Energy-Efficient Hybrid Data Computation via Coordinated AirComp and Edge Offloading

2026-04-11

AI Summary

This work addresses the challenges of resource contention, interference, and high energy consumption arising from the coexistence of over-the-air computation (AirComp) and edge offloading in 6G networks. To this end, it proposes a coordinated hybrid computing architecture that deeply integrates AirComp with edge offloading for the first time. A joint energy-efficiency-driven optimization framework is developed to simultaneously optimize user association, power control, and transceiver scaling, minimizing total system energy consumption while satisfying constraints on offloading capacity and aggregation accuracy. The resulting multidimensional problem is efficiently solved via block coordinate descent. Simulation results demonstrate that the proposed approach significantly outperforms baseline strategies, achieving highly efficient and sustainable hybrid data computation.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The development of 6G networks brings an increasing variety of data services, which motivates the hybrid computation paradigm that coordinates the over-the-air computation (AirComp) and edge computing for diverse and effective data processing. In this paper, we address this emerging issue of hybrid data computation from an energy-efficiency perspective, where the coexistence of both types induces resource competition and interference, and thus complicates the network management. Accordingly, we formulate the problem to minimize the overall energy consumption including the data transmission and computation, subject to the offloading capacity and aggregation accuracy. We then propose a block coordinate descent framework that decomposes and solves the subproblems including the user scheduling, power control, and transceiver scaling, which are then iterated towards a coordinated hybrid computation solution. Simulation results confirm that our coordinated approach achieves significant energy savings compared to baseline strategies, demonstrating its effectiveness in creating a well-coordinated and sustainable hybrid computing environment.

Problem

• Energy Efficiency
• Hybrid Computation
• AirComp
• Edge Offloading
• Resource Competition

Innovation

• AirComp
• edge offloading
• energy efficiency
• hybrid computation
• block coordinate descent