As terrestrial networks face limitations in delivering global wireless communications, the shift towards non-terrestrial networks, particularly satellite-based systems, becomes increasingly essential for connecting remote regions. However, these networks are susceptible to security risks, such as eavesdropping, making the assurance of physical layer security (PLS) critical to achieving secure global wireless connectivity. Emerging technologies like reconfigurable intelligent surfaces (RISs) and higher frequency bands, such as the THz range, offer promising solutions for enhancing the PLS. This paper investigates the secrecy performance of a RIS-assisted satellite communication system operating in the THz band. We derive expressions for key security metrics, including the secrecy outage probability (SOP), lower bound SOP, and intercept probability (IP). Furthermore, in a practical scenario, we analyze the effect of misalignment and solar scintillation on reliable satellite communication. Simulation results validate the analytical findings, highlighting the importance of utilizing RIS in improving secrecy performance.

User activity detection (UAD) is a critical procedure in massive machine-type communications (mMTC) massive MIMO systems (M-MIMO). Physical phenomena are continuously monitored by sensors in the real world, leading to well-defined activity patterns in the data. This work investigates adjustments in regularization weight parameter$\lambda$of sparse group regularizer GLASSO under activity correlation scenarios. By adjusting the input parameters in such regularizer, one can improve the non-Bayesian UAD performance, where pilot hopping ($\mathbf{(H H}$) sequences are deployed by$\mathbf{m M T C}$sensors. Several input factors influence UAD performance, specifically the square cell side$Q$, the number of sensor terminals (UT)$U$, and path loss coefficient$\xi$. Our work identifies how changes in these factors can be incorporated into the regularization parameter to improve the UAD performance. We present an analytical framework that links the optimal$\lambda_{\text{opt}}$to the key system parameters$\xi, Q$, and$U$through power-law functions. We fit results curves and derive expressions that effectively quantify the relationships for three parameters, achieving high coefficients of determination and low mean squared errors in all three fits with high accuracy. Furthermore, we propose a joint fit expression for the optimal$\lambda_{\text{opt}}$, enabling automatic parameter tuning based on system and channel configuration. This advancement enhances the efficiency and robustness of GLASSO-based UAD systems across various operational conditions and channel scenarios.

The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.

The growing demand for improved spectral efficiency is one of the main challenges for the upcoming beyond fifth-generation wireless mobile communications networks. While massive multiple-input multiple-output (MIMO) technology has been demonstrating its potential in achieving higher spectral efficiency, the persistent problem of pilot contamination poses a significant hurdle for these systems. To address this issue, the Rate-Splitting Multiple Access (RSMA) framework has emerged as a potential solution. In this paper, we present a novel approach that leverages reinforcement learning (RL) with the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the sum spectral efficiency (SUM-SE) in a massive MIMO system implementing the RSMA framework with all users sharing a single pilot. The numerical results indicate that the proposed DDPG-based method is a competitive tool for optimizing the SUM-SE in massive MIMO scenarios employing the RSMA framework.

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the spectral efficiency (SE) of the system. It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a very desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might become even worse due to the users’ proximity. Reconfigurable intelligent surfaces (RISs), which are capable of smartly controlling the wireless channel, can be leveraged to achieve intra-cell pilot reuse. In this paper, our main contribution is an RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we then optimize the RIS phase-shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements achieved by the proposed scheme (for both uplink and downlink transmissions) compared to other alternatives.

In this paper, we have derived a closed-form expression for the signal-to-interference-and-noise ratio (SINR) in extra-large multiple-input multiple-output (XL-MIMO) systems with spatially correlated multi-state line-of-sight (LoS)/ non- LoS(NLoS) channels, which is valid for a sufficiently large number of base station (BS) antennas, when least-squares (LS) channel estimation is used and under the absence of pilot reuse among the scheduled user equipments (UEs). The derived SINR expression can be computed based on the channel statistics instead of the instantaneous channel realizations, which makes it valid for hundreds of channel coherence blocks. We also have proposed an expeditious user scheduling (US) algorithm for crowded XL-MIMO systems, which aims to maximize the sum spectral efficiency (SE) with the constraint of assigning mutually orthogonal pilots to all the scheduled UEs, such that their channel estimation accuracy is very high. The numerical results reveal that the proposed SINR expression is very accurate for a relatively large number of BS antennas, and that the proposed US algorithm improves considerably the sum SE in crowded scenarios.

In a cell-free massive multiple-input–multiple-output (CF-mMIMO) network, multiple access points (APs) actively cooperate to serve users’ equipment (UEs). We consider how the random access (RA) problem can be addressed by such a network under the occurrence of pilot collisions. To find a solution, we embrace the user-centric perspective, which basically dictates that only a preferred set of APs needs to serve a UE. Due to the success of the strongest-user collision resolution (SUCRe) protocol for cellular (Ce) mMIMO, we extend it by considering the new setting. Besides, we establish that the user-centric perspective naturally equips a CF network with robust fundamentals for resolving collisions. We refer to this foundation as spatial separability, which enables multiple colliding UEs to access the network simultaneously. We then propose two novel RA protocols for CF-mMIMO: 1) the baseline cell-free (BCF) that resolves collisions with the concept of spatial separability alone and 2) the cell-free sucre (CF-SUCRe) that combines SUCRe and the spatial separability principle to resolve collisions. We evaluate our proposed RA protocols against the Ce-SUCRe. Respectively, the BCF and CF-SUCRe can support$7\times $and$4\times $more UEs’ access on average compared to the Ce-SUCRe with an average energy efficiency gain based on the total power consumed (TPC) by the network per access attempt of$52\times $and$340\times $. Among our procedures, even with a higher overhead, the CF-SUCRe is superior to BCF regarding TPC per access attempt. This is because the combination of methods for collision resolution allows many APs to be disconnected from the RA process without sacrificing much the performance. Finally, our numerical results can be reproduced using the code package available on:github.com/victorcroisfelt/cf-ra-spatial-separability.

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in high user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array, since its large dimension is of the same order of the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL-MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides of seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot (PDP) resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum-rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole.

In this study, the authors consider the application of machine learning (ML) models in cooperative spectrum sensing of cognitive radio networks (CRNs). Based on a statistical analysis of the classic energy detection scheme, the probability of detection and false alarm is derived, which depends solely on the number of samples and signal‐to‐noise ratio of the secondary users. The channel occupancy detection obtained from the established analytical techniques such as maximum ratio combining and AND/OR rules is compared to different ML techniques, including multilayer perceptron (MLP), support vector machine, and Naive Bayes, based on receiver operating characteristic and area under the curve metrics. By using standard profiling tools, they obtain the computational performance of the analysed models during the training phase, a critical step for operating in CRNs. Ultimately, the results demonstrate that the MLP ML technique presents a better trade‐off between training time and channel detection performance.

The recently proposed strongest-user collision resolution (SUCRe) protocol resolves the majority of pilot collisions in crowded machine-type massive MIMO scenarios. We show in this paper that the random access (RA) performance of SUCRe can be improved by replacing the hard decision retransmission rule by a soft decision one, so that each user decides to retransmit or not its pilot sequence with a suitable probability. First, we propose to obtain this probability by mapping the ratio between the signal gain of each UE and the sum of the contenders' signal gains with the aid of a sigmoid function. Then, we propose to determine the retransmission probability of each UE according to the probability of this UE being the strongest contender, while proposing also an approximated probability expression for this event. Our results demonstrate an improvement in terms of a lower number of access attempts and a lower fraction of failed access attempts compared to the SUCRe protocol, while maintaining its features of being decentralized and uncoordinated.

In order to allow the operation of cheap and simple computational nodes, the reduction of the complexity related to the computation of signal processing techniques in massive multiple-input multiple-output (M-MIMO) is a desirable property to be obtained. With this in mind, several methods have been proposed to reduce the complexity of the classical combining/precoding schemes, being one of these based on the Kaczmarz algorithm (KA). Recent works demonstrated that KA-based approaches can suitably estimate the signals received and transmitted in the uplink and downlink phases; simultaneously, the procedure seemed to result in a very low computational complexity. Motivated by such context, this paper proposes a modification in the primary KA-based scheme for M-MIMO, striving to improve its rate of convergence, reducing complexity while holds the performance. Such improvement underlies the effects of pathloss and shadowing over the KA's rate of convergence, as well as in the spectral efficiency. Numerical results are given supporting that the proposed method outclasses the original proposal.

Multiuser transmission (MuT) can be carried out at the base station of a multiple‐input–multiple‐output system for mitigating the downlink multiuser interference, achieving a near‐single‐user performance at the mobile terminals. These MuT systems can be designed for optimising different performance metrics, such as the bit‐error‐rate (BER), which is the focus of this study. In order to mitigate the MuT's complexity, a convex formulation for the minimum BER (MinBER) optimisation problem is proposed, by employing some slight modifications on its original formulation. The authors have shown that the novel convex formulation makes the application of some optimisation algorithms very efficient, leading to a reduced complexity and enabling the system operation in higher dimensions. Indeed, the authors also show that in these conditions the MinBER–MuT system presents the most noticeable gains in performance. The results demonstrate that, among the investigated techniques, the proposed Line Search Quasi‐Newton algorithm relying on the penalty function approach results in the best performance versus complexity trade‐off in the context of high capacity multiuser systems.

In this work heuristic ant colony optimization (ACO) procedure is deployed in conjunction with lattice reduction (LR) technique aiming to improve the performance-complexity tradeoff of detection schemes in MIMO communication. A hybrid LR-ACO MIMO detector using the linear minimum mean squared error (MMSE) criterion as initial guess is proposed and compared with other traditional (non)linear MIMO detector, as well as heuristic MIMO detection approaches from the literature in terms of both performance and complexity. Numerical results show that the proposed LR-ACO outperforms the traditional ACO MIMO detector, as well as the proposed ACO detector with the MMSE solution as initial guess, with a significant complexity reduction.