双目标优化的多用户MIMO-OFDM系统资源分配方法
发布时间：2018-08-02 07:35
【摘要】：随着现代通信对系统吞吐量的需求不断上升,通过无线资源(时、频、功率)分配提高系统吞吐量非常重要。在多用户MIMO-OFDM系统中,由于引入了空分多址,资源在可分配的维度上增加了一倍,使得原本就难以优化的资源分配问题更加复杂。现有的大多数研究为了降低分配的复杂度,对资源分配的自由度做了限制,有些规定每个子载波上只可以容纳一个用户,这就没有利用空分多址带来的频谱增益;有些集中限定功率在子信道上平均分配,这样就放弃了利用不同信道的差异带来的功率增益。另一方面,由于现代社会倡导绿色通信,在一定的系统吞吐量要求下最小化系统总功耗也日渐成为当今4G甚至是未来5G的目标。吞吐量优化和功率优化在目标诉求上相互矛盾,现有的大多数文献都集中在研究优化一个目标上,要么是保证系统一定的吞吐量来集中优化功率,要么在保证系统一定的总消耗功率的前提下优化系统吞吐量,据我们所知,目前文献中没有对多用户MIMO-OFDM系统中功率和速率进行过联合优化,不能进行联合优化的原因有很多,比如存在非线性约束造成优化问题非凸难以求解,以及在用传统的贪婪算法为子载波选择用户的时候无法同时兼顾功率和速率的优化等,造成解决联合优化只能采用穷举法,由于多用户MIMO-OFDM系统较大的资源自由度,因计算量巨大根本不可行。针对以上问题,本文基于多用户MIMO-OFDM系统,提出了一种功率速率联合优化算法。即在最小化系统总消耗功率的同时,使得系统吞吐量最大。将用户对子载波的选择融入到对功率优化和速率优化中完成,减少了因为资源自由度过多带来的问题复杂性。首先我们建立功率和速率的双目标优化模型,由于存在非线性约束,因此设计了原问题的对偶问题,解决了原问题的非凸性,将原问题转变为了可以求解的凸优化问题,同时证明了原问题和其对偶问题的对偶间隙为0,即证明了通过求解对偶问题的最优解也就求得了原问题的最优解。在求解对偶问题时,提出了一种解对偶问题的分解和迭代方法,减少了分配的复杂度。最后,我们通过控制迭代的精度,来最小化系统能耗并最大化系统吞吐量,实现了同时提高功率资源和频谱资源利用效率的目的。仿真结果显示,本文算法在实现的系统吞吐量和消耗的系统总功率方面都优于传统的单目标优化算法。
[Abstract]:With the increasing demand for system throughput in modern communication, it is very important to improve the system throughput through the allocation of wireless resources (time, frequency, power). In multiuser MIMO-OFDM systems, the problem of resource allocation, which is difficult to optimize, is complicated by the introduction of spatial division multiple access (SDMA), which results in a doubling of the allocation of resources in the distributable dimension. In order to reduce the complexity of allocation, most of the existing researches restrict the degree of freedom of resource allocation, some stipulate that only one user can be accommodated on each subcarrier, which does not make use of the spectrum gain brought by space-division multiple access (SDMA). In some cases the power is assigned equally over the subchannels, thus giving up the power gain derived from the differences between the different channels. On the other hand, because the modern society advocates green communication, minimizing the total power consumption of the system under certain system throughput requirements is becoming the goal of 4G and even 5G in the future. Throughput optimization and power optimization are contradictory in terms of target demand. Most of the existing literatures focus on optimizing a target, or to ensure a certain throughput of the system to concentrate on power optimization. Either we can optimize the system throughput under the premise of ensuring a certain total power consumption. As far as we know, there is no joint optimization of power and speed in multi-user MIMO-OFDM system, and there are many reasons why the joint optimization can not be carried out. For example, the nonconvex optimization problem is difficult to solve due to the existence of nonlinear constraints, and when the traditional greedy algorithm is used to select users for subcarriers, the power and speed optimization can not be taken into account at the same time, so the solution of joint optimization can only be solved by exhaustive method. Because of the large degree of resource freedom in multiuser MIMO-OFDM system, it is not feasible because of the huge amount of computation. To solve the above problems, this paper proposes a joint power rate optimization algorithm based on multi-user MIMO-OFDM system. While minimizing the total power consumption, the system throughput is maximized. The selection of subcarriers by users is integrated into power optimization and rate optimization, which reduces the complexity of the problem caused by the excessive degree of freedom of resources. First of all, we establish a two-objective optimization model of power and rate. Because of the nonlinear constraints, we design the dual problem of the original problem, solve the non-convexity of the original problem, and turn the original problem into a convex optimization problem that can be solved. At the same time, it is proved that the duality gap of the original problem and its dual problem is 0, that is, the optimal solution of the original problem is obtained by solving the optimal solution of the dual problem. In solving dual problems, a decomposition and iteration method is proposed, which reduces the complexity of allocation. Finally, by controlling the precision of iteration, we minimize the system energy consumption and maximize the system throughput, and achieve the purpose of improving the efficiency of power resources and spectrum resources at the same time. Simulation results show that the proposed algorithm is superior to the traditional single-objective optimization algorithm in terms of system throughput and total power consumption.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3;TN929.53
本文编号：2158663
[Abstract]:With the increasing demand for system throughput in modern communication, it is very important to improve the system throughput through the allocation of wireless resources (time, frequency, power). In multiuser MIMO-OFDM systems, the problem of resource allocation, which is difficult to optimize, is complicated by the introduction of spatial division multiple access (SDMA), which results in a doubling of the allocation of resources in the distributable dimension. In order to reduce the complexity of allocation, most of the existing researches restrict the degree of freedom of resource allocation, some stipulate that only one user can be accommodated on each subcarrier, which does not make use of the spectrum gain brought by space-division multiple access (SDMA). In some cases the power is assigned equally over the subchannels, thus giving up the power gain derived from the differences between the different channels. On the other hand, because the modern society advocates green communication, minimizing the total power consumption of the system under certain system throughput requirements is becoming the goal of 4G and even 5G in the future. Throughput optimization and power optimization are contradictory in terms of target demand. Most of the existing literatures focus on optimizing a target, or to ensure a certain throughput of the system to concentrate on power optimization. Either we can optimize the system throughput under the premise of ensuring a certain total power consumption. As far as we know, there is no joint optimization of power and speed in multi-user MIMO-OFDM system, and there are many reasons why the joint optimization can not be carried out. For example, the nonconvex optimization problem is difficult to solve due to the existence of nonlinear constraints, and when the traditional greedy algorithm is used to select users for subcarriers, the power and speed optimization can not be taken into account at the same time, so the solution of joint optimization can only be solved by exhaustive method. Because of the large degree of resource freedom in multiuser MIMO-OFDM system, it is not feasible because of the huge amount of computation. To solve the above problems, this paper proposes a joint power rate optimization algorithm based on multi-user MIMO-OFDM system. While minimizing the total power consumption, the system throughput is maximized. The selection of subcarriers by users is integrated into power optimization and rate optimization, which reduces the complexity of the problem caused by the excessive degree of freedom of resources. First of all, we establish a two-objective optimization model of power and rate. Because of the nonlinear constraints, we design the dual problem of the original problem, solve the non-convexity of the original problem, and turn the original problem into a convex optimization problem that can be solved. At the same time, it is proved that the duality gap of the original problem and its dual problem is 0, that is, the optimal solution of the original problem is obtained by solving the optimal solution of the dual problem. In solving dual problems, a decomposition and iteration method is proposed, which reduces the complexity of allocation. Finally, by controlling the precision of iteration, we minimize the system energy consumption and maximize the system throughput, and achieve the purpose of improving the efficiency of power resources and spectrum resources at the same time. Simulation results show that the proposed algorithm is superior to the traditional single-objective optimization algorithm in terms of system throughput and total power consumption.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3;TN929.53
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