LTE网络下D2D通信的资源分配算法研究

发布时间：2018-09-03 09:53

【摘要】：随着无线通信技术的快速发展,人们对于通信系统的传输速率、系统容量以及频谱利用率等方面的要求变得越来越高。为了改善频谱资源日益紧张的现状,终端直通技术(Device-to-Device,D2D)作为一种短距离直接通信技术应运而生。D2D通信就是当两个终端距离较近时,可以在基站的控制下直接进行数据传输,而不需要经过基站中继转发。在网络负载较高的情况下,D2D通信可以复用蜂窝系统的频谱资源,提高频谱利用率。但频谱复用会产生同频干扰,如何通过合理的资源分配算法降低D2D用户和蜂窝用户之间的干扰,变得至关重要。通常情况下,基站可以通过信道分配、功率控制和模式选择等方式来分配资源。在分析了国内外对于D2D通信的研究后,本文进一步对资源分配算法进行研究,具体工作和研究成果如下:首先,现有的算法都是假定D2D用户在各个信道上的功率相同,没有考虑信道质量的差异。针对以上不足,提出了一种集中式资源分配的最优算法。该算法以最大化D2D用户总容量为目标建立凸优化函数,控制蜂窝用户和D2D用户的发射功率。通过拉格朗日乘子法求解最优功率,并把信道分配给具有最大容量的D2D用户。仿真结果表明,该算法能显著增大D2D用户总容量,提升系统的整体性能,但计算复杂度较高。其次,为了降低算法复杂度,便于在实际场景下应用,提出了一种分布式资源分配的次优算法。该算法在两个阶段分别进行信道分配和功率控制,先选出D2D用户的复用信道集合,然后在信道分配完成的基础上,优化蜂窝用户和D2D用户的发射功率,最大化D2D用户总容量。仿真结果表明,该算法能有效降低算法复杂度,性能较为接近最优算法。最后,考虑到实际场景下还存在用户分布不均匀的情况,为了提高系统公平性,提出了一种基于公平性改进的次优算法。该算法重点改进了信道分配方案,定义了复用参数和复用参数门限值,限制每个D2D用户复用的信道数量。仿真结果表明,该算法在用户分布不均匀的场景下显著提高了系统公平性,且性能也较为接近最优算法。
[Abstract]:With the rapid development of wireless communication technology, the requirements of transmission rate, system capacity and spectrum efficiency of communication system become more and more high. In order to improve the shortage of spectrum resources, terminal through technology (Device-to-Device,D2D), as a short-range direct communication technology, emerges as the times require. D2D communication is that when two terminals are close, the data can be transmitted directly under the control of the base station. It does not need to be relayed through the base station. In the case of high network load, D2D communication can reuse the spectrum resources of the cellular system and improve the spectrum efficiency. However, spectrum multiplexing can produce co-frequency interference. How to reduce the interference between D2D users and cellular users through a reasonable resource allocation algorithm becomes very important. In general, the base station can allocate resources through channel allocation, power control and mode selection. After analyzing the research of D2D communication at home and abroad, this paper further studies the resource allocation algorithm. The specific work and research results are as follows: firstly, the existing algorithms assume that the power of D2D users is the same in each channel. The difference in channel quality is not taken into account. In order to solve the above problems, an optimal algorithm for centralized resource allocation is proposed. To maximize the total capacity of D2D users, the algorithm establishes a convex optimization function to control the transmit power of both cellular and D2D users. The optimal power is solved by Lagrange multiplier method and the channel is allocated to D2D users with maximum capacity. Simulation results show that the proposed algorithm can significantly increase the total capacity of D2D users and improve the overall performance of the system, but the computational complexity is high. Secondly, in order to reduce the complexity of the algorithm and facilitate its application in practical scenarios, a sub-optimal algorithm for distributed resource allocation is proposed. The algorithm performs channel allocation and power control in two stages, and selects the multiplexed channel set of D2D users, then optimizes the transmission power of cellular users and D2D users based on the completion of channel allocation, and maximizes the total capacity of D2D users. Simulation results show that the algorithm can effectively reduce the complexity of the algorithm and the performance is close to the optimal algorithm. Finally, in order to improve the fairness of the system, an improved sub-optimal algorithm based on fairness is proposed in order to improve the fairness of the system. The algorithm focuses on improving the channel allocation scheme, defines multiplexing parameters and threshold values of multiplexing parameters, and limits the number of channels to be multiplexed by each D2D user. Simulation results show that the proposed algorithm can significantly improve the fairness of the system in the scenario of uneven user distribution, and the performance of the algorithm is close to that of the optimal algorithm.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5

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