CN102547823B - Method and system for determining scheduling users during network simulation - Google Patents

Abstract

The invention discloses a method and system for determining scheduling users in network simulation, comprising: obtaining the location information of users of completed simulation snapshots; and according to the obtained location information, for each user of this simulation snapshot, start Among the users of the simulated snapshot, respectively determine the users whose positions are close to the users of the simulated snapshot; and estimate the historical transmission rate of each user of the simulated snapshot based on the transmission rates of the determined users whose positions are close; and Based on the channel quality index of each user in this simulation snapshot and the estimated historical transmission rate, use the proportional fair scheduling strategy to determine the scheduling users of this simulation snapshot. By adopting the solution provided by the embodiment of the present invention, the rationality of considering the balance between throughput and fairness in static system-level simulation is improved.

Description

Method and system for determining user scheduling in network simulation

technical field

The present invention relates to the technical field of network simulation in the technical field of wireless communication, in particular to a method and system for determining scheduling users in network simulation.

Background technique

Network simulation is a common method for algorithm research and network planning in 3G systems. It uses computer means to simulate a simplified network structure or key technologies of the physical layer, and then investigates the performance of the virtual network. Network simulation is essentially a sand table exercise of network performance and algorithm efficiency.

Network simulation is generally divided into link level (Link Level) simulation and system level (System Level) simulation: link level simulation is mainly for the wireless transmission technology of the physical layer (such as coding, interleaving, modulation and spread spectrum, etc.) Simulation; system-level simulation is mainly for wireless access systems with network topology simulation.

System-level simulation is generally divided into static simulation and dynamic simulation. The difference is that the concept of time flow is usually incorporated into dynamic simulation, so that information such as the user's position movement, speed change, and throughput increase and decrease at two moments before and after can be accurately recorded; while static system-level simulation is more Using the so-called Monte Carlo simulation, Monte Carlo simulation regards the operation behavior of the entire mobile communication system as the statistical average of the behavior samples shown in multiple time segments, and each time segment is called a snapshot , which reflects the relatively stable behavior of the system in the short run.

For the static simulation based on Monte Carlo technology, each simulation is independent of each other, that is, the user distribution between two simulation snapshots is not related. Therefore, in this sense, it is generally believed that static simulation has no memory, while dynamic simulation has memory. Usually, dynamic simulation is more complex and requires longer simulation time.

Traditionally, considering the hardware resources occupied by the simulation and the time required for the simulation, the planning software used in the project often simulates a large-scale network, and generally adopts a static simulation mechanism, while a dynamic simulation mechanism is mainly used for wireless resource management ( RRM) algorithm research, only simulates small-scale network topology or even a single site.

In HSPA (High-Speed Packet Access), WiMax (Worldwide Interoperability for Microwave Access), B3G (yondThird Generation), UMB, and LTE (Long Term Evolution) mobile communication systems , the packet data service is mainly carried by the shared data channel; compared with the traditional voice service, in the packet data service on the shared channel, the channel resource occupied by each user is not pre-allocated, but the system uses a certain scheduling shared between users.

When measuring the performance of scheduling strategies, two indicators are usually used, namely, throughput and fairness. The scheduling algorithm is a feature of the data service system. The purpose is to make full use of the time-varying characteristics of the channel to obtain multi-user diversity gain and improve the throughput of the system. The throughput is generally expressed by the total amount of data transmitted per cell unit time. Fairness is to consider whether all users who request transmission have a certain service opportunity. A good scheduling algorithm should take into account the overall throughput of the cell and the fairness of network resources occupied by all users to obtain a better compromise. Several common scheduling strategies are briefly introduced below.

Maximum signal-to-interference ratio C/I scheduling strategy: If the data is transmitted when the channel condition is good, the transmission rate can be increased and the redundancy of coding can be reduced. The maximum C/I algorithm is to select only the user with the largest C/I when selecting the scheduling user for transmitting data, that is, let the user with good channel condition transmit all the time, and when the channel becomes worse, let other users with better channel In this way, the effect of multi-user diversity is fully utilized.

Round robin scheduling strategy: When considering fairness, round robin algorithms are generally used as the standard for measurement. Each user occupies the allocated time slot and power with the same probability. Therefore, from the perspective of occupying these two resources, this scheduling algorithm is the most fair. When scheduling each time, it has the same nature as the maximum C/I algorithm, and does not consider the previous scheduling situation of each user. It can be said that these two scheduling algorithms are memoryless.

Proportional fair scheduling strategy: In order to achieve a good compromise between throughput and fairness, a scheduling algorithm called proportional fairness is proposed. At time t, the historical average transmission rate of mobile station k is represented by R _k (t) (k=1, Λ, K), and its requested transmission rate is represented by DRC _k (t), then the selected one is determined by the following formula Scheduling user:

$\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; \{</span>\frac{\mathrm{<span\; class="notranslate">\; DR</span>}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; ;</span>$

It can be seen that the proportional fair scheduling strategy needs the user's historical throughput information as input. On the other hand, from the background technology description of the static system-level simulation above, it can be seen that the memoryless nature of the static system-level simulation technology is essentially It is difficult to implement the proportional fair scheduling strategy.

At present, a static system-level simulation technology that adopts a proportional fair scheduling strategy in the prior art is that the system sorts the pilot signal quality indicators of each HSDPA user in the same cell from large to small, and then uses a certain ratio according to the sorting results. Select a set number of HSDPA users in the cell as scheduled HSDPA users for data transmission.

This scheme essentially ignores the consideration of the historical throughput of the proportional fairness algorithm, and only selects the scheduled users based on the one-dimensional indicator of the pilot channel quality that is easy to obtain in static simulation. In fact, it is still the maximum C/I scheduling The algorithm does not really reflect the characteristics of the proportional fair algorithm, and it cannot achieve a reasonable balance between throughput and fairness.

Contents of the invention

Embodiments of the present invention provide a method and system for determining user scheduling in network simulation to solve the problem in the prior art that a reasonable balance between throughput and fairness cannot be achieved in static system-level simulation.

An embodiment of the present invention provides a method for determining user scheduling in network simulation, including:

Get the location information of the user who has completed the simulation snapshot;

According to the obtained location information, from the users of the completed simulation snapshot, respectively determine the users whose positions are close to the users of this simulation snapshot;

Estimate the historical transmission rate of each user in this simulation snapshot based on the determined transmission rate of users with close locations;

Based on the channel quality index of each user in this simulation snapshot and the estimated historical transmission rate, use the proportional fair scheduling strategy to determine the scheduling users of this simulation snapshot.

An embodiment of the present invention also provides a system for determining user scheduling in network simulation, including:

An acquisition module, configured to acquire the location information of the user of the completed simulation snapshot;

The first determining module is used to determine, from the users of the completed simulation snapshots, users close to the positions of the users of the simulation snapshot according to the acquired location information;

An estimating module, configured to estimate the historical transmission rate of each user in this simulation snapshot based on the determined transmission rate of users whose positions are close;

The second determination module is configured to determine the scheduling users of the simulation snapshot based on the channel quality indicators and estimated historical transmission rates of each user in the simulation snapshot, using a proportional fair scheduling strategy.

In the method provided by the embodiment of the present invention, when performing network simulation, for the completed simulation snapshots, the user's location information and transmission rate of each simulation snapshot are stored, and when performing this simulation snapshot, according to the completed For the location information of the users in the simulation snapshot, for each user in the simulation snapshot, respectively determine the users who are close to the users in the simulation snapshot, and based on the determined transmission rate of the users in the close location, estimate the current simulation The historical transmission rate of each user in the snapshot, and the channel quality index and estimated historical transmission rate of each user based on this simulation snapshot, use the proportional fair scheduling strategy to determine the scheduling user of this simulation snapshot. Since the user's transmission rate is related to the channel quality, and the channel quality is related to the user's location, based on the transmission rate of the user close to the user's location in this simulation snapshot, the historical transmission rate of the user can be estimated more accurately, so that It can realize the channel quality index and estimated historical transmission rate of each user based on this simulation snapshot, and use the proportional fair scheduling strategy to determine the scheduling users of this simulation snapshot, thereby improving the throughput and fairness in static system-level simulation The rationale for balanced consideration.

Description of drawings

Fig. 1 is one of the flow charts of the method for determining the scheduling user in the network simulation provided by the embodiment of the present invention;

Fig. 2 is the second flow chart of the method for determining the scheduling user in the network simulation provided by the embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a system for determining user scheduling in network simulation provided by an embodiment of the present invention.

Detailed ways

In order to provide an implementation plan for improving the rationality of considering the balance between throughput and fairness in static system-level simulation, an embodiment of the present invention provides a method and system for determining user scheduling in network simulation. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention. And in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

An embodiment of the present invention provides a method for determining user scheduling in network simulation, as shown in FIG. 1 , including:

Step S101. Obtain the location information of the user of the completed simulation snapshot.

Step S102 , according to the acquired location information, from among the users of the completed simulation snapshot, respectively determine users whose locations are close to each user of the current simulation snapshot.

Step S103, based on the determined transmission rates of users in close locations, estimate the historical transmission rates of each user in this simulation snapshot.

Step S104 , based on the channel quality index and the estimated historical transmission rate of each user in this simulation snapshot, use a proportional fair scheduling strategy to determine the scheduled users for this simulation snapshot.

The method and system provided by the present invention will be described in detail below with specific embodiments in conjunction with the accompanying drawings.

In the method provided by the embodiment of the present invention, for each simulation snapshot that has been completed, the user's location information and transmission rate of each simulation snapshot (the transmission rate is also equivalent to the throughput in static simulation), as shown in Table 1 Shown:

Table 1:

The above table 1 shows the location information and transmission rate of the users of the completed n-1 simulation snapshots, where the location information is represented by coordinates, and L _i represents the number of users of the i-th simulation snapshot.

Based on the location information and transmission rate of the users of the completed n-1 simulation snapshots shown in Table 1 above, the scheduling user determination method provided by the embodiment of the present invention is used to determine the scheduling users of the nth simulation snapshot, as shown in the figure 2, the detailed description is as follows:

Step S201, obtaining the location information of the user who has completed n-1 simulation snapshots, which can be obtained from the stored Table 1 above.

Step S202 , according to the acquired location information, for each user of the nth simulation snapshot, from the users of the n-1th simulation snapshot, respectively determine users who are close to each user of the nth simulation snapshot.

Wherein, the number of each user and the location information of each user in the nth simulation snapshot can be determined by using existing network simulation technology, and will not be described in detail here.

In the embodiment of the present invention, for this step, several specific ways are proposed as follows:

The first method: For each user of the nth simulation snapshot, determine the user closest to the user from the users of each simulation snapshot of the n-1 simulation snapshots that have been completed, specifically using the following formula Sure:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\sqrt{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, U _{i, j} is the user who is determined to be the closest user to the jth user in the nth simulation snapshot among the users in the ith simulation snapshot; (x _{n, j} , y _{n, j} ) is the The j-th user's coordinates of the n-th simulation snapshot; (xi _{, l} , y _{i, l} ) is the l-th user's coordinates of the i-th simulation snapshot.

That is, for each user of the nth simulation snapshot, corresponding to each simulation snapshot in the n-1 simulation snapshots, determine a user closest to the user's location, and determine n-1 users in total. closest user.

The second method: for each user of the nth simulation snapshot, from the users of the completed n-1 simulation snapshots, determine the number of users who are close to the user's position; All the users of the n-1 simulation snapshots are arranged in descending order according to the distances from the users of the nth simulation snapshot, and the previously set number of users in this order are determined.

Among them, the set number can be set according to actual needs and experience, for example, it can be n-1, or 1. Taking the set number as 1 as an example, it is determined by the following formula:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; 1</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\sqrt{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, U _{n, j} is the user who is determined to be the closest user to the jth user of the nth simulation snapshot among the users of the n-1th simulation snapshot; (x _{n, j} , y _{n, j} ) is the j-th user's coordinates of the n-th simulation snapshot; (xi _{, l} , y _{i, l} ) is the l-th user's coordinates of the i-th simulation snapshot.

That is, for each user of the nth simulation snapshot, a set number of users whose positions are close to the user are determined.

The third method: for each user of the nth simulation snapshot, determine the closest user position to the nth simulation snapshot from the users of a simulation snapshot in the n-1 simulation snapshots that have been completed User; that is, first select a simulation snapshot from the n-1 simulation snapshots, and determine the user closest to the user in the nth simulation snapshot from the users in the selected simulation snapshot.

Specifically, a simulation snapshot can be randomly selected, or a specified simulation snapshot can be selected, such as the previous simulation snapshot, taking the selection result as the i-th simulation snapshot as an example, specifically determined by the following formula:

${\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; (</span>\sqrt{{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, U _{i, j} is the user who is determined to be the closest user to the jth user in the nth simulation snapshot among the users in the ith simulation snapshot; (x _{n, j} , y _{n, j} ) is the The j-th user's coordinates of the n-th simulation snapshot; (xi _{, l} , y _{i, l} ) is the l-th user's coordinates of the i-th simulation snapshot.

That is, for each user of the n-th simulation snapshot, a user whose location is closest to the user of the n-th simulation snapshot is determined from among the users selected for the first simulation snapshot.

Step S203, obtaining the transmission rate of the users whose positions are close to that determined in the above step S202, that is, it may be obtained from the stored Table 1 above.

Step S204: Estimate the historical transmission rate of each user in the n-th simulation snapshot based on the obtained transmission rate of users with close locations, which corresponds to the three ways of determining users with close locations in the above-mentioned step S202. In this step, the following is proposed Three methods of estimating historical transmission rates:

The first method: corresponding to the first method of determining users with close locations, for each user in the nth simulation snapshot, determine the user who is closest to the user in the completed n-1 simulation snapshots The average rate of the transmission rate, and use the average rate as the estimated historical transmission rate of the user, specifically determined by the following formula:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, R _n,j is the estimated historical transmission rate of user j in the nth simulation snapshot; R _i,j is the transmission rate of user U _i,j .

The second method: corresponding to the above-mentioned second method of determining users with close positions, for each user of the nth simulation snapshot, determine the average rate of the transmission rate of the set number of users, and calculate the average rate As the estimated historical transmission rate of the user, it is determined by the following formula:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ;</span>$

Among them, R _{n, j} is the estimated historical transmission rate of the jth user in the nth simulation snapshot; m is the set number; R _i is the transmission rate of the i-th user among the m users whose positions are close rate.

If the set number is 1, then R _n,j is the transmission rate of the user U _n,j .

The third method: Corresponding to the above third method of determining the user whose location is close, for each user of the nth simulation snapshot, the determined transmission rate of the user whose location is closest to the user is taken as the user's Estimated historical transfer rates, as follows:

R _n,j =R _i,j ;

Among them, R _n,j is the estimated historical transmission rate of user j in the nth simulation snapshot; R _i,j is the transmission rate of user U _i,j .

Step S205, determine the channel quality index of each user in the nth simulation snapshot, in different network simulation systems for actual networks of different standards, the channel quality index has different representations, for example, for 3GPP standard HSPA, LTE For the network simulation system of the system, the channel quality index may be the signal-to-interference ratio C/I; for the network simulation system of the 3GPP2 standard (EV-DO simulation system, UMB simulation system), the channel quality index may be the requested transmission rate.

The specific method for determining the channel quality index can use various methods in the existing network simulation technology, and will not be described in detail here.

Step S206 , based on the channel quality index of each user in the nth simulation snapshot and the estimated historical transmission rate, use a proportional fair scheduling strategy to determine the scheduled users for this simulation snapshot.

Taking the determination of one scheduling user as an example, the following formula is used for determination:

$k_{\mathrm{no}}=\arg \underset{j=1,\mathrm{\&Lambda;},L_{\mathrm{no}}}{\max }\left(\frac{{\mathrm{CIR}}_{\mathrm{no},j}}{R_{\mathrm{no},j}}\right);$ or

${\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; DRC</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, k _n is the determined scheduling user of the nth simulation snapshot; CIR _{n, j} is the C/I of the jth user of the nth simulation snapshot; DRC _{n, j} is the jth simulation snapshot of the nth simulation snapshot The requested transmission rate of each user; R _{n, j} is the estimated historical transmission rate of the jth user in the nth simulation snapshot; L _n is the number of users in the nth simulation snapshot.

Step S207, after the scheduling user of the nth simulation snapshot is determined through the above steps S201-step S206, then perform subsequent simulation processing according to the determined scheduling user, which will not be described in detail here; and in the nth simulation snapshot After completion, store the location information and transmission rate of each user in the nth simulation snapshot.

Based on the same inventive concept, according to the method for determining user scheduling in network simulation provided by the above-mentioned embodiments of the present invention, another embodiment of the present invention also provides a system for determining user scheduling in network simulation, the schematic diagram of which is shown in the figure 3, including:

Obtaining module 301, for obtaining the location information of the user of the completed simulation snapshot;

The first determination module 302 is used to determine, from the users of the completed simulation snapshots, the users who are close to the positions of the users of this simulation snapshot according to the acquired location information;

An estimation module 303, configured to estimate the historical transmission rate of each user in this simulation snapshot based on the determined transmission rate of users whose positions are close;

The second determination module 304 is configured to determine the scheduling users of this simulation snapshot by using a proportional fair scheduling strategy based on the channel quality indicators of each user in this simulation snapshot and the estimated historical transmission rate.

Preferably, the first determining module 302 is specifically configured to, for each user of the current simulation snapshot, determine the user closest to the user from the users of each completed simulation snapshot;

Estimation module 303, specifically for each user of this simulation snapshot, determine the average rate of the transmission rate of the user closest to the user's position in each simulation snapshot that has been completed, and use the average rate as the user's Estimated historical transfer rate.

Preferably, the first determination module 302 is specifically configured to, for each user of the simulation snapshot this time, determine a previously set number of users whose positions are close to the user from among the users of the completed simulation snapshot;

The estimation module 303 is specifically configured to, for each user of the current simulation snapshot, determine the average rate of the transmission rates of the set number of users, and use the average rate as the estimated historical transmission rate of the user.

Preferably, the first determining module 302 is specifically configured to, for each user of this simulation snapshot, determine the user closest to the user from among the users of a completed simulation snapshot;

The estimation module 303 is specifically configured to, for each user of the current simulation snapshot, use the determined transmission rate of the user closest to the user as the estimated historical transmission rate of the user.

Preferably, the second determination module 304 is specifically configured to determine the scheduling user of this simulation snapshot by using a proportional fair scheduling strategy based on the signal-to-interference ratio C/I of each user of the simulation snapshot and the estimated historical transmission rate; or

Based on the requested transmission rate of each user in this simulation snapshot and the estimated historical transmission rate, a proportional fair scheduling strategy is used to determine the scheduling users of this simulation snapshot.

To sum up, the solution provided by the embodiment of the present invention includes: obtaining the location information of the user of the completed simulation snapshot; and according to the obtained location information, for each user of the simulation snapshot this time, Among the users, respectively determine the users whose positions are close to the users in this simulation snapshot; and estimate the historical transmission rate of each user in this simulation snapshot based on the transmission rates of the determined users; and based on this simulation The channel quality index and estimated historical transmission rate of each user in the snapshot, using the proportional fair scheduling strategy, determine the scheduling user of this simulation snapshot. By adopting the solution provided by the embodiment of the present invention, the rationality of considering the balance between throughput and fairness in static system-level simulation is improved.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims (10)

1. a definite method for scheduling users during network simulation, is characterized in that, comprising:

Obtain the user's of completed simulation snapshot positional information;

According to the positional information of obtaining, from the user of completed simulation snapshot, determine respectively the user approaching with each customer location of this simulation snapshot;

The user's that position based on determining is approaching transmission rate, estimates each user's of this simulation snapshot historical transmission rate;

The historical transmission rate of the cqi of each user based on this simulation snapshot and estimation, is used direct ratio equity dispatching strategy, determines the dispatched users of this simulation snapshot.

2. the method for claim 1, is characterized in that, for each user of this simulation snapshot, from the user of completed simulation snapshot, determines respectively the user approaching with each customer location of this simulation snapshot, is specially:

For each user of this simulation snapshot, from the user of completed each simulation snapshot, determine and the immediate user of this customer location;

The historical transmission rate of estimating each user of this simulation snapshot, specifically comprises:

For each user of this simulation snapshot, determine in completed each simulation snapshot the Mean Speed with the immediate user's of this customer location transmission rate, and the historical transmission rate of the estimation using described Mean Speed as this user.

3. the method for claim 1, is characterized in that, for each user of this simulation snapshot, from the user of completed simulation snapshot, determines respectively the user approaching with each customer location of this simulation snapshot, is specially:

For each user of this simulation snapshot, from the user of completed simulation snapshot, determine the user of the front setting quantity approaching with this customer location;

The historical transmission rate of estimating each user of this simulation snapshot, specifically comprises:

For each user of this simulation snapshot, determine the user's of described setting quantity the Mean Speed of transmission rate, and the historical transmission rate of the estimation using described Mean Speed as this user.

4. the method for claim 1, is characterized in that, for each user of this simulation snapshot, from the user of completed simulation snapshot, determines respectively the user approaching with each customer location of this simulation snapshot, is specially:

For each user of this simulation snapshot, from the user of a completed simulation snapshot, determine and the immediate user of this customer location;

The historical transmission rate of estimating each user of this simulation snapshot, specifically comprises:

For each user of this simulation snapshot, using that determine and the historical transmission rate of the transmission rate immediate user of this customer location as this user's estimation.

5. the method as described in as arbitrary in claim 1-4, is characterized in that, described cqi is signal interference ratio C/I or request transmission rate.

6. a scheduling users during network simulation fixed system really, is characterized in that, comprising:

Acquisition module, for obtaining the user's of completed simulation snapshot positional information;

The first determination module, for according to the positional information of obtaining, from the user of completed simulation snapshot, determines respectively the user approaching with each customer location of this simulation snapshot;

Estimation module, for the approaching user's in the position based on determining transmission rate, estimates each user's of this simulation snapshot historical transmission rate;

The second determination module, for the cqi of each user based on this simulation snapshot and the historical transmission rate of estimation, is used direct ratio equity dispatching strategy, determines the dispatched users of this simulation snapshot.

7. system as claimed in claim 6, is characterized in that, described the first determination module, specifically for each user for this simulation snapshot, from the user of completed each simulation snapshot, is determined and the immediate user of this customer location;

Described estimation module, specifically for each user for this simulation snapshot, determine in completed each simulation snapshot the Mean Speed with the immediate user's of this customer location transmission rate, and the historical transmission rate of the estimation using described Mean Speed as this user.

8. system as claimed in claim 6, is characterized in that, described the first determination module, specifically for each user for this simulation snapshot, from the user of completed simulation snapshot, is determined the user of the front setting quantity approaching with this customer location;

Described estimation module, specifically for each user for this simulation snapshot, determines the user's of described setting quantity the Mean Speed of transmission rate, and the historical transmission rate of the estimation using described Mean Speed as this user.

9. system as claimed in claim 6, is characterized in that, described the first determination module, specifically for each user for this simulation snapshot, from the user of a completed simulation snapshot, is determined and the immediate user of this customer location;

Described estimation module, specifically for each user for this simulation snapshot, using that determine and the historical transmission rate of the transmission rate immediate user of this customer location as this user's estimation.

10. system as claimed in claim 6, it is characterized in that, described the second determination module, specifically for the signal interference ratio C/I of each user based on this simulation snapshot and the historical transmission rate of estimation, use direct ratio equity dispatching strategy, determine the dispatched users of this simulation snapshot; Or

The request transmission rate of each user based on this simulation snapshot and the historical transmission rate of estimation, used direct ratio equity dispatching strategy, determines the dispatched users of this simulation snapshot.