智能反射面辅助的大规模MIMO系统中叠加导频优化设计

Superimposed Pilot Optimization Design in Intelligent Reflecting Surface-Aided Massive MIMO System

相较于时分复用导频,叠加导频因具有频带利用率高和传输速率快的优点而受到广泛关注。基于此,研究了叠加导频模式下智能反射面辅助的大规模多输入多输出(MIMO)系统,推导了基站使用最小二乘信道估计和最大比合并检测时系统可达和速率的闭合表达式。另外,为了改善叠加导频模式的导频信号与数据信号的相互干扰问题,提出了一种基于几何规划和遗传算法的迭代优化算法。该算法能够通过协同控制导频信号的功率控制系数、数据信号的功率控制系数和智能反射面相位,实现系统可达和速率的最大化。仿真结果验证了速率闭合表达式的准确性,同时表明了所提算法能够有效地提高系统速率性能。

Compared with time-multiplexed pilot,superimposed pilot has been widely concerned for high bandwidth utilization and fast transmission rate.Therefore,the intelligent reflecting surface-aided massive multiple-input multiple-output(MIMO)system in superimposed pilot mode is studied.Besides,the closed-form expression of sum achievable rate is derived when base stations use least square channel estimation and maximum ratio combining detection.In addition,an iterative optimization algorithm based on geometric programming and genetic algorithm is proposed to improve the mutual interference between the pilot signal and the data signal in superimposed pilot mode.The proposed algorithm can maximize the sum achievable rate of the system by cooperatively controlling the power control coefficient of the pilot signal,the power control coefficient of the data signal and the phase of the intelligent reflecting surface.The simulation results verify the accuracy of the closed-form expression of the achievable rate,and show that the proposed algorithm improves effectively the system rate performance.