降维-联合迭代算法的大规模MIMO系统能效研究

Dimension reduction-joint iterative algorithm for massive MIMO system energy efficiency study

针对大规模多输入多输出(MIMO)技术系统能效降低的问题,同时考虑大规模MIMO系统的上、下行链路,提出一种降维-联合迭代算法。该算法考虑发射功率、发射天线数和终端用户数对系统能效的影响,首先基于迫零预处理和动态的功耗模型推导出系统的能效表达式,然后通过注水算法求得最佳的发射功率,将三维问题转化为二维优化,最后联合迭代发射天线和终端用户数,输出系统最优能效值。仿真结果表明,在不同信道状态单小区多用户场景下以及使用不同导频复用因子的多小区多用户场景下,该算法都可以在降低复杂度的情况下取得较好的系统能效性能。

In view of the reduced EE of the massive MIMO(multiple-input multiple-output)system,a dimension reduction-joint iterative optimization algorithm considering both uplink and downlink of massive MIMO system is proposed.In this algorithm,the influence of the transmitting power,the number of transmitting antennas and the number of terminal users on the EE of the system is taken into account.The EE expression of the system is derived based on zero-forcing preprocessing and dynamic power consumption model first,and then the optimal transmission power is obtained by water-filling algorithm,which transforms the three-dimensional problem into two-dimensional optimization.The optimal EE value of the system is obtained by jointly iterating the transmitting antennas and terminal users.Simulation results show that the proposed algorithm can achieve good system EE performance under the premise of reducing system complexity in single-cell multi-user scenarios with different channel states and multi-cell multi-user scenarios with different pilot multiplexing factors.