Accelerating Distributed Optimization via Over-the-Air Computing

نویسندگان

چکیده

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) promising medium access technology, which exploits superposition property of wireless multiple channel (MAC) offers significant bandwidth savings. In work, we propose an AirComp framework for general distributed convex problems. Specifically, primal-dual (DPD) subgradient method utilized procedure. Under assumptions, prove that DPD-AirComp can asymptotically achieve zero expected constraint violation. Therefore, ensures feasibility original problem, despite presence fading additive noise. Moreover, with proper power control users’ signals, non-zero optimality gap also be mitigated. Two practical applications proposed are presented, namely, management allocation. Finally, numerical results confirm DPD-AirComp’s excellent performance, while it shown converges order magnitude faster compared to two digital orthogonal schemes, specifically, time-division (TDMA), frequency-division (OFDMA).

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ژورنال

عنوان ژورنال: IEEE Transactions on Communications

سال: 2023

ISSN: ['1558-0857', '0090-6778']

DOI: https://doi.org/10.1109/tcomm.2023.3286915