Xiaoyan Ma

Mehdi Karbalayghareh, Abhishek Rajasekaran, Xiaoyan Ma et al.

Integrated sensing and communication (ISAC) and AI-and-communication (AIAC) are identified as separate usage scenarios in the ITU IMT-2030 vision for sixth-generation (6G) networks. In practice, however, these two directions are already beginning to merge. ISAC gives the network a way to observe the physical world, while AI gives the network a way to learn from those observations and act on them. This article introduces AI-integrated sensing and communication (AISAC) as a closed-loop framework for this merger. In AISAC, AI is not only a tool used to optimize an ISAC system. ISAC is also the physical substrate through which AI receives data, context, and connectivity. The key technical message is that AISAC requires a new physical-layer design principle, in which the ISAC waveform, beam, power, bandwidth, and sensing mode should be configured for learning alignment, not for sensing distortion or communication rate alone. In particular, the sensing configuration that is most accurate from a classical estimation viewpoint need not be the one that is most useful for training or inference. We present the AISAC landscape, explain why imperfect sensing changes the learning problem, develop the closed-loop architecture and its three-way sensing-communication-learning tension, and outline a vehicular edge-intelligence use case together with open problems for theory, implementation, and standardization.

Xiaoyan Ma, Shahryar Zehtabi, Yinan Zou et al.

Over-the-air (OTA) computation has recently gained significant attentions as an effective approach to enhance the communication efficiency of wireless federated learning (FL). By enabling simultaneous transmission and aggregation of local model updates, OTA-FL can substantially reduce both latency and bandwidth consumption. However, a key challenge lies in the imperfect aggregation of global models caused by channel state information (CSI) uncertainty, which introduces distortion to the final learning performance. To address this issue, we study the long-term mean squared error (MSE) minimization problem for OTA-FL under imperfect CSI conditions. Through convergence analysis, we establish an upper bound for the time-averaged MSE, thereby revealing the effect of aggregation errors accumulated throughout multiple communication rounds on the overall training performances. Based on this analysis, an optimization framework is developed to minimize the long-term MSE via the joint design of (i) transmit coefficients at the local devices and (ii) receive combining vectors at the parameter server (PS). Since this alternating optimization approach requires non-causal CSI, a Lyapunov-based optimization method is further introduced to handle causal CSI scenarios. By incorporating virtual queues to characterize long-term energy consumption, the proposed method effectively decouples temporal dependencies and allows transmit coefficients to be optimized based on the causal CSI of each aggregation round. Comprehensive evaluations on Fashion-MNIST, CIFAR-10 and CIFAR-100 datasets have demonstrated that the proposed algorithms can significantly reduce the degradation of test accuracy caused by imperfect CSI. Comparisons with other benchmark schemes further verify the superiority of our proposed algorithms.