RSMA-Enabled ISAC Networks with Fluid Antenna Systems: Stochastic Geometry Analysis and Low-Complexity Resource Allocation
In this paper, we investigate the downlink performance of multi-cell RSMA-enabled ISAC networks in which base stations (BSs), communication users, and sensing targets are spatially distributed according to independent Poisson point processes (PPPs). Each BS simultaneously serves multiple users using RSMA while exploiting the common stream as a dual-functional communication and sensing waveform. The users are equipped with FAS that selects the best antenna port to maximize the received signal quality. Closed-form analytical expressions are derived for the ergodic sum-rates by combining stochastic geometry, order statistics, and Laplace-transform-based interference analysis. Furthermore, a tractable approximation for the average radar SINR is developed by characterizing the statistical properties of the common precoder. Leveraging the derived analytical expressions, a low-complexity analytical resource allocation framework is proposed to jointly optimize the RSMA power allocation, the communication-sensing beam tradeoff, and the number of scheduled users while sat- isfying the sensing quality-of-service constraint. Compared with conventional iterative optimization approaches, the proposed analytical design significantly reduces computational complexity while achieving nearly identical communication performance. Simulation results verify the accuracy of the developed analytical expressions and demonstrate substantial improvements in both RSMA sum-rate and sensing performance over conventional transmission schemes.