This paper pioneers the domain of multi-user holographic unmanned aerial vehicle (UAV) communications, establishing a robust foundation for future advancements in next-generation aerial wireless networks. It investigates the joint design of hybrid holographic beamforming and three-dimensional (3D) positioning for a UAV equipped with a reconfigurable holographic surface (RHS), with the objective of maximizing the network’s sum rate. To tackle this inherently complex and non-convex optimization problem, a novel alternating optimization framework is proposed. The solution leverages zero-forcing (ZF) digital beamforming and a gradient ascent strategy to iteratively update the holographic beamforming weights and the UAV’s 3D location, while satisfying key system constraints. This framework is tailored to efficiently navigate the trade-offs between hybrid transceiver design and UAV mobility limitations, ensuring both adaptability and performance scalability. Simulation results confirm that the proposed approach achieves substantial gains in sum rate and system robustness compared to conventional methods, validating its effectiveness under diverse channel and deployment conditions.

This paper presents a novel framework for cognitive radio (CR)-enabled integrated terrestrial and non-terrestrial networks (ITNTNs), comprising a primary low-Earth-orbit (LEO) satellite network and a secondary terrestrial network. In particular, a beyond-diagonal reconfigurable intelligent surface (BD-RIS) mounted secondary base station (BS) reuses the same spectrum to communicate with the secondary user. The proposed framework improves the energy efficiency of the secondary network while ensuring that the interference temperature threshold of the primary LEO network is not violated. The joint optimization of BS power allocation and BD-RIS phase shifts is considered, which results in a highly nonconvex problem. To address this challenge, the Dinkelbach method is first employed to transform the fractional objective function, followed by the development of an alternating optimization strategy. Specifically, the BS power allocation is optimized using the Lagrangian method with Karush-Kuhn-Tucker (KKT) conditions, while the BD-RIS phase shifts are updated through manifold optimization techniques. Numerical results demonstrate that the proposed BD-RIS framework performs better than conventional diagonal RIS (D-RIS) configurations in terms of energy and spectral efficiency, highlighting its potential to enable green, adaptive, and high-capacity 6G ITNTN deployments.

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.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

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.

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.

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system.

In this paper, we propose to remove the analog stage of hybrid beamforming (HYBF) in the millimeter wave (mmWave) full-duplex (FD) systems. Such a solution is highly desirable as the analog stage suffers from high insertion loss and high power consumption. Consequently, the mmWave FD nodes can operate with a fewer number of antennas, instead of relying on a massive number of antennas, and to tackle the propagation challenges of the mmWave band we propose to use near-field intelligent reflecting surfaces (NF-IRSs). The objective of the NF-IRSs is to simultaneously and smartly control the uplink (UL) and downlink (DL) channels while assisting in shaping the SI channel: this to obtain very strong passive SI cancellation. A novel joint active and passive beamforming design for the weighted sum-rate (WSR) maximization for the NF-IRSs-assisted mmWave point-to-point FD system is presented. Results show that the proposed solution fully reaps the benefits of the IRSs, only when they operate in the NF, which leads to considerably higher gains compared to the conventional massive MIMO (mMIMO) mmWave FD and half duplex (HD) systems.

Full Duplex (FD) communication can revolutionize wireless communications as it avoids using independent channels for bi-directional communications. This work generalizes the point-to-point FD communication in millimeter wave (mmWave) band consisting of K-pairs of massive MIMO FD nodes operating simultaneously. We present a novel joint hybrid beamforming (HYBF) and combining scheme for weighted sum-rate (WSR) maximization to enable the coexistence of massive MIMO FD links cost-efficiently. The proposed algorithm relies on alternative optimization based on the minorization-maximization method. Moreover, we present a novel SI and massive MIMO interference channel aware power allocation scheme to include the optimal power control. Simulation results show significant performance improvement compared to a traditional bidirectional fully digital half-duplex (HD) system.

This paper considers a bidirectional (BD) full-duplex (BD-FD) communication system design under the joint sum-power and per-antenna power constraints. The sum-power constraints are naturally imposed by regulation to limit the total transmit power, and the per-antenna power constraints consider the physical limits of the power amplifiers (PAs). We propose a novel beamforming design to maximize the weighted sum-rate (WSR) with alternating optimization under the limited dynamic range (LDR) noise model. At each iteration, we use minorization-maximization approach to optimize the beamformers and power allocation. Simulation results show significant performance gain compared to a half-duplex BD system or FD system with only sum-power constraints. However, the gains are limited by the maximum of the thermal noise variance or the LDR noise variance.

In-band Full-Duplex (FD) is a promising wireless transmission technology allowing to increase data rates by up to a factor of two, via simultaneous transmission and reception, but with a potential to increase system throughput even much more in cognitive radio and random access systems thanks to simultaneous transmission and sensing. In this work, we consider a practical hybrid beamforming design for a bidirectional massive MIMO FD system under the joint per-antenna and sum-power constraints. Moreover, we consider non-ideal circuitry in the transmit and receive chains, which is modelled with the limited dynamic range (LDR) noise model. The per-antenna power constraints take into account the actual physical limits of the power amplifiers and the sum-power constraints are imposed to limit the total transmit power. The precoders are optimized with alternating optimization by using the minorization-maximization approach. Simulation results show significant performance improvement compared to a traditional bidirectional half-duplex system.

In-band Full Duplex (FD) is a wireless communication technology which has the potential to transmit and receive simultaneously in the same frequency band. Self-interference cancellation (SIC) is the key enabler to achieve FD operation. As the SI is severe, some SIC is required at the antenna level and in the analog domain before analog to digital converter (ADC) in the receiver chain because the ADC has only a limited dynamic range. Here we consider deliberately scaling up the received signal, provoking ADC saturation due to the SI signal. This leads to missing samples which we propose to reconstruct under the assumptions that the receive signal of interest is a low pass bandlimited signal with known spectrum (mask), oversampling and (perfect) digital SIC after the ADC. The missing samples are estimated by fixed lag Kalman smoothing. More upscaling leads to fewer available samples but with less quantization noise. Hence an optimum compromise arises. We provide an approximate resulting MSE analysis based on large random matrix theory, replacing randomly selected Fourier transform vectors by vectors of i.i.d. variables. Simulation results show the improvement in reconstruction Signal to Noise Ratio (RSNR) and the optimal compromise behavior.