AI 中文总结
本文针对采用无线前传的无蜂窝大规模MIMO上行链路，构建能效最大化框架，联合优化资源拆分、自适应量化分辨率与前传功率，通过交替优化算法求解，实现节能目标。
AI 中文摘要
采用无线前传的无蜂窝大规模MIMO是6G网络中实现节能的有前景架构，但接入链路与前传链路需共享同一稀缺频谱，且在全集中式（Option-8）功能拆分下，前传速率由接入点（AP）采用的有限量化分辨率决定。本文针对该系统的上行链路构建网络能效（EE）最大化框架，联合优化集成接入与前传（IAF）的资源拆分、每个AP的自适应量化分辨率及前传功率，并统一处理时分（TD）与频分（FD）工作模式。当AP激活价值不高时，可将其关闭（休眠），因此分辨率分配与AP选择存在内在耦合。所得混合整数非凸分数规划问题通过交替优化算法求解，该算法具有每块最优性保证，可直接应用于两种模式，得到闭式最优时间拆分、带宽二分法及每个AP的最优比特选择。该设计基于易处理的加性量化噪声模型，报告的性能通过实际Lloyd-Max量化器及基于Bussgang分解的可达速率界端到端获得。
英文摘要
Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees---a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection---that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd--Max quantizers and a Bussgang decomposition-based achievable-rate bound.
Comments13 pages, 5 figures, submitted to an IEEE journal
