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

TL;DR

This work tackles fairness-aware computation offloading in wireless-powered MEC systems by introducing cooperative energy recycling (CER) that enables energy exchange among sensors. It formulates a joint energy-and-computation optimization under an $\alpha$-fairness objective, converts the inherently non-convex problem into a convex form via variable substitutions and MRC beamforming, and solves it with Lagrangian duality and alternating optimization. Closed-form solutions are derived for zero, common, and max-min fairness regimes, underpinning three algorithms (ZFBA, CFBA, MFBA) that balance throughput and fairness while incorporating energy causality and latency constraints. Simulations confirm CER’s throughput gains and fairness benefits across diverse scenarios, showing the tunable $\alpha$ parameter affords flexible control over performance-fairness trade-offs.

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.

Figures (11)

• Figure 1: A WPCN-assisted MEC system with energy recycling.
• Figure 2: The convergence of the proposed algorithms.
• Figure 3: The total computable data versus the fairness control parameter $\alpha$.
• Figure 4: The Jain's fairness index versus the fairness control parameter $\alpha$.
• Figure 5: The largest data gap versus the number of WSs.