Energy-Efficient Hybrid Data Computation via Coordinated AirComp and Edge Offloading
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915932327116800 |
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| author | Jiang, Yudan Tang, Xiao Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu |
| author_facet | Jiang, Yudan Tang, Xiao Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10196 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy-Efficient Hybrid Data Computation via Coordinated AirComp and Edge Offloading Jiang, Yudan Tang, Xiao Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu Signal Processing Information Theory 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. |
| title | Energy-Efficient Hybrid Data Computation via Coordinated AirComp and Edge Offloading |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2604.10196 |