Analytically Characterized Optimal Power Control for Signal-Level-Integrated Sensing, Computing and Communication in Federated Learning

2026-04-29

AI Summary

This work addresses the tension between object detection performance and aggregation distortion in over-the-air federated learning by proposing an integrated signal-level design that jointly optimizes sensing, computation, and communication. Specifically, it co-designs device transmit power and receiver scaling factors to minimize model aggregation error while preserving detection accuracy. The key innovation lies in the first-ever deep integration of sensing, computation, and communication at the signal level, coupled with a variable transformation that equivalently recasts the original non-convex problem into a convex one. Leveraging analytical optimality conditions, the authors derive an efficient polynomial-time algorithm with theoretical guarantees on optimality and robustness. Simulations demonstrate that the proposed method significantly outperforms existing baselines in both aggregation accuracy and overall federated learning performance.

Abstract

In the Internet-of-Things (IoT) era, efficient functionality integration is essential to address the growing demands of communication, computation, and sensing. Signal-level integrated sensing, computing, and communication (Sig-ISCC) is envisioned, where a single waveform simultaneously supports sensing, computing and communication via over-the-air computation (AirComp). Meanwhile, federated learning (FL) is widely regarded as a promising distributed machine learning framework that enables network intelligence in a privacy-preserving and secure manner, and exhibits strong synergy with AirComp, which alleviates the communication bottleneck of FL. In this paper, we study uplink Sig-ISCC design for AirComp-FL with joint target detection. We formulate the joint power and receive-scaling control problem, where edge devices' transmitted signals should serve both sensing and AirComp purposes. The goal is to minimize the AirComp aggregation distortion subject to a joint target-detection requirement. Although the resulting problem is non-convex in the original variables, we show that it admits an equivalent convex reformulation after a suitable variable transformation. By exploiting analytical optimality properties, we develop a robust, optimal, and polynomial-time-complexity algorithm that efficiently achieves the optimal transmit powers and receive scaling factor. Simulation results validate the optimality and numerical robustness of the proposed algorithm and show its superior FL performance compared to baseline methods.

Problem

• Signal-level Integrated Sensing, Computing and Communication
• Federated Learning
• Over-the-air Computation
• Power Control
• Target Detection

Innovation

• Signal-level Integrated Sensing Computing and Communication (Sig-ISCC)
• Over-the-Air Computation (AirComp)
• Federated Learning
• Optimal Power Control
• Convex Reformulation