Fluid antenna systems (FAS) have emerged as a promising technology to achieve high spatial diversity by dynamically reconfiguring multiple closely spacedNantenna ports. However, the inherent spatial correlation among these ports poses significant challenges for accurate performance analysis. Traditional block-correlation modeling algorithms, which partition theN×NToeplitz-structured correlation matrix into independentDblocks with constant correlation coefficients, often yield substantial approximation errors to block-correlation models, especially in scenarios with limited ports. In this paper, we revisit the spatial block-correlation model for FAS and introduce a novel block-correlation modeling algorithm in tuning the model parameters, which realizes the variable block-correlation model in practice. Our proposed approach derives closed-form expressions for the optimal block-specific correlation coefficients and develops a low-complexity heuristic algorithm that reduces the computational complexity from exponentialDN–Dto linear (N–D) ×Dsearches,thereby achieving significantly lower approximation error compared to constant correlation models. To validate the effectiveness of our variable block-correlation modeling algorithm, we first apply it to point-to-point FAS communications with closely spaced ports, deriving analytical expressions for the joint probability density function (PDF) of channel amplitudes and outage probability. Our analysis shows that the proposed algorithm offers tractable performance evaluation and superior accuracy, particularly when the number of ports is small (NThese results underscore the practical value of our approach for the design and optimization of next-generation FAS-based wireless networks.

Integrated sensing and communication (ISAC), which simultaneously achieves wireless sensing and communication, is promising for next-generation wireless networks. In this paper, we consider a joint state sensing and communication system, where a multi-antenna ISAC transceiver simultaneously senses a sensing target and conveys a message to a multi-antenna communication receiver in multiple-input-multiple-output (MIMO) real Gaussian channels. Our goal is to optimize the signal input probability distribution to obtain the approximate capacity-distortion region. The formulated optimization problem is difficult because of high optimization dimension and high storage complexity. To reduce the optimization dimension, it is theoretically proved that over each transmitting antenna, the optimal signal inputs over different symbol durations should follow an independent and identical distribution. To reduce the storage complexity, it is also theoretically proved that the signal input probability distribution optimization over MIMO channels is equivalent to that over multiple-input-single-output channels. We propose an alternating optimization based Blahut-Arimoto algorithm to solve the optimization problem. Numerical results illustrate that the proposed joint state sensing and communication system achieves the larger capacity-distortion region than both the basic time-sharing (TS) and improved TS schemes.

Future Internet-of-Things applications may require both high-quality wireless data aggregation and communication simultaneously. In this paper, we propose an integrated over-the-air computation (AirComp) and non-orthogonal multiple access (NOMA) communication scheme in a wireless uplink system, which consists of a data fusion center, a base station, and associated multiple users. Each user participates in AirComp and information transmission simultaneously. The data fusion center is responsible for wireless data aggregation and the base station is responsible for data collection of all users. Our aim is to minimize the computation mean square error (CMSE) at the data fusion center while satisfying the total transmission rate requirement at the base station. The formulated optimization problem is non-convex. To solve the problem, we propose a one-dimensional search based globally optimal solution, an alternating optimization based locally optimal solution, and a generalized Rayleigh quotient based globally optimal solution. Numerical results illustrate that although the locally optimal solution has the shortest average program execution time (PET), it achieves the suboptimal system performance. The generalized Rayleigh quotient based globally optimal solution, which addresses the CMSE minimization problem in a different manner, has much lower average PET than the one-dimensional search based globally optimal solution.