Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption.

As a technology that can accommodate more users and significantly improve spectral efficiency, non-orthogonal multiple access (NOMA) has attracted the attention of many scholars in recent years. The basic idea of NOMA is to implement multiple access in the power domain and decode the desired signal via successive interference cancellation (SIC). However, the resource allocation problem in such NOMA system is non-convex. It is difficult to directly solve this optimization problem through conventional methods. As such, we propose to apply a reinforcement learning (RL) approach based on cooperative Q-learning to solve the resource allocation problem in multi-antenna downlink NOMA systems. First, we formulate the resource allocation process as a sum rate maximization problem, subject to the power budget constraints and quality of service (QoS) condition. Second, we design a reward function to improve the sum rate while meeting the power and capacity constraints. Multiple Q-tables are created and cooperatively updated to get the optimal beamforming matrix. Then, we analyze the convergence of our proposed RL based power allocation method. Our simulations show that the proposed power allocation scheme yields excellent performance in terms of sum rate, energy efficiency, and spectral efficiency.

Source localization in underwater sensor networks (UWSNs) presents complex challenges due to sensor nodes drift caused by ocean currents, non-line-of-sight (NLOS) propagation resulting from underwater multipath effects, and environmental noise. This paper proposes a practical and innovative algebraic solution based on the time difference of arrival (TDOA) for source localization in shallow seas. The proposed solution effectively addresses the issues arising from sensor position errors and multipath effects by incorporating the sea-surface reflection non-line-of-sight (SNLOS) link and optimizing the algorithm, thereby significantly improving positioning accuracy. The core concept of the method involves utilizing the weighted least squares algorithm to obtain an initial estimate of the source position, followed by direct estimation of the bias and subsequent refinement of the solution. In contrast to traditional closed-form solutions, this method avoids the introduction of intermediate parameters and directly handles the estimated bias from the previous step. Even when only considering the line-of-sight (LOS) link, the proposed solution achieves precise localization with a minimal number of sensors. Theoretical analysis demonstrates that the solution can achieve the Cramér–Rao lower bound (CRLB) accuracy under low noise conditions, and simulation results validate the superior performance of the proposed solution.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

This paper investigates the secrecy energy efficiency (SEE) maximization problem in a downlink multiple-input single-output (MISO) wireless communication system assisted by an intelligent reflecting surface with movable elements (ME-IRS). Unlike a conventional IRS, which has fixed-position elements, the proposed ME-IRS enables dynamic adjustment of element positions to exploit additional spatial degrees of freedom for performance enhancement. However, such flexibility introduces new challenges due to the strong coupling among transmit beamforming, IRS phase shifts, and element positions, as well as the additional power consumption caused by element movement. To address these issues, we formulate an SEE maximization problem by jointly optimizing the transmit beamforming, phase shift matrix, and element positions. The resulting problem is highly non-convex owing to the fractional objective function and coupled variables. To address this challenge, an efficient alternating optimization (AO) framework is developed by leveraging semidefinite relaxation (SDR), successive convex approximation (SCA), and gradient-based methods. Simulation results demonstrate that the proposed ME-IRS configuration significantly outperforms conventional fixed-position and discrete-position IRS configurations in terms of SEE, providing valuable insights into the impact of movable region size and system parameters.