Optimized Power Control for Over-the-Air Computation in Fading Channels
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Abstract (arXiv)
In this paper, we study the power control problem for Over-the-air computation (AirComp) over fading channels. Our objective is to minimize the computation error by jointly optimizing the transmit power at the power-constrained devices and a signal scaling factor (called denoising factor) at the fusion center (FC). The problem is generally non-convex due to the coupling of the transmit power over devices and denoising factor at the FC. To tackle the challenge, we first consider the special case with static channels, for which we derive the optimal solution in closed form. The optimal power control exhibits a threshold-based structure. Specifically, for each device, if the product of the channel quality and power budget, called quality indicator, exceeds an optimized threshold, this device applies channel-inversion power control; otherwise, it performs full power transmission. Building on the results, we proceed to consider the general case with time-varying channels. To solve the more challenging non-convex power control problem, we use the Lagrange-duality method via exploiting its "time-sharing" property. The derived optimal power control exhibits a regularized channel inversion structure, where the regularization has the function of balancing the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case with only one device being power limited, we show that the optimal power control for the power-limited device has an interesting channel-inversion water-filling structure, while those for other devices (with sufficient power budgets) reduce to channel-inversion power control over all fading states.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2019.
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Citing papers per year: 2021: 6; 2022: 0; 2023: 1.
7 papers.
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
- 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Aircited by 11
- 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learningcited by 10
- 2021Relay-Assisted Cooperative Federated Learningcited by 9
- 2021User Scheduling for Federated Learning Through Over-the-Air Computationcited by 7
- 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackerscited by 6
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
- 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequences14 Jun 2023
- 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers18 Oct 2021
- 2021User Scheduling for Federated Learning Through Over-the-Air Computation5 Aug 2021
- 2021Relay-Assisted Cooperative Federated Learning20 Jul 2021
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challenges5 Apr 2021
- 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Air30 Mar 2021
- 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning3 Mar 2021