大规模多输入多输出（Multiple Input Multiple Output，MIMO）技术作为5G关键技术之一，可有效改善系统频谱效率和能量效率，由于其能够应对用户所需的高数据率，从而受到广泛的关注和研究。但大规模天线的使用导致射频链路急剧增加，产生较大的能量消耗，如何提高系统的能效，是大规模MIMO亟待解决的核心问题。天线选择和预编码技术是提高大规模MIMO系统能量效率的两项重要技术，因此，本文对其进行研究以优化大规模MIMO系统的能效。主要研究工作如下：

       本文考虑单小区多用户大规模MIMO系统的下行链路，通过使用迫零（Zero Forcing，ZF）预编码处理，从能效最大化的角度出发，重点研究了发射天线数、发射功率和最佳发射天线矩阵对系统能效的影响，提出一种在ZF准则下，基于天线选择的能效优化资源分配算法。首先利用朗伯W函数推导出基站发射天线数和发射功率的最佳表达式，经迭代求解得出最佳发射功率和发射天线数；其次求最佳发射天线矩阵，主要思想是以最大化能效为目标，使用一种低复杂度的天线选择策略，利用凸优化方法选出最佳发射天线矩阵，该算法的目的在于降低系统运算复杂度，提高系统能效；最后通过联合调整基站发射天线数、发射功率、发射天线矩阵来优化系统能效。仿真结果表明，所提算法能够有效降低计算复杂度，提高系统能效和容量。

       ZF预编码方案虽可一定程度上消除用户间干扰，但该方案也导致加性噪声被加权放大。因此，为了提高系统性能，本文提出一种结合天线选择的信漏噪比（Signal to Leakage and Noise Radio，SLNR）预编码算法优化系统能效。首先表明SLNR预编码方案系统性能优于ZF预编码方案；其次使用改进的天线选择算法得到最佳发射天线矩阵，该算法主要思想是以最大化用户接收功率和最大化系统容量为优化目标，并利用凸优化选出最佳发射天线矩阵；然后以最大化SLNR为准则求最佳用户预编码矩阵；最后通过联合调整发射天线矩阵和预编码矩阵来优化系统能效。仿真结果表明，所提联合算法显著提高了系统能效和容量。

       As one of the key technologies of 5G, massive multiple-input multiple-output (Multiple Input Multiple Output, MIMO) technology can effectively improve the spectral efficiency and energy efficiency of the system, and it has received extensive attention and research due to its ability to cope with the high data rates required by users. However, as the use of large-scale antennas leads to a dramatic increase in RF links and generates large energy consumption, How to improve the energy efficiency of the system is the core problem that needs to be solved in large-scale MIMO. Antenna selection and precoding techniques are two important techniques to improve the energy efficiency of large-scale MIMO systems, so they are investigated in this paper to optimize the energy efficiency of large-scale MIMO system. The main research work is as follows:

       This paper considers the downlink of a single-cell multi-user large-scale MIMO system, By using zero forcing (Zero Forcing, ZF) precoding processing, the impact of the number of transmitting antennas, transmitting power and the optimal transmitting antenna matrix on the energy efficiency of the system is taken account of the perspective of maximizing energy efficiency, and an energy-efficient optimal resource allocation algorithm based on antenna selection under the ZF criterion is proposed. Firstly, the optimal expressions for the number of transmitting antennas and transmitting power of the base station are derived using the Lambert W function, and the optimal transmitting power and number of transmitting antennas are obtained by iterative solution; Secondly, the optimal transmit antenna matrix is found. The main idea of this method is to aim to maximizing system energy efficiency,It uses a low-complexity antenna selection strategy, and selects the final transmitting antenna matrix using convex optimization, The purpose of the algorithm is to reduce the computational complexity of the system and improve the energy efficiency of the system; Finally, the number of transmitting antennas, transmitting power and transmitting antenna matrix of the base station are jointly adjusted to optimise the energy efficiency of the system. The simulation results show that the proposed algorithm can effectively reduce the computational complexity and improve the system energy efficiency and capacity.

       Although ZF precoding scheme can eliminate inter user interference to some extent, it also leads to weighted amplification of additive noise. Therefore, in order to improve the system performance, this paper proposes a signal to leakage and noise radio (Signal to Leakage and Noise Radio, SLNR) precoding algorithm combined with antenna selection to optimize the system energy efficiency. Firstly, it is shown that the system performance of the SLNR precoding scheme is better than that of the ZF precoding scheme; secondly, the optimal transmit antenna matrix is obtained by using the improved antenna selection algorithm. The main idea of the method is to maximize the user's received power and maximize the system capacity as the optimization objectives, and uses convex optimization to select the best transmit antenna matrix; then, based on the criterion of maximizing SLNR, the optimal user precoding matrix is obtained; finally, the system energy efficiency is optimized by jointly adjusting the transmit antenna matrix and the precoding matrix. The simulation results show that the proposed joint algorithm significantly improves the system energy efficiency and capacity.

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