Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering enhanced efficiency by reducing transmission data. However, implementing semantic communication faces significant challenges, particularly in resource-constrained devices that cannot support the complex artificial intelligence (AI) models required for semantic extraction. This paper addresses this challenge by proposing a novel unmanned aerial vehicle (UAV)-enabled semantic-bit coexisting relay system, where the UAV serves as intermediate nodes to assist transmissions from resource-limited users to the base station. By deploying semantic extraction models at the UAV, the proposed system solves the computational resource limitations for user devices while minimizing transmission latency via data size reduction. In such a system, we formulate a system latency minimization problem that jointly considers semantic compression model selection and bandwidth allocation. To address this complex problem, we develop an effective solution method by decomposing the original problem into a semantic compression model selection based on performance-latency trade-offs and a bandwidth-allocation optimization via convex optimization techniques. Extensive numerical evaluations demonstrate that the proposed framework consistently outperforms conventional schemes across diverse network settings and compression parameters, significantly reducing end-to-end latency while maintaining high-quality semantic communication.

This work explores combining the rate-splitting multiple-access (RSMA) technique with an active reconfigurable intelligent surface (RIS) to improve the quantized multiuser multiple-input single-output network. The active RIS facilitates communication between the base station (BS) and users equipped with low-resolution quantizers, whereas RSMA improves downlink transmission efficiency. By maximizing the spectral efficiency while minimizing the power consumption at the transmitter and active RIS, we formulate an energy efficiency maximization problem by jointly designing the BS precoding matrix and active RIS reflecting matrix. The optimization problem presents nonconvexity, which makes finding the optimal solution challenging. Therefore, we reformulate the problem into a reinforcement learning-based problem that is solvable by applying deep reinforcement learning (DRL) algorithms. To ensure action accuracy, we design a constraint-matching function that integrates with the DRL algorithm, forming a DRL framework securing all problem constraints. To assess the proposed DRL algorithm, we propose an alternating-based solution that decomposes the problem into precoding matrix optimization and active reflecting matrix optimization sub-problems, which are solvable using the successive convex approximation-based method. The performance evaluations demonstrate the convergence and effectiveness of the proposed approaches in various scenarios.

Recently, there has been a growing interest in exploring new multi-antenna technologies for 6 G wireless networks, including the use of extremely largescale antenna arrays, tremendously high frequencies, and novel antenna technologies. These emerging trends introduce unique characteristics that cannot be adequately addressed by classical far-field communication techniques with planar wavefronts. As a result, there is a need to investigate near-field communication design with spherical wavefronts. In this paper, we present a comprehensive overview of near-field communications, focusing on basic concepts, applications, and future directions.

This research examines quantized uplink multi-user MIMO communication systems with low-resolution quantizers at users and base stations (BS). In such a system, we employ the non-orthogonal multiple access (NOMA) technique for communication between users and the BS to enhance communication performance. To maximize the number of users that satisfy the quality of service (QoS) requirement while minimizing the user’s transmit power, we jointly optimize the transmit power and precoding matrices at the users and the digital beamforming matrix at the BS. Owing to the non-convexity of the objective function, we transform the problem into a reinforcement learning-based problem and propose a deep reinforcement learning (DRL) framework named QNOMA-DRLPA to overcome the challenge. Because the nature of the action decided by the DRL algorithm may not satisfy the problem constraints, we propose a postactor process to redesign the actions to meet all the problem constraints. In the simulation, we assess the proposed framework’s performance in training convergence and demonstrate its superior performance under various environmental parameters compared with other benchmark schemes.

This paper proposes a flexible and efficient access control scheme that combines the orthogonal frequency division multiple access and rate-splitting multiple-access techniques for enhancing the uplink transmission in a digital twin edge network system. We formulate a non-convex mixed integer optimization problem that minimizes the energy consumption of all Internet of Things devices (IoTDs) and maximizes the number of successful IoTD tasks. To this end, we propose a deep reinforcement learning (DRL) framework by normalizing a DRL training algorithm named deep deterministic policy gradient for efficiently designing the variables while ensuring the problem constraints. However, in the inference stage, the proposed DRL method may encounter different devices and services. Therefore, we design an exhaustive-improved DRL method that can improve the proposed DRL effectively using information from a digital-twin module. We also propose a mathematical approximation-based solution employing two convexification approach: Dinkelbach’s method and relaxed Linear Matrix Inequality (LMI). Through extensive simulations over different parameters and scenarios, we identify the polynomial complexity, stable convergence, and operating regime of the proposed solutions. It is also confirmed that the proposed approaches work well even with digital twin defects and provide improved performance in terms of energy consumption and number of successful tasks in comparison with benchmark schemes.

Aerial access infrastructures have been considered a compulsory component of the sixth-generation (6G) networks, where airborne vehicles play the role of mobile access points to service ground users (GUs) from the sky. In this scenario, intelligent reflecting surface (IRS) is one of the promising technologies associated with airborne vehicles for coverage extensions and throughput improvements, a.k.a., flying IRS (F-IRS). This study considers a multiuser multiple-input single-output (MISO) F-IRS system, where the F-IRS reflects downlink signals from ground base stations (BSs) to users located at underserved areas where direct communications are unavailable. To achieve the system sum-rate maximization, we proposed a deep reinforcement learning (DRL) algorithm namedFlyReflectto jointly optimize the flying trajectory and IRS phase shift matrix. First, end-to-end communications from a BS to its GUs via the F-IRS are analyzed to identify environmental and operational factors that impact achievable system sum rate. Subsequently, the system is transformed into a DRL model, which is resolvable by the deep deterministic policy gradient (DDPG) algorithm. To improve the action decision accuracy of the DDPG algorithm, we proposed a mapping function to guarantee that all constraints are satisfied regardless of noise additions in the exploration process. Simulation results showed that our proposed algorithm outperforms state-of-the-art algorithms in multiple scenarios.

Intelligent reflecting surface (IRS) is a promising 6G technology that can improve wireless communication capacity in a cost-effective and energy-efficient manner, by adjusting a large number of passive reflectors to appropriately change the signal propagation. In this study, we identified the achievable rate region of a two-hop interference channel with distributed multiple IRS relays. To do so, we formulated a non-convex problem that characterizes the rate-profile, and found its solution using successive convex approximation (SCA). We then proposed an alternating direction method of multipliers (ADMM) and alternating optimization (AO) based distributed and low-complex IRS control that maximizes the achievable sum-rate, and proved its convergence and optimality. We then compared the proposed IRS control with semi-definite relaxation (SDR)-, random phase-, deep reinforcement learning (DRL)- based IRS controls, and optimal amplify-and-forward (AF)-, interference neutralization (IN)-, and decode-and-forward (DF) based relaying schemes. We demonstrated that the proposed control with multiple IRS elements outperforms the benchmark controls in terms of the achievable rate region, achievable sum-rate, and energy efficiency under same power budget. We also confirmed that the discrete phase approximation of the proposed control provides near-optimal performance with fewer bits, and the proposed control is robust under imperfect CSI condition. The proposed controls can be efficiently applied to large-scale multi-pair multihop device-to-device and machine-type device communications in the interference-limited or low-powered dense networks of 5G and 6G environments.

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep Q-network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.