论文标题
RIS AIDED MULTIUSER MIMO上行链路传输的能源效率和光谱效率折衷
Energy Efficiency and Spectral Efficiency Tradeoff in RIS-Aided Multiuser MIMO Uplink Transmission
论文作者
论文摘要
可重新配置的智能表面(RISS)的出现使我们能够通过采用低成本的被动反射单元来建立可编程无线通信的可编程无线通信。这项工作研究了配备有离散相位变速器的RIS的多源多输入多输出(MIMO)上行链路通信中能源效率(EE)和光谱效率(SE)之间的非平凡权衡。为了减少所需的信号开销和能源消耗,我们的传输策略设计基于部分通道状态信息(CSI),包括RIS和用户终端(UTS)之间的统计CSI以及RIS与基站之间的瞬时CSI。为了调查EE-SE权衡,我们开发了一个框架,以优化UTS的传输预编码和RIS反射光束的联合优化,以最大程度地提高称为资源效率(RE)的性能指标。为了设计UT的预编码,它可以通过UTS最佳传输方向的封闭形式解决方案的帮助来简化UTS发射功率的设计。为了避免计算预期涉及的嵌套积分的高复杂性,我们得出了渐近的确定性目标表达。对于RIS阶段的设计,通过资本化同型,加速的投影梯度和大型化最小化方法提出了迭代均方体误差最小化方法。数值结果说明了我们提出的优化框架的有效性和快速收敛速率。
The emergence of reconfigurable intelligent surfaces (RISs) enables us to establish programmable radio wave propagation that caters for wireless communications, via employing low-cost passive reflecting units. This work studies the non-trivial tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a RIS equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our transmission strategy design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a performance metric called resource efficiency (RE). For the design of UT's precoding, it is simplified into the design of UTs' transmit powers with the aid of the closed-form solutions of UTs' optimal transmit directions. To avoid the high complexity in computing the nested integrals involved in the expectations, we derive an asymptotic deterministic objective expression. For the design of the RIS phases, an iterative mean-square error minimization approach is proposed via capitalizing on the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness and rapid convergence rate of our proposed optimization framework.