集成感知的空联邦边缘学习（OTA-FEEL）：联合调度与波束成形设计
Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design
AI总结：
针对下一代无线网络中目标回波干扰OTA-FEEL模型聚合的问题，提出联合调度与波束成形框架，结合感知能力优化学习性能，经实验验证可有效抑制干扰、保障聚合质量。
摘要：
在下一代无线网络中，采用兼具感知与通信双重功能的无线系统正变得至关重要。本文提出了一种鲁棒的空联邦边缘学习（OTA-FEEL）设计，利用参数服务器（PS）的感知能力来减轻目标回波对模拟模型聚合的影响。我们首先推导了目标响应的克拉美罗界（Cramer-Rao bound）和估计全局模型的均方误差（MSE）的新表达式，分别用于衡量雷达感知质量和模型聚合质量。随后，我们构建了一个联合调度与波束成形框架，在将感知质量（由克拉美罗界衡量）和通信质量（由下行可达速率衡量）维持在期望范围的同时，优化OTA-FEEL的性能。由此产生的调度问题可转化为一个组合混合整数非线性规划问题（MINLP）。我们基于压缩感知领域中广泛用于稀疏恢复的匹配追踪算法，开发了一种低复杂度的分层方法。该算法采用逐步策略，在每次迭代中根据同时反映系统聚合质量和感知质量的指标，剔除效果最差的设备。此外，它还采用交替优化方案，通过在每次迭代中对下行波束成形和上行后处理进行边际优化，迭代更新二者。本文对所提算法的收敛性和复杂度进行了分析。在MNIST和CIFAR-10数据集上的数值评估验证了所提算法的有效性。结果表明，通过利用精确的感知，上行信号中的目标回波可得到有效抑制，确保模型聚合质量在存在干扰的情况下仍保持完好。
英文摘要：
Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramer-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramer-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
