Fairness-Aware Computation Offloading in Wireless-Powered MEC Systems with Cooperative Energy Recycling

2025-11-04

AI Summary

In wireless-powered mobile edge computing (MEC) systems, simultaneously achieving energy efficiency, latency sensitivity, and user fairness remains challenging due to stringent energy constraints and heterogeneous task requirements. Method: This paper proposes a Collaborative Energy Recycling (CER) mechanism that reuses peer-to-peer signal energy, jointly optimizing local computation and task offloading. An α-fairness-tunable optimization framework is formulated to jointly maximize total computable data and user fairness under energy, latency, and task-size constraints. A non-convex problem is transformed into a convex one via variable substitution, and an efficient algorithm is developed using Lagrangian duality and alternating optimization. Closed-form solutions are derived for three canonical fairness regimes: throughput maximization, proportional fairness, and max-min fairness. Results: Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods in both computational throughput and adaptability to diverse fairness objectives.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

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.

Problem

• Optimizing computation offloading in wireless-powered MEC systems with energy recycling
• Balancing total computable data and user fairness under resource constraints
• Solving non-convex optimization with coupled resource variables and fairness regularization

Innovation

• Cooperative energy recycling from peer transmissions
• Variable substitution transforms non-convex problem
• Lagrangian duality with alternating optimization method