a modeling unit 200 , a setting unit 210 , a calculating unit 220 and a judging unit 230 .

The modeling unit 200 is used to establish a Markov chain model of the channel state. The channel state includes an idle state and an occupied state, and the idle state and the occupied state of the channel state are represented by an exponential distribution function.

The idle state of the channel is idle, and the occupied state is busy (the channel is dedicated to registered users), and the Markov chain model of the channel state is established to predict the conversion of the two states of the channel. The state of the channel is represented by S _i , S _i ∈ (0(BUSY), 1(IDLE)). The time length of the idle state and the occupied state of the channel is represented by a random variable, the idle state is idle, and the idle time length is represented by a random variable Y _i ; the occupied state is busy, and the busy time length is represented by a random variable _Xi . The time durations of channel states are independently exponentially distributed, and this stochastic process forms a continuous-time Markov chain.

For each channel i, the time length y _i of each idle state (i.e. idle state) follows the mean value The exponential distribution of , such as formula (1):

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}& {\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

For each channel i, the time length x _i of each occupied state (busy state) follows the mean value The exponential distribution of , such as formula (2):

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

The setting unit 210 sets a status threshold. The status threshold can be set by the user, and then adjusted according to the results of the experiment.

The calculation unit 220 calculates the availability probability of each channel in the next sensing period according to the channel state in the current sensing period. According to the static communication model of the registered users, the available probability of the channel is predicted. The available probability of the channel is the probability that the channel is idle. The probability of channel i being idle in the next sensing period is calculated as follows:

${{\mathrm{<span\; class="notranslate">\; P</span>}}^{\mathrm{<span\; class="notranslate">\; i</span>}}}_{\mathrm{<span\; class="notranslate">\; idle</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}& {\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; IDLE</span>}\\ \frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}& {\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; BUSY</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In formula (3), P ⁱ _idle is the probability that channel i is idle in the next sensing cycle, Δt _i is the interval between two adjacent observation time slots, and S _i =IDLE or S _i =BUSY is the probability of channel i being idle in the current sensing cycle The state of the channel.

The judging unit 230 is used to compare the available probability of each channel with the state threshold, if the available probability of the channel is greater than or equal to the state threshold, the channel state in the next sensing cycle is idle state, otherwise the channel state in the next sensing cycle is occupancy state.

The parameter acquisition module 30 is used to acquire the transmission demand of cognitive users and the basic parameters of each channel, the basic parameters include the forward transmission rate and feedback transmission rate of the channel, the bandwidth of the channel, and the total amount of information transmitted on the channel.

Among them, the forward transmission rate and the feedback transmission rate of the channel are the probability of successful transmission. When the cognitive user transmits a data packet, not every transmission will be successful, and multiple transmissions are required to succeed. Therefore, there is an expected value of transmission times (ETX), which refers to the expected value of the number of times required to successfully deliver a data packet. The expected value of the number of transmissions can be calculated by the forward transmission rate and the feedback transmission rate. The forward transmission rate d _f represents the probability that a data packet successfully arrives at the receiving end, and the feedback transmission rate d _r refers to the successful reception of the ACK (the confirmation flag in the header of the TCP data packet, which confirms the received TCP message) packet probability. Therefore, the probability that a data packet is successfully received and fed back is d _f *d _r , and if the sender does not receive the ACK packet, it will retransmit the data packet.

Each transmission attempt can be regarded as a Bernoulli experiment, and the expected value of the number of transmissions can be calculated by the following formula:

$\mathrm{<span\; class="notranslate">\; ETX</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In formula (4), ETX is the expected value of transmission times, d _f is the forward transmission rate, and d _r is the feedback transmission rate.

In a preferred embodiment, the parameter acquisition module 30 includes a transmission rate processing unit, and the transmission rate processing unit is configured to acquire the forward transmission rate d _f or the feedback transmission rate d _r of the channel. The specific process includes:

(a1) Set the time window and the interval for sending probe packets. Set the time window w, the interval time τ between sending probe packets.

(a2) Calculate the number of sending detection packets according to the time window and the interval time. w/τ is the number of probe packets sent within the set time window.

(a3) Obtain the number of detection packets received within the set time window. Within the set time window w, the number of probe packets received at the receiving end is Count(t-w, t).

(a4) Calculate the ratio of the number of probe packets received to the number of probe packets sent to obtain the forward transmission rate or feedback transmission rate of the channel. The formula for calculating the channel forward transmission rate is shown in formula (5):

${\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; count</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

The feedback transmission rate is obtained in the same way.

The number of channels is set as N, and the bandwidth of channel i is B _i . The total amount of information transmitted on channel i is M _i , That is, the total amount of information transmitted on N channels is the transmission demand M of cognitive users.

The calculation module 40 is used to predict the expected transmission time of the cognitive user in each channel according to the transmission demand of the cognitive user and the basic parameters of each channel, and according to the state of each channel in the predicted next sensing cycle, each Based on the bandwidth of the channel and the expected transmission time of the cognitive user in each channel, the total available transmission capacity of multiple channels in the next sensing period is calculated, and the probability that the channel meets the transmission demand in the next sensing period is calculated.

Calculation module 40 predicts the expected transmission time of cognitive users on each channel according to the transmission demand of cognitive users and the basic parameters of each channel, and the calculation formula of expected transmission time of cognitive users on each channel is as shown in formula (6) Show:

${\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; ETX</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

$<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; \&times;</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}}<span\; class="notranslate">\; \&times;</span>\frac{{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; log</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; SINR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>}$

In formula (6), ETT _i is the expected transmission time of channel i, M _i is the total amount of information transmitted on channel i, B _i is the bandwidth of channel i, γ is the correction parameter of channel i capacity, is the forward transmission rate of channel i, is the feedback transmission rate of channel i, SINR _i is the signal-to-noise ratio of channel i, and B _i log(1+SINR _i ) is the Shannon formula for calculating channel capacity.

The calculation module 40 can calculate the available total transmission capacity of the N channels according to the predicted state of each channel in the next sensing period, the bandwidth of each channel and the expected transmission time of the cognitive user on each channel. The calculation formula is as formula (7):

$\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In formula (7), J is the total transmission capacity of N channels.

The specific process of calculation module 40 calculating the probability that the channel satisfies the transmission requirement includes:

(b1) Comparing the expected transmission time of cognitive users in each channel to obtain the maximum expected transmission time, and calculating the probability that the idle state time of the channel is less than the maximum expected transmission time.

The maximum expected transmission time T _max is:

${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; Max</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

When the idle state time (idle time) of the licensed channel is less than the maximum expected transmission time, channel switching will occur. Therefore, spectrum switching occurs at when is the idle time of channel i.

The idle time of registered users can be estimated by an exponential distribution with parameter λ, so the channel switching probability can be expressed as:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; idl</span>}{\mathrm{<span\; class="notranslate">\; e</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\mathrm{<span\; class="notranslate">\; t</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

In formula (9), _Pi is the switching probability of channel i.

(b2) According to the calculated probability that the idle state time of the channel is less than the maximum expected transmission time, obtain the probability that the channel meets the transmission requirement in the next sensing cycle.

The probability that the channel meets the transmission requirements, that is, the probability that the idle time of the channel is greater than or equal to the maximum expected transmission time, that is, the probability that spectrum switching is not required, is represented by Pa, and the calculation formula is as follows:

Pa=P(T _idel ≥ T _max )=1-P(T _idel <T _max ) (10)

The channel allocation module 50 is used to set constraint conditions according to the probability that the channel satisfies the transmission demand in the next sensing period and the available total transmission capacity of multiple channels, and under the constraint conditions, calculate the total The minimum value of the bandwidth is used to obtain the allocation results of multiple channels in the next sensing period.

Among them, the formula for the probability Pa that the channel meets the transmission demand in the next sensing period is greater than the conditional threshold δ is as in formula (11):

Pa=P(T _idel ≥T _max )=1-P(T _idel <T _max )>δ (11)

The conditional threshold δ is set by the user, and then adjusted according to the results of channel usage.

The total available transmission capacity J of multiple channels in the next sensing period is greater than or equal to the transmission demand M of the cognitive user, and the calculation formula is as follows (12):

$\mathrm{<span\; class="notranslate">\; J</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

Under the two constraints, the minimum value of the total bandwidth of multiple channels is obtained, namely

After a plurality of channels are allocated, within the obtained sensing period, if the cognitive user is allocated a channel, data transmission will be performed; if no channel is allocated, the next sensing period adjacent to the next sensing period will continue to be predicted, Re-predict the channel and perform reallocation.

The spectrum allocation method and system above can be applied to cognitive radio networks, wireless Mesh networks, ad hoc networks, and the like. Cognitive radio can adaptively change the frequency, bandwidth, modulation technology and transmission power of communication according to the communication environment to meet the requirements of communication. The network formed by the interconnection of cognitive radio nodes is the cognitive radio network. A wireless mesh network is a wireless mesh network, which is a wireless multi-hop network. An ad hoc network is a peer-to-peer network.

In addition, indicators to measure the spectrum allocation of a cognitive radio network mainly include spectrum switching rate, spectrum utilization rate and throughput of cognitive users.

Spectrum switching rate p _sh , that is, the probability that a cognitive user switches to another available channel due to the arrival of a registered user. Assume that the probability of registered users appearing obeys the Poisson distribution λ, and its appearance probability is P. In the case of multi-channel allocation, this technical solution is compared with the greedy algorithm. The greedy algorithm means that the cognitive user selects the channel with the largest expected transmission time (ETT) as the transmission channel. It can be seen from the experimental results that as the cognitive user accumulates As the traffic increases, the switching rate of the greedy algorithm allocation method increases significantly. Compared with the single-channel allocation method, the switching rate of the multi-channel frequency spectrum allocation method is reduced by 90%. Therefore, the technical solution reduces the number of spectrum switching times, that is, reduces the number of communication interruptions during the data transmission process, and greatly improves the spectrum utilization rate.

Spectrum utilization of this technical solution. Assume that the total amount of information transmitted on the i-th channel selected by the cognitive user is M _i , and A is the set of channels selected for transmission. Spectrum utilization is defined as:

$\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; A</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; ETT</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$

Compared with the greedy algorithm and the spectrum method of random allocation, the technical scheme has a better performance in spectrum utilization.

The average throughput of the technical solution is increased by at least 100%.

The spectrum allocation method and system above adopts the method of obtaining the state of each channel in the current sensing cycle, predicting the state of each channel in the next sensing cycle, calculating the expected transmission time of the cognitive user on each channel, and then calculating the next cycle The available total transmission capacity of multiple channels and the probability that the channel meets the transmission demand, set constraints, and solve the minimum value of the total bandwidth of multiple channels under the constraints, so as to obtain the allocation of multiple channels in the next cycle As a result, in this way, a plurality of channels are allocated reasonably, and the utilization rate of the frequency spectrum is improved.

In addition, the constraint condition is that the probability that the channel meets the transmission demand in the next sensing period is greater than the conditional threshold, and the total available transmission capacity of multiple channels in the next sensing period is greater than or equal to the transmission demand of the cognitive user, ensuring The total available transmission capacity of the channel is greater than or equal to the transmission demand of the cognitive user, which also ensures the probability that the channel meets the transmission demand. The channel allocation obtained by the solution is more accurate, the switching rate of the spectrum is reduced, and the spectrum utilization rate is improved.

The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (8)

1. a frequency spectrum distributing method, comprises the following steps:

Registration frequency range is divided into multiple channel, obtains the state of each channel in the current perception cycle;

According to the state of each channel in next perception cycle of status predication of channel each in described current period;

Obtain the transmission demand of cognitive user and the basic parameter of each channel, predict that cognitive user is in the expectation transmission time of each channel according to the transmission demand of described cognitive user and the basic parameter of each channel, described basic parameter comprises forward transfer rate and feedback transmission rate, the bandwidth of channel, the informational capacity of channel of channel;

According to the state of each channel, the bandwidth of each channel and cognitive user in next perception cycle of described prediction in the expectation transmission time of each channel, calculate available total transmission quantity of multiple channel in next perception cycle;

Calculate channel in next perception cycle and meet the probability of transmission demand;

Total transmission quantity setting constraints of the probability of transmission demand and the available of multiple channel is met according to channel in next perception cycle described, under described constraints, calculate the minimum value of the total bandwidth of multiple channel in next perception cycle, draw the allocation result of multiple channel in next perception cycle;

In next perception cycle of the described status predication according to channel each in current period, the concrete steps of the state of each channel comprise:

Set up the Markov chain model of channel status, described channel status comprises idle condition and seizure condition, and the idle condition of described channel status and seizure condition adopt exponential distribution function to represent;

State threshold is set, the usable probability of each channel in next perception cycle is calculated according to channel status in the described current perception cycle, the usable probability of each channel is compared with described state threshold, if the usable probability of described channel is more than or equal to described state threshold, in next perception cycle described, channel status is idle condition, otherwise in next perception cycle described, channel status is seizure condition;

The probability that the channel in next perception cycle of described calculating meets transmission demand specifically comprises:

Comparative cognition user draws the greatest hope transmission time in the expectation transmission time of each channel, and the time calculating the idle condition of channel is less than the probability in greatest hope transmission time;

Be less than the probability in greatest hope transmission time according to the time of the idle condition of the described channel calculated, show that in next perception cycle, this channel meets the probability of transmission demand.

2. frequency spectrum distributing method according to claim 1, it is characterized in that, described constraints is the probability greater than condition threshold value that in next perception cycle, channel meets transmission demand, and in next perception cycle, available total transmission quantity of multiple channel is more than or equal to the transmission demand of described cognitive user.

3. frequency spectrum distributing method according to claim 1, is characterized in that, the forward transfer rate of described acquisition channel or the concrete steps of feedback transmission rate are:

The interval time of setting-up time window and transmission detection packet;

The quantity sending detection packet is calculated according to time window and interval time;

Obtain the quantity of the detection packet received in described setting-up time window;

Calculate the quantity of detection packet and the ratio of the quantity of the detection packet of transmission that receive, draw forward transfer rate or the feedback transmission rate of channel.

4. frequency spectrum distributing method according to claim 1, is characterized in that, the computing formula in the expectation transmission time of described channel is:

${\mathrm{ETT}}_i=\mathrm{\&gamma;}\&times;\frac{1}{d_{f_i}\&times;d_{r_i}}\&times;\frac{M_i}{B_i\&times;\log (1+{\mathrm{SINR}}_i)},$

Wherein, ETT _ifor the expectation transmission time of channel i, M _ithe informational capacity of transmission on channel i, B _ibe the bandwidth of channel i, γ is the corrected parameter of channel i capacity, for the forward transfer rate of channel i, for the feedback transmission rate of channel i, SINR _ifor the signal to noise ratio of channel i.

5. a spectrum allocation system, is characterized in that, comprising:

Scan module, for registration frequency range is divided into multiple channel, obtains the state of each channel in the current perception cycle;

Prediction module, for the state according to each channel in next perception cycle of status predication of channel each in described current period;

Parameter acquisition module, for the basic parameter of the transmission demand and each channel that obtain cognitive user, described basic parameter comprises forward transfer rate and feedback transmission rate, the bandwidth of channel, the informational capacity of channel of channel;

Computing module, for according to the transmission demand of described cognitive user and the Parameter Calculation cognitive user of each channel expectation transmission time at each channel, according to the state of each channel, the bandwidth of each channel and cognitive user in next perception cycle of described prediction in the expectation transmission time of each channel, calculate available total transmission quantity of multiple channel in next perception cycle, and calculate channel in next perception cycle and meet the probability of transmission demand;

Channel assignment module, for meeting total transmission quantity setting constraints of the probability of transmission demand and the available of multiple channel according to channel in next perception cycle described, under described constraints, calculate the minimum value of the total bandwidth of multiple channel in next perception cycle, draw the allocation result of multiple channel in next perception cycle;

Described prediction module comprises modeling unit, setting unit, computing unit and judging unit, described modeling unit is for setting up the Markov chain model of channel status, described channel status comprises idle condition and seizure condition, and the idle condition of described channel status and seizure condition adopt exponential distribution function to represent; Described setting unit arranges state threshold; Described computing unit calculates the usable probability of each channel in next perception cycle according to channel status in the described current perception cycle; Described judging unit is used for the usable probability of more each channel and described state threshold, if the usable probability of described channel is more than or equal to described state threshold, in next perception cycle described, channel status is idle condition, otherwise in next perception cycle described, channel status is seizure condition;

Described computing module also draws the greatest hope transmission time for comparative cognition user in the expectation transmission time of each channel, the time calculating the idle condition of channel is less than the probability in greatest hope transmission time, and the probability in greatest hope transmission time is less than according to the time of the idle condition of the described channel calculated, show that in next perception cycle, this channel meets the probability of transmission demand.

6. spectrum allocation system according to claim 5, it is characterized in that, described constraints is the probability greater than condition threshold value that the channel in next perception cycle meets transmission demand, and available total transmission quantity of multiple channels in next perception cycle is more than or equal to the transmission demand of described cognitive user.

7. spectrum allocation system according to claim 5, it is characterized in that, described parameter acquisition module comprises transfer rate processing unit, the interval time of described transfer rate processing unit setting-up time window and transmission detection packet, the quantity sending detection packet is calculated according to time window and interval time, obtain the quantity of the detection packet received in described setting-up time window, calculate the quantity of the detection packet received and the ratio of the quantity of the detection packet of transmission, draw forward transfer rate or the feedback transmission rate of channel.

8. spectrum allocation system according to claim 5, is characterized in that, the formula that described computing module calculates the expectation transmission time of described channel is:

${\mathrm{ETT}}_i=\mathrm{\&gamma;}\&times;\frac{1}{d_{f_i}\&times;d_{r_i}}\&times;\frac{M_i}{B_i\&times;\log (1+{\mathrm{SINR}}_i)},$

Wherein, ETT _ifor the expectation transmission time of channel i, M _ithe informational capacity of transmission on channel i, B _ibe the bandwidth of channel i, γ is the corrected parameter of channel i capacity, for the forward transfer rate of channel i, for the feedback transmission rate of channel i, SINR _ifor the signal to noise ratio of channel i.