发表机构
Southern Illinois University（南伊利诺伊大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对CFmMIMO系统提出三混合预编码器设计，联合优化三层预编码器与AP发射功率，经仿真验证其在GEE、SE等性能上优于多种基准方案，还明确了AP与RF链数量的最优区域及动态超表面天线的增益效果。
AI 中文摘要
本文针对无小区大规模多输入多输出（CFmMIMO）系统，提出了一种三混合预编码器设计，该设计级联了数字、模拟和电磁处理三层结构。我们联合优化三层预编码器及接入点（AP）的发射功率，以在每个AP的功率和前传约束下最大化全局能量效率（GEE）。为解决该问题，我们开发了一种交替优化算法：第一个子例程采用加权最小均方误差、黎曼流形优化及洛伦兹相位上的梯度上升来最大化预编码器设计的总频谱效率（SE）；第二个子例程通过Dinkelbach分数规划和逐次凸近似优化功率分配。我们还分析了收敛性和计算复杂度。仿真结果表明，在20dBm发射功率下，所提设计相比全数字预编码器和经典混合预编码器，GEE分别提升87%和11%；此外，联合功率优化相比等功率基准，SE提升22%、GEE提升16%，且在SE上优于共址架构。最后，我们确定了AP和射频（RF）链数量的GEE最优区域，并证明在低功率区域中，增大动态超表面天线尺寸可提升GEE，且有源功率开销可忽略不计。
英文摘要
This paper proposes a tri-hybrid precoder design, cascading digital, analog, and electromagnetic processing layers, for cell-free massive multiple-input multiple-output (CFmMIMO) systems. We jointly optimize the three-layer precoders and access point (AP) transmit power to maximize global energy efficiency (GEE) under per-AP power and fronthaul constraints. To solve this, we develop an alternating optimization algorithm. The first sub-routine maximizes sum spectral efficiency (SE) for precoder design using weighted minimum mean square error, Riemannian manifold optimization, and gradient ascent over Lorentzian phases. The second optimizes power allocation via Dinkelbach's fractional programming and successive convex approximation. Convergence and computational complexity are also analyzed. Simulation results show the proposed design improves GEE by 87% and 11% over fully digital and classical hybrid precoders, respectively, at 20dBm transmit power. Moreover, joint power optimization yields 22% SE and 16% GEE gains over equal-power baselines, while outperforming colocated architectures in SE. Finally, we identify GEE-optimal regimes for AP and RF chain counts, and demonstrate that increasing dynamic metasurface antenna sizes enhances GEE in low-power regimes with negligible active power overhead.
