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.

翻译：在物联网（IoT）时代，高效的功能集成对于满足日益增长的通信、计算和感知需求至关重要。信号级集成感知、计算与通信（Sig-ISCC）构想通过空中计算（AirComp）技术，使单一波形能够同时支持感知、计算和通信功能。与此同时，联邦学习（FL）被广泛视为一种有前景的分布式机器学习框架，能够以保护隐私和安全的方式实现网络智能，并与缓解FL通信瓶颈的AirComp技术展现出强协同效应。本文针对联合目标检测场景下的AirComp-FL上行链路Sig-ISCC设计展开研究。我们提出了联合功率与接收缩放控制问题，其中边缘设备的发射信号需同时满足感知与AirComp需求。目标是在满足联合目标检测要求的条件下，最小化AirComp聚合失真。尽管原始变量下的优化问题具有非凸性，但通过适当的变量变换可证明其等价于凸优化形式。利用解析最优性特性，我们开发了一种鲁棒、最优且多项式时间复杂度的算法，可高效实现最优发射功率与接收缩放因子。仿真结果验证了所提算法的最优性与数值鲁棒性，并表明其在联邦学习性能上优于基线方法。