大规模MIMO信号检测技术研究

发布时间：2018-03-10 19:55

本文选题：大规模MIMO　切入点：信号检测　出处：《重庆大学》2016年硕士论文　论文类型：学位论文

【摘要】：大规模MIMO具有传输效率高、频谱利用率高、能量效率高、抗干扰能力强等优点近年来成为无线通信领域一个研究热点。信号检测技术是大规模MIMO的核心技术之一,对整个系统的抗干扰能力及可靠性有着重要影响。随着大规模MIMO天线数目的增加,常规MIMO的信号检测技术应用到大规模MIMO当中,存在复杂度过高、检测性能不佳的问题。为解决这两个基本问题,论文重点研究了两种启发式算法似然上升搜索算法(LAS)及动态禁忌搜索算法(RTS)。论文主要工作如下:(1)给出了大规模MIMO系统模型。分析了现有信号检测算法的性能和复杂度。重点研究了LAS、RTS算法及两种算法的衍生算法,并通过实验仿真分析初值性能、初值个数及最大搜索次数,对BER性能及复杂度的影响,为论文研究工作提供了切入点。(2)针对LAS信号检测算法易陷入局部极小、检测性能不佳以及现有改进算法改善了局部极小的情况、但大大增加了信号检测复杂度的问题,提出一种结合遗传性质的改进LAS信号检测算法。该算法在传统LAS算法基础上,通过分析无差错接收情况下ML代价函数的统计信息判定解的质量,然后用解的质量不断修正算法所需要的迭代次数。同时继承最优解向量、引入随机因素对最优解向量的某些位置进行变异、得到下一次迭代的初始向量。仿真结果表明结合遗传性质的改进LAS算法进一步提高了算法的性能,使LAS算法所需要的迭代次数减少,降低了复杂度。(3)针对RTS算法在高阶调制的情况下,检测性能较差的问题,提出一种基于等效信道维度的改进分层RTS算法。该算法在分层RTS算法基础上,根据等效信道矩阵维度,自适应改变最大搜索步数。在等效信道矩阵维度较小时,用较小的最大搜索步数避免迂回搜索;在等效信道矩阵维度较大时,用较大的最大搜索步数让搜索的范围更广,增强搜索能力,提高检测性能。仿真结果表明,算法提高了信号检测能力,提升了系统可靠性。
[Abstract]:Large-scale MIMO has the advantages of high transmission efficiency, high spectral efficiency, high energy efficiency and strong anti-jamming ability. In recent years, signal detection technology is one of the core technologies of large-scale MIMO. With the increase of the number of large-scale MIMO antennas, the signal detection technology of conventional MIMO is applied to large-scale MIMO, and the complexity is too high. To solve these two basic problems, This paper focuses on two heuristic algorithms, likelihood ascending search (LAS) and dynamic Tabu search (MIMO). The main work of this paper is as follows: (1) A large scale MIMO system model is presented. The performance and complexity of existing signal detection algorithms are analyzed. The algorithm of LASORTS and the derivation algorithm of the two algorithms are studied in detail. And through the experimental simulation analysis of the initial value performance, the number of initial values and the maximum number of searches, the effects on the performance and complexity of BER are analyzed, which provides a breakthrough point for the research work of this paper. (2) for the LAS signal detection algorithm, it is easy to fall into local minima. The poor detection performance and the existing improved algorithms improve the local minima, but greatly increase the complexity of signal detection. An improved LAS signal detection algorithm combining genetic properties is proposed. The algorithm is based on the traditional LAS algorithm. By analyzing the statistical information of ML cost function in the case of error-free reception, the quality of the solution is determined, and then the iterative times required by the algorithm are constantly corrected with the quality of the solution. At the same time, the optimal solution vector is inherited. The initial vector of the next iteration is obtained by introducing random factors to some positions of the optimal solution vector. The simulation results show that the improved LAS algorithm with genetic properties can further improve the performance of the algorithm. The number of iterations required for LAS algorithm is reduced, and the complexity is reduced. (3) aiming at the problem of poor detection performance of RTS algorithm in the case of high order modulation, An improved hierarchical RTS algorithm based on the equivalent channel dimension is proposed. The algorithm adaptively changes the maximum search steps according to the equivalent channel matrix dimension based on the hierarchical RTS algorithm. When the dimension of the equivalent channel matrix is larger, the larger maximum search steps can make the search range wider, enhance the search ability and improve the detection performance. The simulation results show that, when the dimension of the equivalent channel matrix is large, the maximum search steps are used to avoid roundabout search. The algorithm improves the signal detection ability and the reliability of the system.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN911.23;TN919.3

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