Terahertz (THz) massive multiple-input multiple-output (mMIMO) systems offer ultra-high data rates but face significant challenges such as beam squint effects, high power consumption, severe path loss, and signal blockage. Incorporating reconfigurable intelligent surfaces (RIS) can mitigate these issues but complicates channel state information (CSI) acquisition. To address this, we propose a generative artificial intelligence-based hierarchical deep reinforcement learning (GAI-HDRL) framework that jointly performs channel prediction, hybrid precoding at the base station (BS), passive RIS beamforming, and digital combining at the UE to minimize transmit power. The proposed GAI-HDRL efficiently decomposes the optimization into high-level (precoding and combining) and low-level actions (CSI prediction and RIS configuration), achieving fast convergence and improved prediction accuracy. Simulation results confirm its superiority over state-of-the-art methods in terms of performance and computational efficiency, demonstrating practical viability in THz communications.

This work lays the foundations for full-duplex (FD) extremely large (XL) holographic multiple-input multiple-output (MIMO) communication systems to achieve seamless integration of reconfigurable holographic surfaces (RHS) and FD capabilities, enabling ultra-high-capacity, low-latency, and energy-efficient wireless communications. We consider the problem of sum-rate maximization by jointly designing the digital beamformers, holographic beamformer, and holographic combiner at the FD base station to jointly suppress self-interference (SI) and cross-interference. However, this results in a highly non-convex problem, for which a novel alternating optimization combining the minorization-maximization principle and the gradient ascent method is proposed. Simulation results demonstrate that the proposed method almost doubles the spectral efficiency compared to a half-duplex (HD) system.

We investigate the performance of a multiple re-configurable intelligence surface (RIS)-aided millimeter wave (mmWave) beamspace multiple-input multiple-output (MIMO) system with multiple users (UEs). We focus on a challenging scenario in which the direct links between the base station (BS) and all UEs are blocked, and communication is facilitated only via RISs. The maximum ratio transmission (MRT) is utilized for data precoding, while a low-complexity algorithm based on particle swarm optimization (PSO) is designed to jointly perform beam selection, power allocation, and RIS profile configuration. The proposed optimization approach demonstrates positive trade-offs between the complexity (in terms of running time) and the achievable sum rate. In addition, our results demonstrate that due to the sparsity of beamspace channels, increasing the number of unit cells (UCs) at RISs can lead to higher achievable rates than activating a larger number of beams at the MIMO BS.

In this paper, we investigate the performance of the rate-splitting multiple access (RSMA) framework in a mmWave cell-free massive multiple-input multiple-output (CF-mMIMO) system assisted by multiple reconfigurable intelligent surfaces (RISs). We consider the practical scenario of low-resolution digital-to-analog converters (DACs) at the distributed access points (APs) to reduce hardware complexity and power consumption. Our main objective is to maximize the minimum rate among the users by jointly optimizing the precoding vectors at each AP, the common rates, and the reflection coefficients of RISs. The resultant non-convex optimization problem is then solved using alternating optimization and successive convex approximation-based methods. Numerical results demonstrate the superior performance of the proposed RSMA-based scheme over traditional methods across several deployment scenarios, with performance gains from RIS deployment notably improved in hotspot scenarios.

The terahertz communications have the potential to revolutionize data transfer with unmatched speed and facilitate the development of new high-bandwidth applications. This paper studies the performance of downlink terahertz system assisted by beyond diagonal intelligent reconfigurable surface (BD-IRS). For enhanced energy efficiency and low cost, a joint precoding and BD-IRS phase shift design satisfying the 1-bit resolution constraints to maximize the spectral efficiency is presented. The original problem is non-linear, NP-hard, and intricately coupled, and obtaining an optimal solution is challenging. To reduce the complexity, we first transform the optimization problem into two problems and then iteratively solve them to achieve an efficient solution. Numerical results demonstrate that the proposed approach for the BD-IRS assisted terahertz system significantly enhances the spectral efficiency compared to the conventional diagonal IRS assisted system.

The third generation partnership project (3GPP) has recently defined two frequency bands for direct access with satellites, which is a concrete step toward realizing the anticipated space-air-ground integrated networks. In addition, given the rapid increase in the numbers of satellites orbiting the Earth and emerging satellites applications, non-terrestrial networks (NTNs) might soon need to operate with integrated access and backhaul (lAB), which has been standardized for terrestrial networks to enable low-cost, flexible and scalable network densification. Therefore, this work investigates the performance of satellite lAB, where the same spectrum resources at a low earth orbit (LEO) satellite are utilized to provide access to a handheld user (UE) and backhaul via inter-satellite links. The UE is assumed to operate with frequency division duplex (FDD) as specified by the 3GPP, while both FDD and time division duplex (TDD) are investigated for backhauling. Our analysis demonstrate that the interference between access and backhaul links can significantly affect the performance under TDD backhauling, especially when the access link comes with high quality-of-service demands.

Reflective Intelligent Surface (RIS)-assisted Ultra-Massive MIMO (Um-MIMO) systems in the terahertz (THz) spectrum are gaining attention for surpassing current wireless system limitations. However, limited diffraction at these frequen-cies typically results in low-rank Um-MIMO channels, completely reducing the potential for spatial multiplexing gains. In this work, we aim at promoting a new research direction towards the strategies for achieving high-rank Um-MIMO for RIS-assisted THz communications. The maximum achievable spatial multiplexing gain over the RIS-assisted channel is analyzed, and the optimal antennas and RIS elements placement strategy is proposed for achieving extremely high-rank Um-MIMO channels. Simulation results demonstrate that while conventional Um-MIMO THz systems display low-rank, the proposed approach enables achieving a rank on the order of hundreds for the Um-MIMO THz systems.

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.

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

In this work, we propose a time- and a unit cell-splitting protocol for supplying the energy needs of reconfigurable intelligent surfaces (RISs) through wireless energy harvesting (EH) from information signals. We first compute the RIS energy consumption per frame that is common for both protocols and incorporates the energy burden for channel estimation. Based on it, we subsequently formulate an optimization problem that maximizes the average rate under the constraint of meeting the RIS long-term energy consumption demands. In addition, closed-form solutions regarding the optimal allocation of resources are provided for both protocols in the case of deterministic channel gains for the transmitter-RIS links and a methodology to obtain such a solution in the general case of random channels. Finally, for the optimal resource allocation for both protocols numerical results based on Monte-Carlo simulations reveal that the unit cell-splitting protocol exhibits a superior performance compared to its time-splitting counterpart.

The key advantage of successive relaying (SR) networks is their ability to mimic the full-duplex (FD) operation with half-duplex (HD) relays. However, the main challenge that comes with such schemes is the associated inter-relay interference (IRI). In this work, we propose a reconfigurable intelligent surface (RIS)-enhanced SR network, where one RIS is deployed near each of the two relay nodes to provide spatial suppression of IRI, and to maximize the gain of desired signals. The resultant max-min optimization problem with joint phase-shift design for both RISs is first tackled via the semidefinite programming (SDP) approach. Then, a lower-complexity solution suitable for real-time implementation is proposed based on particle swarm optimization (PSO). Numerical results demonstrate that even relatively small RISs can provide significant gains in achievable rates of SR networks, and the proposed PSO scheme can achieve a near optimal performance.

In this work, we investigate the decode-and-forward (DF) relay-aided double reconfigurable intelligent surface (RIS)-assisted networks, where the signal is subject to reflections from two RISs before reaching the destination. Different relay-aided network architectures are considered for maximum achievable rate under a total power constraint. Phase optimization for the double-RIS channels is tackled via the alternating optimization and majorization-minimization (MM) schemes. Moreover, closed-form solutions are obtained for each case. Numerical results indicate that the deployment of two relays, one near each RIS, achieves higher rates at low and medium signal-to-noise ratios (SNRs) compared to placing a single relay between the two RISs; while at high SNRs, the latter approach achieves higher rates only if the inter-relay interference for the former case is considerably high.

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

In this paper, a low-complexity cell-edge-aware Antenna Selection (AS) algorithm is proposed for a Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) downlink system with Matched Filter (MF) precoding. We assume that the users are uniformly distributed in the cell, and therefore, have different Signal-to-Interference plus Noise Ratios (SINRs). At each iteration, the proposed algorithm selects one antenna to reduce the highest interference term between any two users to its minimum value. Furthermore, we utilize a Max-Min Power Allocation (MMPA) scheme to further enhance the performance of cell-edge users and achieve higher fairness. In addition, the complexity of the proposed AS algorithm is evaluated in terms of number of floating-point operations (FLOPs) required for its implementation. Finally, our proposed AS method is compared with other low-complexity AS schemes found in the literature and shown to demonstrate an impressive performance-complexity trade-off.

Massive Multiple-Input Multiple-Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). However, having a massive number of radio-frequency (RF) chains at the BS can be costly and energy inefficient. One way to achieve the diversity gain of massive MIMO systems is to employ a massive number of antennas with limited number of RF chains. Thus, antenna selection techniques can be applied to reduce the system complexity and hardware cost. In this paper, a Quantum-inspired Tabu Search (QTS) algorithm is applied to antenna selection in Massive MIMO systems and compared with two well known algorithms; namely, a Classical Tabu Search (CTS) algorithm and a Genetic Algorithm (GA). The QTS algorithm has a great advantage over CTS, since it only requires finding the optimum rotation angle to evolve the system towards a better solution. In contrast, in CTS, the dimensions of the tabu matrix are dynamic and need to be optimized. Moreover, to achieve maximum performance, these dimensions need to be reconfigured when changing the number of antennas or the number of iterations, while no such a problem occurs in the QTS. The QTS algorithm also shows better results in terms of the system capacity compared to CTS and GA. Furthermore, the classical and quantum inspired TS algorithms require much lower complexity than the GA.