毫米波大规模MIMO系统中基于机器学习的自适应连接混合预编码  

Machine Learning-based Adaptive Connection Hybrid Precoding for mmWave Massive MIMO Systems

作　　者：甘天江 傅友华 王海荣[2,3] Gan Tianjiang;Fu Youhua;Wang Hairong(College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China;College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China;National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing, Jiangsu 210023, China)

机构地区：[1]南京邮电大学电子与光学工程学院、微电子学院,江苏南京210023 [2]南京邮电大学通信与信息工程学院,江苏南京210003 [3]射频集成与微组装技术国家地方联合工程实验室,江苏南京210023

出　　处：《信号处理》2020年第5期677-685,共9页Journal of Signal Processing

基　　金：国家自然科学基金(61771257);射频集成与微组装技术国家地方联合工程实验室开放基金(KFJJ20170304);南京邮电大学科研基金资助项目(NY218009)。

摘　　要：毫米波大规模MIMO系统混合预编码是提升无线通信系统容量和降低射频链使用数量的关键技术之一,但是仍然需要大量高精度的相移器实现阵列增益。为了解决这个问题,本文中,首先通过最大化每个用户的接收信号功率,得到自适应连接结构中射频链与基站天线匹配关系,然后创新地把基于机器学习的自适应交叉熵优化方法应用于1比特量化相移的自适应连接混合预编码器中。通过减小交叉熵和加入常数平滑参数保证收敛,自适应地更新概率分布以得到几乎最优的混合预编码器。最后,仿真验证了所提方案的可行性以及具有满意的可达和速率,与其他相同硬件复杂度的混合预编码方案相比具有更优的可达和速率性能。Millimeter-wave massive MIMO system hybrid precoding is one of the key technologies to increase the capacity of wireless communication systems and reduce the number of RF chains used,but still requires a large number of high-precision phase shifters to achieve array gain.To solve this problem,in this paper,first,by maximizing the received signal power of each user,the matching relationship between the RF chain and the base station antenna in the adaptive connection structure with one-bit quantized phase shifter is obtained,and then the adaptive cross entropy optimization method based on machine learning is innovatively applied to adaptive connection structure in hybrid precoding.By reducing the cross-entropy and adding constant smoothing parameter to ensure convergence,the probability distribution is adaptively updated to obtain an almost optimal hybrid precoder.Finally,simulations verify the feasibility and satisfactory achievable sum-rate of the proposed scheme.It has better achievable sum-rate performance compared with other hybrid precoding schemes with the same hardware complexity.

关 键 词：机器学习 自适应连接结构 1比特量化相移 自适应交叉熵优化 混合预编码 

分 类 号：TN929.5[电子电信—通信与信息系统]