标题： 空中FEEL与集成感知的联合调度和波束成形设计 显示英文标题

标题： Over-the-Air FEEL with Integrated Sensing: Joint Scheduling and Beamforming Design

摘要： 利用具有双传感和通信功能的无线系统在下一代无线网络中变得至关重要。 在本文中，我们提出了一种鲁棒设计用于空中联邦边缘学习（OTA-FEEL），该设计利用参数服务器（PS）的传感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Cramer-Rao界的新表达式以及估计全局模型的均方误差（MSE），分别用于衡量雷达传感和模型聚合的质量。 然后，我们开发了一个联合调度和波束成形框架，在保持传感和通信质量的同时优化OTA-FEEL性能，传感和通信质量分别根据Cramer-Rao界和可实现的下行链路速率来确定，并保持在期望范围内。 所得的调度问题简化为一个组合混合整数非线性规划问题（MINLP）。 我们开发了一种基于匹配追踪算法的低复杂度分层方法，该算法广泛用于压缩感知文献中的稀疏恢复。 所提出的算法使用一种逐步策略，在每次迭代中根据捕捉系统聚合和传感质量的指标省略效果最差的设备。 它进一步调用交替优化方案，通过在每次迭代中对它们进行边际优化来迭代更新下行链路波束成形和上行链路后处理。 给出了所提出算法的收敛性和复杂性分析。 在MNIST和CIFAR-10数据集上的数值评估证明了我们所提算法的有效性。 结果表明，通过利用精确的传感，可以有效抑制上行链路信号上的目标回波，确保模型聚合的质量不受干扰的影响。 显示英文摘要

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.