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Revisiting Wireless-Powered MEC: A Cooperative Energy Recycling Framework for Task-Energy Co-Design

Haohao Qin, Bowen Gu, Xianhua Yu, Hao Xie, Yongjun Xu, Qihao Li, Liejun Wang

TL;DR

This work tackles fair, energy-constrained MEC in wireless-powered networks by introducing a cooperative energy recycling (CER) framework that couples energy sharing with joint local computing and offloading. By reformulating the nonconvex objective into a convex problem via relaxation, maximum ratio combining, and variable substitution, and solving with an alternating optimization using Lagrangian duality, the authors obtain closed-form solutions that illuminate the resource allocation dynamics. The key contributions include a max–min fairness objective, a MEC-CER integration strategy, and analytical and simulation results showing substantial improvements in total computable data and user fairness, especially in dense networks or under limited power. The findings highlight CER as a practical mechanism to balance energy availability and computation demand, enhancing sustainability and responsiveness of future MEC-enabled IoT systems.

Abstract

Cooperative energy recycling (CER) offers a new way to boost energy utilization in wireless-powered multi-access edge computing (MEC) networks, yet its integration with computation-communication co-design remains underexplored. This paper proposes a CER-enabled MEC framework that maximizes the minimum computable data among users under energy causality, latency, and power constraints. The intractable problem is reformulated into a convex form through relaxation, maximum ratio combining, and variable substitution, and closed-form solutions are derived via Lagrangian duality and alternating optimization, offering analytical insights. Simulation results verify that the proposed CER mechanism markedly increases total computable data while maintaining equitable performance across heterogeneous users.

Revisiting Wireless-Powered MEC: A Cooperative Energy Recycling Framework for Task-Energy Co-Design

TL;DR

This work tackles fair, energy-constrained MEC in wireless-powered networks by introducing a cooperative energy recycling (CER) framework that couples energy sharing with joint local computing and offloading. By reformulating the nonconvex objective into a convex problem via relaxation, maximum ratio combining, and variable substitution, and solving with an alternating optimization using Lagrangian duality, the authors obtain closed-form solutions that illuminate the resource allocation dynamics. The key contributions include a max–min fairness objective, a MEC-CER integration strategy, and analytical and simulation results showing substantial improvements in total computable data and user fairness, especially in dense networks or under limited power. The findings highlight CER as a practical mechanism to balance energy availability and computation demand, enhancing sustainability and responsiveness of future MEC-enabled IoT systems.

Abstract

Cooperative energy recycling (CER) offers a new way to boost energy utilization in wireless-powered multi-access edge computing (MEC) networks, yet its integration with computation-communication co-design remains underexplored. This paper proposes a CER-enabled MEC framework that maximizes the minimum computable data among users under energy causality, latency, and power constraints. The intractable problem is reformulated into a convex form through relaxation, maximum ratio combining, and variable substitution, and closed-form solutions are derived via Lagrangian duality and alternating optimization, offering analytical insights. Simulation results verify that the proposed CER mechanism markedly increases total computable data while maintaining equitable performance across heterogeneous users.

Paper Structure

This paper contains 14 sections, 24 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. System Structure and Transmission Mechanism
  4. Energy Recycling Mechanism
  5. Task Execution Mechanism
  6. Local Computing
  7. Computation Offloading
  8. Problem Formulation and Algorithm Design
  9. Problem Formulation
  10. Problem Transformation
  11. Algorithm Design
  12. Offloading capacity enhancement with CER
  13. Simulation Results
  14. Conclusions

Figures (2)

  • Figure 1: A WPCN-assisted MEC system with energy recycling.
  • Figure 2: Performance evaluation of the proposed algorithm: (a) total computable data under different $P_{\max}$; (b) total computable data under different number of WSs $K$; (c) total computable data under different number of receive antennas $N$; (d) maximum and minimum computable data versus $K$.