Bai Jiawei, Wang Guangying, Wang Ming, Zhu Jinjin
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel). 2024 Sep 27;24(19):6276. doi: 10.3390/s24196276.
Cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) can effectively overcome the shadow fading effect and provide macro gain to boost the throughput of communication networks. Nevertheless, the majority of the existing studies have overlooked the user-centric characteristics and practical fronthaul capacity limitations. To solve these practical problems, we introduce a resource allocation scheme using statistical channel state information (CSI) for uplink user-centric cell-free mmWave MIMO system. The hybrid beamforming (HBF) architecture is deployed at each access point (AP), while the central processing unit (CPU) only combines the received signals by the large-scale fading decoding (LSFD) method. We further frame the issue of maximizing sum-rate subject to the fronthaul capacity constraint and minimum rate constraint. Based on the alternating optimization (AO) and fractional programming method, we present an algorithm aimed at optimizing the users' transmit power for the power allocation (PA) subproblem. Then, an algorithm relying on the majorization-minimization (MM) method is given for the HBF subproblem, which jointly optimizes the HBF and the LSFD coefficients.
无蜂窝毫米波（mmWave）多输入多输出（MIMO）能够有效克服阴影衰落效应，并提供宏增益以提高通信网络的吞吐量。然而，大多数现有研究忽略了以用户为中心的特性和实际前传容量限制。为了解决这些实际问题，我们针对以用户为中心的上行链路无蜂窝毫米波MIMO系统，引入一种使用统计信道状态信息（CSI）的资源分配方案。在每个接入点（AP）部署混合波束成形（HBF）架构，而中央处理单元（CPU）仅通过大规模衰落解码（LSFD）方法对接收信号进行合并。我们进一步将在满足前传容量约束和最小速率约束的条件下最大化和速率的问题进行了公式化。基于交替优化（AO）和分式规划方法，我们针对功率分配（PA）子问题提出一种旨在优化用户发射功率的算法。然后，针对HBF子问题给出一种基于主元最小化（MM）方法的算法，该算法联合优化HBF和LSFD系数。
