Fluid-antenna multiple access (FAMA) enables each user device to opportunistically select the port experiencing the most favorable instantaneous interference condition, thereby extracting micro-scale spatial diversity without relying on multiple radio frequency (RF) chains. However, existing FAMA studies assume purely stochastic interference fields and do not provide mechanisms for actively engineering the spatial fluctuations that FAMA relies upon. In parallel, reconfigurable intelligent surfaces (RISs) have been used almost exclusively for coherent signal enhancement, leaving the interference behavior largely unaltered. This paper introduces a fundamentally different integration of RISs with FAMA by using the RIS as a deep-fade engineering device that shapes the second-order statistics of the interference field rather than the desired signal. In near-field user-centric deployments, the RIS induces strong, controllable port-to-port interference variations that significantly increase the likelihood of deep fades at one or more fluid-antenna ports. We develop a full statistical characterization of the cascaded interferer-RIS-UE channel, show that the aggregated interference follows a generalized Gamma convolution law, and obtain a highly accurate moment-matched Gamma model whose parameters are explicitly RIS-controlled. Building on this model, we formulate a variance-maximization RIS design tailored to FAMA port selection and devise a computationally efficient projected gradient-ascent algorithm. We then derive the exact per-port SIR distribution, proving that it follows a Beta-prime law, and provide closed-form outage expressions for both independent and correlated ports. Extensive simulations validate all analytical results and demonstrate substantial performance gains. Compared with conventional FAMA and random-phase RIS configurations, the proposed RIS-enabled deep-fade engineering achieves multi-dB SIR improvements, and markedly lower outage probability.

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.

The integration of electromagnetic metasurfaces into wireless communications enables intelligent control of the propagation environment. Recently, flexible intelligent metasurfaces (FIMs) have evolved beyond conventional reconfigurable intelligent surfaces (RISs), enabling three-dimensional surface deformation for adaptive wave manipulation. However, most existing FIM-aided system designs assume perfect instantaneous channel state information (CSI), which is impractical in large-scale networks due to the high training overhead and complicated channel estimation. To overcome this limitation, we propose a robust statistical-CSI-based optimization framework for downlink multiple-input single-output (MISO) systems with FIM-assisted transmitters. A block coordinate ascent (BCA)-based iterative algorithm is developed to jointly optimize power allocation and FIM morphing, maximizing the average achievable sum rate. Simulation results show that the proposed statistical-CSI-driven FIM design significantly outperforms conventional rigid antenna arrays (RAAs), validating its effectiveness and practicality.

Flexible intelligent metasurface (FIM) is a recently developed, groundbreaking hardware technology with promising potential for 6G wireless systems. Unlike conventional rigid antenna array (RAA)-based transmitters, FIM-assisted transmitters can dynamically alter their physical surface through morphing, offering new degrees of freedom to enhance system performance. In this letter, we depart from prior works that rely on instantaneous channel state information (CSI) and instead address the problem of average sum spectral efficiency maximization under statistical CSI in a FIM-assisted downlink multiuser multiple-input single-output setting. To this end, we first derive the spatial correlation matrix for the FIM-aided transmitter and then propose an iterative FIM optimization algorithm based on the gradient projection method. Simulation results show that with statistical CSI, the FIM-aided system provides a significant performance gain over its RAA-based counterpart in scenarios with strong spatial channel correlation, whereas the gain diminishes when the channels are weakly correlated.

In this letter, we analyze the coverage performance of fluid antenna system (FAS)–equipped users in interference-limited heterogeneous cellular networks using stochastic geometry. In particular, we consider a downlink single-tier Poisson network where the user selects the antenna port that maximizes the instantaneous signal-to-interference ratio (SIR) from a set of spatially correlated ports within a finite aperture. A tractable framework is developed to characterize the coverage probability under arbitrary interference fading, with closed-form results for Rayleigh fading. The analysis shows that increasing the number of ports enhances coverage via spatial selection diversity, but the gains are fundamentally limited by spatial correlation and diminish beyond moderate port counts. Simulation results validate the analysis and highlight the joint impact of aperture size and port correlation on coverage performance.

We derive a novel closed-form lower bound on the ergodic capacity of holographic multiple-input multiple-output (HMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs) under Rayleigh fading conditions. The proposed expression is valid for systems with a finite number of antennas and SIM elements and exhibits tightness throughout the whole signal-to-noise ratio (SNR) range. Furthermore, we conduct a comprehensive low-SNR analysis, offering meaningful observations on how key system parameters influence the capacity performance.

Conventional multiple-input multiple-output (MIMO) systems rely on static antenna placement. To exploit additional spatial degrees of freedom, the fluid antenna (FA) concept has emerged as a promising solution for improving data rates and diversity performance. Most existing FA studies assume Rayleigh fading, whereas analytical characterization under Nakagami-m fading is more challenging. This article investigates the ergodic capacity of FA-assisted MIMO systems over Nakagami-m fading channels. By applying majorization theory, upper and lower bounds on the ergodic capacity are derived. High signal-to-noise ratio (SNR) approximations are then obtained to clarify the role of the fading parameter and the number of propagation paths. The large-system behavior is also studied, and Monte Carlo simulations are used to assess the tightness of the proposed bounds. The results show that the upper bound closely tracks the simulated capacity, while the lower bound remains useful mainly in the low-SNR regime.

Sixth-generation (6G) wireless systems target ubiquitous connectivity by integrating terrestrial (TNs) and non-terrestrial networks (NTN) cooperation, including low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs). In such a vertically stratified architecture characterized by massive multi connectivity, the selection of the most suitable tier or tier-combination for each mobile user is a complicated task, as the decision depends jointly on the propagation environment, user requirements, and the instantaneous characteristics of the topology. This paper proposes an altitude-aware Deep Learning (DL) framework that casts multi-tier network selection as a seven-class classification problem spanning standalone TN, LEO, HAPS, and UAV access as well as their TN-assisted multi-connectivity combinations. A fourteen-feature physics-based dataset is generated through extensive simulations based on standardized channel and geometry models, including 3GPP TR 38.901 terrestrial pathloss and line-of-sight probability, satellite-constellation elevation angles, and air-to-ground link geometry for HAPS and UAV. A deep neural network (DNN) is trained on the aforementioned dataset and assessed using stratified five-fold cross-validation. The proposed model achieves an overall classification accuracy of 88.1% with a macro-averaged F1-score of 0.88 and a single-sample inference latency of approximately 2 ms, well within typical 6G handover budgets. The results demonstrate that requirement- and geometry-aware tier selection can be performed accurately, enabling real-time decision-making in ambiguous multi-connectivity configurations for 6G networks.

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_eff,L), where M_eff=M under MRT and M_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. 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.

This work analyzes the ergodic capacity behavior of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems with a finite and arbitrary number of antennas and RIS elements under Nakagami-m fading conditions. By combining Hadamard’s determinant inequality with the Cauchy–Schwarz inequality, this work derives a dimensionally consistent closed-form upper bound on the ergodic capacity in terms of the Meijer G-function. Subsequently, it is demonstrated that at a high signal-to-noise ratio (SNR), a simplified expression for the capacity upper bound can be derived, enabling an analytical assessment of how the fading parameter influences the ergodic capacity. The study also explores the asymptotic behavior in the large-system regime, where the number of antennas or RIS elements tends to infinity. Monte Carlo (MC) simulations confirm the accuracy of the proposed bound and scaling laws.

Stacked intelligent metasurface (SIM) is a promising enabler for next-generation high-capacity networks that exhibit better performance compared to its single-layer counterpart by means of just wave propagation. However, the study of ergodic mutual information (EMI) and outage probability for SIM-assisted multiple-input-multiple-output (MIMO) systems is not available in the literature. To this end, we obtain the distribution of the MI by using large random matrix theory (RMT) tools. Next, we derive a tight closed-form expression for the outage probability based on statistical channel state information (CSI). Moreover, we apply the gradient descent method for the minimization of the outage probability. Simulation results verify the analytical results and provide fundamental insights such as the performance enhancements compared to conventional MIMO systems and the single-layer counterpart. Notably the proposed optimization algorithm is faster than the alternating optimization (AO) benchmark by saving significant overhead.

Although reconfigurable intelligent surface (RIS) is a promising technology for shaping the propagation environment, it consists of a single-layer structure within inherent limitations regarding the number of beam steering patterns. Based on the recently revolutionary technology, denoted as stacked intelligent metasurface (SIM), we propose its implementation not only on the base station (BS) side in a massive multiple-input multiple-output (mMIMO) setup but also in the intermediate space between the base station and the users to adjust the environment further as needed. For the sake of convenience, we call the former BS SIM (BSIM), and the latter channel SIM (CSIM). Hence, we achieve wave-based combining at the BS and wave-based configuration at the intermediate space. Specifically, we propose a channel estimation method with reduced overhead, being crucial for SIMassisted communications. Next, we derive the uplink sum spectral efficiency (SE) in closed form in terms of statistical channel state information (CSI). Notably, we optimize the phase shifts of both BSIM and CSIM simultaneously by using the projected gradient ascent method (PGAM). Compared to previous works on SIMs, we study the uplink transmission, a mMIMO setup, channel estimation in a single phase, a second SIM at the intermediate space, and simultaneous optimization of the two SIMs. Simulation results show the impact of various parameters on the sum SE, and demonstrate the superiority of our optimization approach compared to the alternating optimization (AO) method.

Low-cost reconfigurable intelligent surfaces (RISs) are being considered as promising physical-layer technology for next-generation wireless networks due to their ability to re-engineer the propagation environment by tuning their elements. Although RIS-aided systems have many advantages, one of their major bottlenecks is that they can provide coverage only in front of the surface. Fortunately, simultaneous transmitting and reflecting surface (STARS)-assisted systems have emerged to fill this gap by providing 360 degrees wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system's spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while potentially doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as projected gradient ascent method (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled counterparts, and also to show the effect of different parameters of interest on the system performance.

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol.

Stacked intelligent metasurfaces (SIM) transceiver design enables precoding in the wave domain while enjoying reduced energy consumption and hardware cost. On this ground and contrary to previous works studying various achievable rates, we derive the ergodic mutual information (EMI) at the large system region in closed form for SIM-assisted multiple-input-multiple-output (MIMO) systems based on statistical channel state information (CSI). Next, by applying a gradient ascent algorithm, we maximize the EMI with respect to the phase shifts of the two SIMs of the transceiver simultaneously, which saves significant overhead compared to a traditional alternating optimization (AO) approach. Simulations shed light in the performance of the proposed system at the large system limit and provide a comparison under different CSI conditions.

This work proposes a rate-splitting (RS) strategy for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (mMIMO) systems to reduce the interference among multiple users and enhance the spectral efficiency (SE) while improving the coverage degraded by blockages. Specifically, we use the RS to design the precoder for the common part by solving the asymptotic problem. Also, unlike traditional RIS-aided systems, receivers can be positioned on either side of the RIS panel in the proposed system. We derive the sum-rate based on statistical channel state information (CSI) to reduce the signal overhead. Next, we optimize the rate through a projected gradient ascent method algorithm simultaneously with respect to the amplitudes and phase shifts of the STAR-RIS. Simulations show the advantages of the RS strategy compared with the broadcasting strategy in improving the sum-rate. We further evaluate the efficiency of the STAR-RIS system against the traditional RIS-aided system. In our analysis, we employ energy splitting and mode switching protocols to fine-tune the transmission and reflection coefficients of the outgoing and incoming signals.

Reconfigurable intelligent surfaces (RIS)-assisted massive multiple-input multiple-output (mMIMO) is a promising technology for applications in next-generation networks. However, reflecting-only RIS provides limited coverage compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Hence, in this paper, we focus on the downlink achievable rate and its optimization of a STAR-RIS-assisted mMIMO system. Contrary to previous works on STAR-RIS, we consider mMIMO, correlated fading, and multiple user equipments (UEs) at both sides of the RIS. In particular, we introduce an estimation approach of the aggregated channel with the main benefit of reduced overhead links instead of estimating the individual channels. Next, leveraging channel hardening in mMIMO and the use-and-forget bounding technique, we obtain an achievable rate in closed-form that only depends on statistical channel state information (CSI). To optimize the amplitudes and phase shifts of the STAR-RIS, we employ a projected gradient ascent method (PGAM) that simultaneously adjusts the amplitudes and phase shifts for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols. By considering large-scale fading, the proposed optimization can be performed every several coherence intervals, which can significantly reduce overhead. Considering that STAR-RIS has twice the number of controllable parameters compared to conventional reflecting-only RIS, this accomplishment offers substantial practical benefits. Simulations are carried out to verify the analytical results, reveal the interplay of the achievable rate with fundamental parameters, and show the superiority of STAR-RIS regarding its achievable rate compared to its reflecting-only counterpart.

In this work, we investigate the effect of phase noise in downlink simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided massive multiple-input multiple-output (mMIMO) systems, where users are located on both sides of the STAR-RIS panel. We assume that users only know the knowledge of statistical channel state information (CSI), and account for correlated Rayleigh fading to meet realistic conditions. The use-and-then-forget (UatF) bound of the sum-rate has been derived by assuming maximum ratio transmission (MRT) precoding. Notably, a projected gradient ascent method (PGAM) algorithm has been used to optimize the amplitudes and phase shifts of the STAR-RIS panel simultaneously. Our simulations show that the sum-rate can be improved as the noise concentration parameter of the Von Mises (VM) distribution increases.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution to improve wireless communication performance through just passive reflection. Recently, the concept of simultaneously transmitting and reflecting RIS (STAR-RIS) has appeared but the study of minimum signal-to-interference-plus-noise ratio (SINR) and the impact of hardware impairments (HWIs) remain open. In addition to previous works on STAR-RIS, we consider a massive multiple-input multiple-output (mMIMO) base station (BS) serving multiple user equipments (UEs) at both sides of the RIS. Specifically, in this work, focusing on the downlink of a single cell, we derive the minimum SINR obtained by the optimal linear precoder (OLP) with HWIs in closed form. The OLP maximises the minimum SINR subject to a given power constraint for any given passive beamforming matrix (PBM). Next, we obtain deterministic equivalents (DEs) for the OLP and the minimum SINR, which are then used to optimise the PBM. Notably, based on the DEs and statistical channel state information (CSI), we optimise simultaneously the amplitude and phase shift by using a projected gradient ascent algorithm (PGAM) for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols with reduced feedback, which is quite crucial for STAR-RIS systems that include the double number or variables compared to reflecting only RIS. Simulations verify the analytical results, shed light on the impact of HWIs, and demonstrate the better performance of STAR-RIS compared to conventional RIS. Also, a benchmark full instantaneous CSI (I-CSI) based design is provided and shown to result in higher SINR but lower net achievable sum-rate than the statistical CSI based design because of large overhead associated with the acquisition of full I-CSI acquisition. Thus, not only do we evaluate the impact of HWIs but we also propose a statistical CSI based design that provides higher net sum-rate with low overhead and complexity.

Stacked intelligent metasurfaces (SIMs) have recently gained significant interest since they enable precoding in the wave domain that comes with increased processing capability and reduced energy consumption. The study of SIMs and high frequency propagation make the study of the performance in the near field of crucial importance. Hence, in this work, we focus on SIM-assisted multiuser multiple-input multiple-output (MIMO) systems operating in the near field region. To this end, we formulate the weighted sum rate maximisation problem in terms of the transmit power and the phase shifts of the SIM. By applying a block coordinate descent (BCD)-relied algorithm, numerical results show the enhanced performance of the SIM in the near field with respect to the far field.