In this paper, cooperative energy recycling (CER) is investigated in wireless-powered mobile edge computing systems. Unlike conventional architectures that rely solely on a dedicated power source, wireless sensors are additionally enabled to recycle energy from peer transmissions. To evaluate system performance, a joint computation optimization problem is formulated that integrates local computing and computation offloading, under an alpha-fairness objective that balances total computable data and user fairness while satisfying energy, latency, and task size constraints. Due to the inherent non-convexity introduced by coupled resource variables and fairness regularization, a variable-substitution technique is employed to transform the problem into a convex structure, which is then efficiently solved using Lagrangian duality and alternating optimization. To characterize the fairness-efficiency tradeoff, closed-form solutions are derived for three representative regimes: zero fairness, common fairness, and max-min fairness, each offering distinct system-level insights. Numerical results validate the effectiveness of the proposed CER-enabled framework, demonstrating significant gains in throughput and adaptability over benchmark schemes. The tunable alpha fairness mechanism provides flexible control over performance-fairness trade-offs across diverse scenarios.

翻译：本文研究了无线供能移动边缘计算系统中的协同能量回收机制。与传统架构仅依赖专用电源不同，本系统进一步允许无线传感器从对等传输中回收能量。为评估系统性能，本文构建了一个联合计算优化问题，该问题融合了本地计算与计算卸载，并以α-公平性为目标，在满足能量、时延和任务规模约束的前提下，平衡可计算数据总量与用户公平性。由于耦合资源变量与公平性正则化项导致问题固有的非凸性，本文采用变量替换技术将问题转化为凸结构，进而利用拉格朗日对偶与交替优化方法高效求解。为刻画公平性与效率的权衡关系，推导了三种典型机制下的闭式解：零公平性、常规公平性与最大最小公平性，每种机制均揭示了独特的系统级特性。数值结果验证了所提协同能量回收框架的有效性，相较于基准方案，在吞吐量与适应性方面均展现出显著优势。可调节的α公平性机制为不同场景下的性能-公平性权衡提供了灵活调控手段。