CN103052145B - Method for multi-mode mobile terminal to select sector with high service quality - Google Patents

Abstract

A method for a multi-mode mobile terminal to select a high-quality-of-service sector, characterized in that it includes: step S100, the multi-mode terminal traverses all sectors at the location, and counts the number N of sectors; step S200, calls the particle swarm algorithm to select At least one sector is used as the sector selection scheme; step S300, the multi-mode terminal accesses the sector according to the sector selection scheme of the particle swarm optimization algorithm. A method for selecting a high-quality sector for a mobile multi-mode terminal provided by the present invention runs on the side of the multi-mode mobile terminal, aims at improving the service quality of the multi-mode mobile terminal, and selects a suitable sector for each multi-mode mobile terminal The combination has the advantages of convenient operation, clear steps, strong stability, and low algorithm complexity.

Description

A method for a multi-mode mobile terminal to select a high-quality-of-service sector

technical field

The invention relates to the technical field of sectorized cellular wireless network communication, and relates to a method for a multi-mode mobile terminal to select a high-quality-of-service sector.

Background technique

At present, cellular mobile communication technology, as a mature mobile communication technology, has been deployed in more than 200 countries around the world, providing communication services for more than 3 billion users. With the increase in the number of operators and the scarcity of spectrum resources, the competition between operators is becoming more and more intense, and the service quality of cellular networks has become a decisive factor for users to choose operators and network models.

Traditional cellular mobile communication refers to omnidirectional cellular, that is, the base station transmits wireless signals to a full range of 360 degrees. Due to the very limited wireless spectrum resources, in order to continuously increase the capacity of users in the area, operators must continuously reduce the cell size to improve system performance. However, the continuous reduction of cell size has greatly increased the cost of deployment and the frequency interference of adjacent cells. Traditional cellular spectrum reuse cannot fully meet the needs of mobile communications.

Based on the above reasons, in order to further increase the system capacity without increasing the total number of cells, sectorization technology is usually used to divide a mobile communication cell into several sectors, and each sector is equipped with an independent transceiver antenna. That is, each sector is equivalent to an independent cell. Currently, there are two commonly used sector division methods: 120° and 60°.

The sectorized cellular network can effectively enhance signal energy and improve network service quality for users. Because the energy of the radio frequency is concentrated within a certain angle, compared with the previous 360° range, the user's signal strength is greater and the service quality is higher. At the same time, since the signals of a certain frequency are only concentrated in a certain angle, the signals of the same frequency can be reused in a relatively close area, which avoids interference of the same frequency while enhancing the efficiency of spectrum utilization.

With the continuous advancement of technology, multi-mode terminals can already access two or more sector signals at the same time. For example, some multi-mode terminals can use TD-SCDMA sector signals to download 3G networks while using GSM sector signal for voice communication. Simultaneous access to multiple sectors not only enhances the flexibility of multi-mode terminal access, but also improves the service quality of users, which can effectively improve user satisfaction.

However, since a multi-mode terminal can select multiple sectors for access at the same time, sector selection has become a kind of extremely difficult "non-deterministic problem with polynomial complexity", referred to as NP-hard problem. At present, there is no effective solution to find the optimal solution for this kind of problem, and it is difficult to find the optimal solution in polynomial time. For example, if a multi-mode terminal has 10 sectors with signal strength for selective access, there are 2 ¹⁰ =1024 sector selection schemes in total. Multi-mode terminals have limited computing power, and cannot verify the feasibility and service quality of all 1024 schemes within a limited time.

At the same time, when the number of mobile multi-mode terminals and sectors in the cell is large, and the mobile terminal has sectors in multiple network modes to choose from, the selection and allocation of sectors by the mobile multi-mode terminal becomes very complicated. Since the shape of each sector is no longer the traditional hexagonal cell, and the frequency allocation of the sector is different from that of the traditional cell, the traditional hexagonal cell selection strategy is no longer applicable to the 120° and 60° sector selection.

After searching the current IEEE technical literature, it is found that evolutionary algorithms are often used for multi-mode terminal sector selection at this stage. The representative literature can be found in: "Dynamic sectorization of microcells for balanced traffic in CDMA: genetic algorithms approach" (published in IEEE Transactions on Vehicular Technology, Volume: 51, Issue:1), this paper mainly describes a sector selection scheme based on genetic algorithm. In this scheme, sectors are selected and finalized through iterations of a genetic algorithm. However, the genetic algorithm is prone to premature convergence and evolutionary stagnation, the convergence speed is slow, and the service quality value of the obtained allocation scheme is not high. At the same time, when there are many sectors and active terminals, the calculation speed of the genetic algorithm is slow, which cannot meet the real-time requirements of the system.

Particle swarm optimization algorithm is a new evolutionary algorithm developed in recent years. The particle swarm optimization algorithm starts from a random solution, finds the optimal solution through iteration, and evaluates the quality of the solution through fitness. It is simpler than the rules of the genetic algorithm, it does not have the "crossover" and "mutation" operations of the genetic algorithm, and it searches for the global optimum by following the optimal value currently searched. The advantages of particle swarm optimization algorithm, such as easy implementation, high precision, and fast convergence, have attracted the attention of the academic community, and have demonstrated its superiority in many practical problems. Because the sector selection problem of multi-mode terminals belongs to the non-deterministic problem of polynomial complexity that particle swarm optimization algorithm is good at solving, how to apply particle swarm optimization algorithm to sector allocation has become one of the key issues concerned by relevant researchers .

Contents of the invention

The invention provides a method for a multi-mode mobile terminal to select a high-quality-of-service sector, which is used to solve the problem that the sector allocation algorithm in the prior art has a slow convergence speed and the service quality value of the obtained sector selection scheme is not high.

A method for a multi-mode mobile terminal to select a high-quality-of-service sector provided by the present invention includes:

Step S100, the multi-mode mobile terminal traverses all the sectors at the location, and counts the number N of sectors, where N is a natural number;

Step S200, calling the particle swarm optimization algorithm to select at least one sector as a sector selection scheme;

Step S300, the multi-mode mobile terminal accesses the sector according to the sector selection scheme selected by the particle swarm optimization algorithm;

Wherein, step S200 includes the following steps:

Step S210, according to the number of sectors N, the particle position x of the particle swarm optimization algorithm is binary-coded into a binary sequence with a length of N;

Step S220, the parameters of the particle swarm optimization algorithm are initialized, the number of particles is set to be D, the maximum number of iterations is M, M and D are natural numbers, and each particle is given an initial random position x ₀ and velocity v ₀ ;

Step S230, calculating the quality of service value of the initial position _x0 of the particle;

Step S240, update the velocity of each particle according to the calculation formula, that is, the velocity of the particle at the k+1th iteration The calculation formula is:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>$

In the formula, k is the number of iterations, k is a natural number, Indicates the historical optimal position of the current particle, Indicates the global historical optimal position, is the position of the current particle, Indicates the position of the optimal particle in the current particle swarm, rand(1) indicates a random number between 0 and 1 randomly generated by the computer, Indicates the velocity of the particle at the kth iteration, Indicates the velocity of the particle at the k+1th iteration;

Step S250, according to the velocity of the particle at the k+1th iteration described in step S240 Update the position of each particle according to the calculation formula, that is, the position of the particle at the k+1th iteration The calculation formula is:

in, is the position of the particle at the k+1th iteration, Indicates the velocity of the particle at the k+1th iteration;

Step S260, according to the position of the particle at the k+1th iteration Calculate the position of each particle in the particle swarm quality of service value;

Step S270, judge whether the number of iterations k is greater than the maximum number of iterations M, if it reaches the maximum number of iterations M, then output As a sector selection scheme; otherwise, return to step S240.

Further, in the method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to the present invention, in the step S220, set the number of particles D=50, and the maximum number of iterations M=100.

Further, in the method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to the present invention, the step S210 is to set at least 10 sectors available for selection, and the number of sectors N is greater than or equal to 10, where N is a natural number.

Further, in the method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to the present invention, in step S200, the particle swarm optimization algorithm is run on the multi-mode mobile terminal.

Further, in the method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to the present invention, the step S200 is to use a particle swarm optimization algorithm to select a sector in a sectorized cell.

A method for a multi-mode mobile terminal to select a high-quality-of-service sector provided by the present invention has the advantages of:

In the process of selecting a sector selection scheme for a multi-mode mobile terminal, the selected particle swarm optimization algorithm has a clear process, is simple to implement, requires fewer parameters, and has a faster algorithm convergence speed, which effectively improves the service quality of the multi-mode mobile terminal in the sector At the same time, compared with the prior art method of using genetic algorithm for multi-mode mobile terminal sector selection, the present invention can obtain a better solution in a shorter time, and will not fall into a calculation stagnation state.

Simulation results through software simulation experiments show that the method for selecting a high-quality-of-service sector for a multi-mode mobile terminal proposed by the present invention improves the service quality value obtained by the genetic algorithm by about 10% compared with the prior art.

To sum up, the method of the present invention can improve the service quality value of the multi-mode mobile terminal, and has a very good promotion prospect. This method is completely run on the side of the multi-mode mobile terminal, with the goal of improving the service quality of the multi-mode mobile terminal, and selecting a suitable sector combination for each multi-mode mobile terminal. It has the advantages of convenient operation, clear steps, strong stability, and complex algorithms. advantages such as low degree.

Description of drawings

Fig. 1 is the overall flow diagram of the method of embodiment 2 of the present invention;

FIG. 2 is a schematic diagram of a detailed flow chart of step S200 of the method of Embodiment 2 of the present invention;

Fig. 3 is a comparison curve diagram of the terminal service quality value of the method of embodiment 1 of the present invention relative to the genetic algorithm in the prior art;

Fig. 4 is a graph comparing the terminal QoS value of the method according to Embodiment 2 of the present invention with that of the genetic algorithm in the prior art.

Detailed ways

In order to better understand the present invention, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

The embodiment of the present invention provides a method for a multi-mode mobile terminal to select a high-quality-of-service sector. Based on the particle swarm optimization algorithm, the multi-mode mobile terminal selects a sector in a sectorized cell. The particle swarm optimization algorithm runs on a multimode mobile terminal.

Example 1:

Fig. 1 is the overall flow diagram of the method of embodiment 1 of the present invention, as shown in Fig. 1,

Embodiment 1 of the present invention provides a method for a multi-mode mobile terminal to select a high-quality-of-service sector, including:

Step S100, the multi-mode mobile terminal traverses all sectors in the location, and counts the number N of sectors;

Step S200, calling the particle swarm optimization algorithm to select at least one sector as a sector selection scheme;

Step S300, the multi-mode mobile terminal accesses the sector according to the sector selection scheme selected by the particle swarm optimization algorithm;

FIG. 2 is a schematic diagram of a detailed flow chart of step S200 of the method of Embodiment 2 of the present invention, as shown in FIG. 2, wherein step S200 includes the following steps:

Step S210, according to the number of sectors N, the particle position x of the particle swarm optimization algorithm is binary-coded into a binary sequence with a length of N;

For example, if there are 10 sectors available for selection, 0000000001 means that the multi-mode terminal accesses the first sector, and 0000000011 means that the multi-mode terminal accesses the first and second sectors simultaneously. By analogy, 1111111111 represents that the multimode terminal accesses ten sectors at the same time.

Step S220, initialize the parameters of the particle swarm optimization algorithm, set the number of particles as D, the maximum number of iterations as M, and give each particle an initial random position _x0 and velocity _v0 ;

Set the number of particles D=50, the maximum number of iterations M=100;

Step S230, calculating the quality of service value of the initial position _x0 of the particle;

Specifically include:

Step S231, setting the network jitter upper limit;

Step S232, obtain the selected sector according to the initial position x ₀ of the particle;

Step S233, obtain the network jitter value of the selected sector, add the network jitter values of all selected sectors, and compare with the network jitter upper limit value, if the added network jitter value is greater than the network jitter upper limit value, then set The quality of service value at the initial position _x0 is recorded as 0; if the added network jitter value is less than or equal to the network jitter upper limit value, then enter step S234;

Step S234, after summing the download rates of all the selected sectors and normalizing with the upper limit of the download rate of the multi-mode mobile terminal, the obtained value is the service quality value of the initial position _x0 .

In this embodiment 1, parameters are selected according to the current specific service situation of the multi-mode mobile terminal, and the service quality value corresponding to the particle position x ₀ is calculated by using the sum normalization method. In Embodiment 1, the multi-mode mobile terminal is conducting a video call service. The higher the download rate, the clearer the video and the higher the call quality. The download rate is selected as a parameter to calculate the service quality value. The quality of service value is obtained by summing the download rates of all sectors and normalizing the upper limit of the multi-mode terminal download rate, but it needs to be limited by the upper limit of the acceptable network jitter, which exceeds the acceptable network jitter After the upper limit, the video call will be very slow, the business cannot be carried out normally, and the service quality value is 0.

For example, it is assumed that the upper limit of the download rate of the multi-mode terminal is 10 Mbps (megabits per second), and the upper limit of acceptable network jitter is 10 ms (milliseconds).

The bandwidth and cost of sector 1 are 1.5Mbps and 5ms respectively.

The bandwidth and cost of sector 2 are 1Mbps and 9ms respectively.

The bandwidth and cost of sector 3 are 1.5Mbps and 3ms respectively.

When the particle position x ₀ is 0000000011, the multimode terminal accesses the first sector and the second sector at the same time.

Then the network jitter of simultaneously accessing sector 1 and sector 2 is 5ms+9ms=14ms, which exceeds the acceptable upper limit of network jitter by 10ms, that is, the service quality value of particle position 0000000011 is 0.

When the particle position x ₀ is 0000000101, the multi-mode terminal accesses the first and third sectors at the same time, then the network jitter of simultaneously accessing sector 1 and sector 3 is 5ms+3ms=8ms, which is not beyond the limit The upper limit of accepted network jitter is 10ms, so the QoS value corresponding to particle position 0000000101 is not 0.

The sum of the bandwidth of accessing sector 1 and sector 3 at the same time is 1.5Mbps+1.5Mbps, and after normalization with the upper limit of the multi-mode terminal download rate of 10Mbps, it is:

(1.5Mbps+1.5Mbps)÷10Mbps=0.3. That is, the QoS value corresponding to the particle position 0000000101 is 0.3.

Step S240, update the velocity of each particle according to the calculation formula, that is, the velocity of the particle at the k+1th iteration The calculation formula is:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>$

In the formula, k is the number of iterations, Indicates the historical optimal position of the current particle, Indicates the global historical optimal position, is the current particle position, Indicates the position of the optimal particle in the current particle swarm, rand(1) indicates a random number between 0 and 1 randomly generated by the computer, Indicates the velocity of the particle at the kth iteration, Indicates the velocity of the particle at the k+1th iteration;

Step S250, according to the velocity of the particle at the k+1th iteration described in step S240 Update the position of each particle according to the calculation formula, that is, the position of the particle at the k+1th iteration The calculation formula is:

in, is the position of the particle at the k+1th iteration, Indicates the velocity of the particle at the k+1th iteration;

Step S260, according to the position of the particle at the k+1th iteration Calculate the position of each particle in the particle swarm quality of service value;

Specifically include:

Step S261, setting the upper limit of network jitter;

Step S262, according to the position of the particle at the k+1th iteration Get the selected sector;

Step S263, obtain the network jitter value of the selected sector, add the network jitter values of all selected sectors, and compare with the network jitter upper limit value, if the added network jitter value is greater than the network jitter upper limit value, then set The position of the particle at iteration k+1 The quality of service value is recorded as 0; if the added network jitter value is less than or equal to the network jitter upper limit value, then enter step S264;

Step S264, after summing the download rates of all selected sectors and normalizing the quotient with the upper limit of the download rate of the multi-mode mobile terminal, the obtained value is the position of the particle at the k+1 iteration quality of service value.

In this embodiment 1, the parameters are selected according to the current specific business conditions of the multi-mode mobile terminal, and the particle position is calculated by using the sum normalization method Corresponding quality of service value. In Embodiment 1, the multi-mode mobile terminal is conducting a video call service. The higher the download rate, the clearer the video and the higher the call quality. The download rate is selected as a parameter to calculate the service quality value. The quality of service value is obtained by summing the download rates of all sectors and normalizing the upper limit of the multi-mode terminal download rate, but it needs to be limited by the upper limit of the acceptable network jitter, which exceeds the acceptable network jitter After the upper limit, the video call will be very slow, the business cannot be carried out normally, and the service quality value is 0.

For example, it is assumed that the upper limit of the download rate of the multi-mode terminal is 10 Mbps (megabits per second), and the upper limit of acceptable network jitter is 10 ms (milliseconds).

The bandwidth and cost of sector 1 are 1.5Mbps and 5ms respectively.

The bandwidth and cost of sector 2 are 1Mbps and 9ms respectively.

The bandwidth and cost of sector 3 are 1.5Mbps and 3ms respectively.

When the particle position When it is 0000000011, the multimode terminal accesses the first sector and the second sector at the same time.

Then the network jitter of simultaneously accessing sector 1 and sector 2 is 5ms+9ms=14ms, which exceeds the acceptable upper limit of network jitter by 10ms, that is, the service quality value of particle position 0000000011 is 0.

When the particle position When it is 0000000101, the multi-mode terminal accesses the first and third sectors at the same time, then the network jitter of simultaneously accessing sector 1 and sector 3 is 5ms+3ms=8ms, which does not exceed the acceptable upper limit of network jitter 10ms, so the quality of service value corresponding to the particle position 0000000101 is not 0.

The sum of the bandwidth of accessing sector 1 and sector 3 at the same time is 1.5Mbps+1.5Mbps, and after normalization with the upper limit of the multi-mode terminal download rate of 10Mbps, it is:

(1.5Mbps+1.5Mbps)÷10Mbps=0.3. That is, the QoS value corresponding to the particle position 0000000101 is 0.3.

Step S270, judge whether the number of iterations k is greater than the maximum number of iterations M, if it reaches the maximum number of iterations M, then output As a sector selection scheme; otherwise, return to step S240.

The parameter configuration and simulation results of the computer simulation experiment of Embodiment 1 will be given in detail below.

There are 10 sectors altogether in the embodiment of the present invention. A multi-mode mobile terminal can arbitrarily select one or several sectors from the 10 sectors where it is located. The upper limit of the download rate of the multi-mode terminal is 10Mbps (megabits per second), and the upper limit of the acceptable network jitter is 10ms (milliseconds). The bandwidth of each sector is randomly generated between 0 and 2Mbps, and the network jitter is randomly generated between 0 and 10ms. The number of particles in the particle swarm optimization algorithm is 50 for D, the maximum number of iterations for M is 100, the minimum velocity of particles is 0, and the maximum velocity is 6. As a comparison, the number of individuals in the genetic algorithm population is 50, the maximum number of iterations is 100, the crossover probability is 0.7, and the mutation probability is 0.05.

Fig. 3 is a graph comparing the terminal QoS value of the method in Embodiment 1 of the present invention with that of the genetic algorithm in the prior art. As shown in Figure 3, the upper solid line in Figure 3 is the QoS value curve simulated by the method for multi-mode mobile terminals to select high-QoS sectors proposed in Embodiment 1 of the present invention, and the lower dotted line is the QoS value curve in the prior art The service quality value curve obtained by genetic algorithm. As can be seen from the simulation curve, in 100 iterations, the quality of service value of the multimode mobile terminal obtained by the method described in Embodiment 1 of the present invention is 0.04 higher than the quality of service value of the multimode mobile terminal obtained by the genetic algorithm 0.05, that is, the performance has been improved by 8% to 10%, indicating that the sector and network mode allocation results obtained by the method of the present invention can significantly improve the communication service quality of the terminal. Also can find out from figure simultaneously, the performance of the inventive method is much more stable than genetic algorithm, just gradually flattens convergence after 50 iterations, and genetic algorithm does not reach horizontal convergence in the whole 100 iterations process, is unfavorable for actual system use.

Example 2:

Embodiment 2 of the present invention provides a method for a multi-mode mobile terminal to select a high-quality-of-service sector, including:

Step S100, the multi-mode mobile terminal traverses all sectors in the location, and counts the number N of sectors;

Step S200, calling the particle swarm optimization algorithm to select at least one sector as a sector selection scheme;

Step S300, the multi-mode mobile terminal accesses the sector according to the sector selection scheme selected by the particle swarm optimization algorithm;

Step S200 includes the following steps:

Step S210, according to the number of sectors N, the particle position x of the particle swarm optimization algorithm is binary-coded into a binary sequence with a length of N;

In Embodiment 2, there are 10 sectors available for selection, 0000000001 means that the multi-mode terminal accesses the first sector, and 0000000011 means that the multi-mode terminal simultaneously accesses the first and second sectors. By analogy, 1111111111 represents that the multimode terminal accesses ten sectors at the same time.

Step S220, initialize the parameters of the particle swarm optimization algorithm, set the number of particles as D, the maximum number of iterations as M, and give each particle an initial random position _x0 and velocity _v0 ;

Initialize the parameters of the particle swarm algorithm, set the number of particles D=10, the maximum number of iterations M=100, give each particle an initial random position x ₀ and velocity v ₀ , x ₀ is generated by the computer as a random 0 or 1 binary Sequence, length 10. v ₀ is a random rational number between 0 and 6, generated by computer.

Step S230, calculating the quality of service value of the initial position _x0 of the particle;

Specifically include:

Step S231, setting the network fee upper limit;

Step S232, obtain the selected sector according to the initial position x ₀ of the particle;

Step S233, obtain the network fee value of the selected sector, add the network fee values of all the selected sectors, and compare with the network fee upper limit value, if the added network fee value is greater than the network fee upper limit value, then set The quality of service value at the initial position _x0 is recorded as 0; if the added network cost value is less than or equal to the network cost upper limit value, then enter step S234;

Step S234, after summing the download rates of all the selected sectors and normalizing with the upper limit of the download rate of the multi-mode mobile terminal, the obtained value is the service quality value of the initial position _x0 .

In Embodiment 2, parameters are selected according to the current specific service situation of the multi-mode mobile terminal, and the service quality value corresponding to the particle position x ₀ is calculated by using the sum normalization method. In Embodiment 2, the multi-mode terminal is performing a file download service, and the download rate is selected as a parameter to calculate the service quality value. The quality of service value is obtained by summing the download rates of all sectors and normalizing with the upper limit of the multi-mode terminal download rate, but it needs to be limited by the upper limit of the acceptable cost. After exceeding the upper limit of the acceptable cost The quality of service value is 0.

For example, assuming that the upper limit of the download rate of the multi-mode mobile terminal is 10 Mbps, the upper limit of the acceptable fee is 0.06 yuan/minute.

The bandwidth and cost of sector 1 are 2Mbps and 0.04 yuan/minute respectively.

The bandwidth and cost of sector 2 are 1Mbps and 0.01 yuan/minute respectively.

The bandwidth and cost of sector 3 are 1.5Mbps and 0.05 yuan/minute respectively.

When the particle position x ₀ is 0000000011, the multimode mobile terminal accesses the first and second sectors simultaneously.

Then the cost of simultaneously accessing sector 1 and sector 2 is 0.04 yuan/minute + 0.01 yuan/minute = 0.05 yuan/minute, and the upper limit of the acceptable fee is 0.06 yuan/minute, and there is no need to set the service quality value to 0 .

The bandwidth of accessing sector 1 and sector 2 at the same time is 2Mbps+1Mbps, and after normalizing with the upper limit of the download rate of multi-mode terminals, it is: (2Mbps+1Mbps)÷10Mbps=0.3. That is, the QoS value corresponding to the particle position 0000000011 is 0.3.

When the particle position x ₀ is 0000000101, and the multi-mode terminal accesses the first and third sectors at the same time, the cost of simultaneously accessing sector 1 and sector 3 is 0.04 yuan/minute+0.05 yuan/minute=0.09 Yuan/minute, which exceeds the acceptable upper limit of 0.06 yuan/minute, so the quality of service value corresponding to the particle position 0000000101 is 0.

Step S240, update the velocity of each particle according to the calculation formula, that is, the velocity of the particle at the k+1th iteration The calculation formula is:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>$

In the formula, k is the number of iterations, Indicates the historical optimal position of the current particle, Indicates the global historical optimal position, is the current particle position, Indicates the position of the optimal particle in the current particle swarm, rand(1) indicates a random number between 0 and 1 randomly generated by the computer, Indicates the velocity of the particle at the kth iteration, Indicates the velocity of the particle at the k+1th iteration;

At the first iteration, the velocity of the particle at the kth iteration is the initial random velocity v ₀ , the current particle position and the position of the optimal particle in the former particle swarm is the initial random position x ₀ , the historical optimal position of the current particle is the initial random position x ₀ . That is, at the first iteration, Thereafter, calculation is performed according to the calculation formula in step S240.

The subtraction here is a binary subtraction, specifically according to the following rules: if the corresponding bits are the same, it is 0, and if the corresponding positions are different, it is 1.

For example, 0000000111-0000000101=0000000010.

Step S250, according to the velocity of the particle at the k+1th iteration described in step S240 Update the position of each particle according to the calculation formula, that is, the position of the particle at the k+1th iteration The calculation formula is:

in, is the position of the particle at the k+1th iteration, Indicates the velocity of the particle at the k+1th iteration;

Step S260, according to the position of the particle at the k+1th iteration Calculate the position of each particle in the particle swarm quality of service value;

Specifically include:

Step S261, setting the network fee upper limit;

Step S262, according to the position of the particle at the k+1th iteration Get the selected sector;

Step S263, obtain the network cost value of the selected sector, add the network cost values of all the selected sectors, and compare with the network cost upper limit value, if the added network cost value is greater than the network cost upper limit value, then set The position of the particle at iteration k+1 The quality of service value is recorded as 0; if the added network cost value is less than or equal to the network cost upper limit value, then enter step S264;

Step S264, after summing the download rates of all selected sectors and normalizing the quotient with the upper limit of the download rate of the multi-mode mobile terminal, the obtained value is the position of the particle at the k+1 iteration quality of service value.

In this embodiment 2, the parameters are selected according to the current specific business conditions of the multi-mode terminal, and the particle position is calculated by using the sum normalization method Corresponding quality of service value. In Embodiment 2, the multi-mode terminal is performing a file download service, and the download rate is selected as a parameter to calculate the service quality value. The quality of service value is obtained by summing the download rates of all sectors and normalizing with the upper limit of the multi-mode terminal download rate, but it needs to be limited by the upper limit of the acceptable cost. After exceeding the upper limit of the acceptable cost The quality of service value is 0.

For example, if the upper limit of the download rate of the multi-mode terminal is 10 Mbps, the upper limit of the acceptable fee is 0.06 yuan/minute.

The bandwidth and cost of sector 1 are 2Mbps and 0.04 yuan/minute respectively.

The bandwidth and cost of sector 2 are 1Mbps and 0.01 yuan/minute respectively.

The bandwidth and cost of sector 3 are 1.5Mbps and 0.05 yuan/minute respectively.

When the particle position When it is 0000000011, the multimode terminal accesses the first sector and the second sector at the same time.

Then the cost of simultaneously accessing sector 1 and sector 2 is 0.04 yuan/minute + 0.01 yuan/minute = 0.05 yuan/minute, and the upper limit of the acceptable fee is 0.06 yuan/minute, and there is no need to set the service quality value to 0 .

The bandwidth of accessing sector 1 and sector 2 at the same time is 2Mbps+1Mbps, and after normalizing with the upper limit of the download rate of multi-mode terminals, it is: (2Mbps+1Mbps)÷10Mbps=0.3. That is, the QoS value corresponding to the particle position 0000000011 is 0.3.

When the particle position When it is 0000000101, the multi-mode terminal accesses the first and third sectors at the same time, then the fee for simultaneously accessing sector 1 and sector 3 is 0.04 yuan/minute + 0.05 yuan/minute = 0.09 yuan/minute, exceeding The upper limit of the acceptable fee is 0.06 yuan/minute, so the service quality value corresponding to the particle position 0000000101 is 0.

Step S270, judge whether the number of iterations k is greater than the maximum number of iterations M, if it reaches the maximum number of iterations M, then output As a sector selection scheme; otherwise, return to step S240.

The parameter configuration and simulation results of the computer simulation experiment of Embodiment 2 will be given in detail below.

In Embodiment 2 of the present invention, there are 10 sectors in total. A multi-mode terminal can choose one or several sectors from the 10 sectors at its location. The upper limit of the download rate of the multi-mode terminal is 10Mbps, and the upper limit of the acceptable fee is 0.06 yuan/minute. The bandwidth of each sector is randomly generated between 0 and 2Mbps, and the cost of each sector is randomly generated between 0 and 0.06 yuan/minute. The number of particles in the particle swarm optimization algorithm is 50 for D, the maximum number of iterations for M is 100, the minimum velocity of particles is 0, and the maximum velocity is 6. As a comparison, the number of individuals in the genetic algorithm population is 50, the maximum number of iterations is 100, the crossover probability is 0.7, and the mutation probability is 0.05.

Fig. 4 is a graph comparing the terminal QoS value of the method according to Embodiment 2 of the present invention with that of the genetic algorithm in the prior art. As shown in Figure 4, the upper solid line in Figure 4 is the QoS value curve simulated by the method for multi-mode mobile terminals to select high-QoS sectors proposed in Embodiment 2 of the present invention, and the lower dotted line is the QoS value curve in the prior art The service quality value curve obtained by genetic algorithm. It can be seen from the simulation curve that after 100 iterations, the quality of service value of the multimode mobile terminal obtained by the method described in Embodiment 2 of the present invention is 0.05 higher than the quality of service value of the multimode mobile terminal obtained by the genetic algorithm , that is, the performance is improved by 10%, which shows that the sector and network mode allocation results obtained by the method of the present invention can significantly improve the communication service quality of the terminal. It can also be seen from the figure that the performance of the method of the present invention is much more stable than that of the genetic algorithm, and it gradually flattens and converges after 50 iterations, while the genetic algorithm has been in a significant fluctuation process during the entire 100 iterations, and obtains The quality of service value of is highly random, which is not conducive to the actual system adoption.

The above are only preferred embodiments of the present invention. Of course, the present invention can also have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various There are various corresponding changes and modifications, but these corresponding changes and modifications should fall within the protection scope of the appended claims of the present invention.

Claims (5)

1. A method for a multimode mobile terminal to select a high-quality-of-service sector, comprising: Step S100, the multi-mode mobile terminal traverses all the sectors at the location, and counts the number N of sectors, where N is a natural number; Step S200, calling the particle swarm optimization algorithm to select at least one sector as a sector selection scheme; Step S300, the multi-mode mobile terminal accesses the sector according to the sector selection scheme selected by the particle swarm optimization algorithm; Wherein, step S200 includes the following steps: Step S210, according to the number of sectors N, the particle position x of the particle swarm optimization algorithm is binary-coded into a binary sequence with a length of N; Step S220, the parameters of the particle swarm optimization algorithm are initialized, the number of particles is set to be D, the maximum number of iterations is M, M and D are natural numbers, and each particle is given an initial random position x ₀ and velocity v ₀ ; Step S230, calculating the quality of service value of the initial position _x0 of the particle; Step S240, update the velocity of each particle according to the calculation formula, that is, the velocity of the particle at the k+1th iteration The calculation formula is: ${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; id</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; rand</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; gd</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>$ In the formula, k is the number of iterations, k is a natural number, Indicates the historical optimal position of the service quality value of the current particle, Indicates the historical optimal position of the global service quality value, is the current particle position, Indicates the position of the particle with the best service quality value in the current particle swarm, rand(1) indicates a random number between 0 and 1 randomly generated by the computer, Indicates the velocity of the particle at the kth iteration, Indicates the velocity of the particle at the k+1th iteration; Step S250, according to the velocity of the particle at the k+1th iteration described in step S240 Update the position of each particle according to the calculation formula, that is, the position of the particle at the k+1th iteration The calculation formula is: in, is the position of the particle at the k+1th iteration, Step S260, according to the position of the particle at the k+1th iteration Calculate the position of each particle in the particle swarm quality of service value; Step S270, judge whether the number of iterations k is greater than the maximum number of iterations M, if it reaches the maximum number of iterations M, then output As a sector selection scheme; otherwise, return to step S240. 2. The method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to claim 1, characterized in that in step S220, set the number of particles D=50, and the maximum number of iterations M=100. 3. The method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to claim 2, characterized in that, in the step S210, at least 10 sectors for selection are set, and the number of sectors N is greater than or equal to 10 , N is a natural number. 4. The method for a multi-mode mobile terminal to select a high-quality-of-service sector according to any one of claims 1 to 3, wherein in step S200, the particle swarm optimization algorithm is run on the multi-mode mobile terminal. 5. The method for selecting a high-quality-of-service sector for a multi-mode mobile terminal according to claim 4, wherein in step S200, a particle swarm optimization algorithm is used to select a sector in a sectorized cell.