Fluid Antenna Systems Empowered Integrated Communication and Over-the-Air Computation

Over-the-air computation (AirComp) enables swift wireless data aggregation by leveraging the superposition property of multiple-access channels (MAC), making it essential for the seamless integration of communication and computing in future networks. Meanwhile, fluid antenna systems (FAS) offer dynamic spatial degrees of freedom (DoF) by reconfiguring antenna positions, thus enhancing adaptability under varying channel conditions. This paper investigates the integration of FAS into a communication and AirComp coexistence framework. We aim to jointly optimize the transceiver beamforming vectors and the antenna positioning vector (APV) to minimize the computation distortion while ensuring reliable cellular communication performance. To tackle this highly non-convex problem, we develop an efficient joint learning-optimization framework. Specifically, we propose a neural network (NN) framework with a dedicated surrogate loss function design to infer optimal APV based on multi-path channel conditions, while an alternating optimization (AO) method is developed to find a locally optimal solution of transceivers by iteratively optimizing each variables with the others being fixed. Besides, to provide analytical tractability and benchmark insight, the APV design problem is relaxed and transformed into a tractable quadratically constrained quadratic program (QCQP) by successive convex approximation (SCA) as a special case under line-of-sight (LoS) channels, which reveals the performance bounds and convergence properties of the system. Numerical results show that proposed method significantly improves the system performance compared with traditional fixed-position antenna (FPA) as well as various benchmark schemes with remarkable generalization capabilities across diverse channel conditions.