AI 中文总结
针对无蜂窝大规模MIMO下行链路能效最大化问题，提出深度展开加速投影梯度框架，在保持能效性能的同时大幅降低计算成本，浮点运算量最多减少30倍。
AI 中文摘要
本文研究在服务质量（QoS）和每个接入点功率约束下，无蜂窝大规模多输入多输出（cell-free massive MIMO）系统下行链路的能效（EE）最大化问题。我们首先推导目标函数关于功率分配系数的闭式梯度表达式，随后提出一种加速投影梯度（APG）方法求解该问题。为降低APG的计算复杂度与运行时间，我们提出深度展开APG框架，将迭代APG更新映射至有限数量的神经网络层，其中步长、惩罚系数等参数通过数据学习得到。该方法通过固定数量的基于梯度的更新生成功率分配解，无需线搜索或手动参数调优。数值结果表明，所提方法的EE性能可与迭代APG方法相当，同时计算成本显著降低，在考虑的系统设置下浮点运算量最多可减少30倍。
英文摘要
This paper investigates energy efficiency (EE) maximization for the downlink of cell-free massive multiple-input multiple-output systems under quality-of-service and per-access point power constraints. We first derive closed-form gradient expressions of the objective function with respect to the power allocation coefficients, and then propose an accelerated projected gradient (APG) approach to solve this problem. To reduce the computational complexity and runtime of APG, we propose a deep-unfolded APG framework that maps iterative APG updates onto a finite number of neural network layers, where parameters such as step sizes and penalty coefficients are learned from data. The proposed approach produces power allocation solutions through a fixed number of gradient-based updates without the need for line search or manual parameter tuning. Numerical results show that the method achieves EE performance comparable to the iterative APG approach while requiring significantly lower computational cost, with up to a 30-fold reduction in floating-point operations under the considered system settings.
