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

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.