This study explores a next-generation multiple access (NGMA) framework for cell-free massive MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.

We investigate the integration of beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) into cell-free massive multiple-input multiple-output (CF-mMIMO) systems to enhance simultaneous wireless information and power transfer (SWIPT). To simultaneously support two groups of users—energy receivers (ERs) and information receivers (IRs)— without sacrificing time-frequency resources, a subset of access points (APs) is dedicated to serving ERs with the aid of a BD-RIS, while the remaining APs focus on supporting IRs. A protective partial zero-forcing precoding technique is implemented at the APs to manage the non-coherent interference between the ERs and IRs. Subsequently, closed-form expressions for the spectral efficiency of the IRs and the average sum of harvested energy (HE) at the ERs are leveraged to formulate a comprehensive optimization problem. This problem jointly optimizes the AP selection, AP power control, and scattering matrix design at the BD-RIS, all based on long-term statistical channel state information. This challenging problem is then effectively transformed into more tractable forms. To solve these sub-problems, efficient algorithms are proposed, including a heuristic search for the scattering matrix design, as well as successive convex approximation and deep reinforcement learning methods for the joint AP mode selection and power control design. Numerical results show that a BD-RIS with a group- or fully-connected architecture achieves significant EH gains over the conventional diagonal RIS, especially delivering up to a 7-fold increase in the average sum of HE when a heuristic-based scattering matrix design is employed.

This paper investigates the integration of beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) into cell-free massive multiple-input multiple-output (CF-mMIMO) systems, focusing on applications involving simultaneous wireless information and power transfer (SWIPT). The system supports concurrently two user groups: information users (IUs) and energy users (EUs). A BD-RIS is employed to enhance the wireless power transfer (WPT) directed towards the EUs. To comprehensively evaluate the system's performance, we present an analytical framework for the spectral efficiency (SE) of IUs and the average harvested energy (HE) of EUs in the presence of spatial correlation among the BD-RIS elements and for a non-linear energy harvesting circuit. Our findings offer important insights into the transformative potential of BD- RIS, setting the stage for the development of more efficient and effective SWIPT networks. Finally, incorporating a heuristic scattering matrix design at the BD-RIS results in a substantial improvement compared to the scenario with random scattering matrix design.

The exponential growth of the Internet of Multimedia Things (IoMT) traffic has posed a threat of service quality degradation due to the limitation of current communication, networking, and computing advances in mobile networks. In this regard, managing the Quality-of-Experience (QoE) for IoMT services is a vital challenge to meet user satisfaction. To cope with this problem, we investigate the joint optimization of video quality variation and latency in multiuser downlink rate-splitting multiple-access (RSMA) networks, especially within imperfect network conditions and state information. To accomplish this, we first formulated the joint optimization problem into a Markov decision process framework, then exploited a deep reinforcement learning approach to adaptively calculate the optimal configuration of the RSMA against environment dynamics. As a result, the proposed deep deterministic policy gradient on RSMA-based video streaming system (DDPG-RMAVS) provides QoE maintenance by minimizing video resolution reduction and latency. Extensive simulation results revealed that the proposed DDPG-RMAVS algorithm surpasses existing algorithms by achieving higher video quality, lower delay, larger buffer capacity, and limited stalling events, representing a significant breakthrough in IoMT streaming optimization.

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is nonconcave. Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.

In recent years, video has become a tremendous growth of media in the content delivery network (CDN) coupled with the enormous increase of users. However, it leads to new challenges including an explosion of data demand on networking resources, backhaul bottleneck, and many congestions between transmissions in the network. To address these network congestions and minimize the content download latency, an LEO Satellite-based overlay system in CDN is proposed. A Low Earth orbit (LEO) satellite network, where the user equipment (UE) is covered by multiple satellites, is an important solution to the wireless communication network in the future. To ensure the quality of the service of the LEO satellite network, the handover problem and caching problem need to be considered. The paper focuses on a wireless network consisting of various caching nodes to serve users' requests supported by an LEO satellite-based overlay system. Our goal is the maximization the number of UEs covered by a Satellite by considering the location of users and the Received Signal Strength Indicator (RSSI) of users to select the best action. The Deep Reinforcement Learning (DRL) with multi-agent Q learning algorithm and Caching update algorithm with the popularity parameters are proposed to solve Problem statements of our System Model.