Anastasios Papazafeiropoulos

Anastasios Papazafeiropoulos

Fluid antenna multiple access (FAMA) enables each user to rapidly switch among several closely spaced ports and select the strongest received signal. Although this mechanism offers micro-scale spatial diversity, its behavior in multiuser downlink systems with spatial correlation and linear precoding is not well understood. This paper develops a unified analytical framework for the multiple-input single-output (MISO) downlink with FAMA users served via maximum ratio transmission (MRT) or zero-forcing (ZF). We show that the per-port signal-to-interference ratio (SIR) follows a Beta-prime distribution with parameters \((M_{\mathrm{eff}},L)\), where \(M_{\mathrm{eff}}=M\) under MRT and \(M_{\mathrm{eff}}=M-U+1\) under ZF, and derive closed-form finite-sum cumulative distribution functions (CDFs) for both cases. We further provide the first analytical characterization of cross-port SIR correlation. \textcolor{black}{Furthermore, we derive rigorous outage probability bounds that tightly bracket the exact performance and become exact in the limiting cases of fully correlated and independent ports.} Asymptotic analyses reveal the fundamental diversity orders and tail behavior for each precoder. Numerical results confirm the accuracy of the SIR distributions, correlation model, and outage bounds, and show that MRT achieves weaker port correlation and larger selection gains than ZF when the base station (BS) has ample spatial degrees of freedom. The framework offers explicit guidelines for port configuration and precoder selection in practical FAMA systems.

Anastasios Papazafeiropoulos

Stacked intelligent metasurfaces (SIMs) and fluid antenna systems (FAS) are emerging technologies for wave-domain and spatial signal manipulation, respectively.This letter proposes a novel joint SIM-FAS communication model in which transmission and reception are performed by a SIM and an FAS, respectively. Using the block-diagonal matrix approximation (BDMA), a closed-form expression for the outage probability is derived, and the SIM phase shifts are optimized to minimize outage. Numerical results validate the analytical accuracy and demonstrate substantial performance gains over conventional benchmark schemes.

Ahmet M. Elbir, Anastasios Papazafeiropoulos

In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.