Stacked intelligent metasurface (SIM) as an advanced signal processing paradigm enables real-time processing of electromagnetic waves at the speed of light. Benefiting from this technology, the current paper studies the downlink transmission of a wireless network, where a SIM-deployed base station (BS) serves two disjoint sets of energy- and information-oriented terminals via simultaneous wireless information and power transfer (SWIPT). Toward optimizing the performance of this system, a resource allocation problem is formulated for characterizing the fundamental trade-off between the aggregate information rate and the overall harvested energy. By virtue of its tightly-coupled and non-convex nature, we equivalently transform this problem to a Markov decision process (MDP) form. Next, we train an asynchronous advantage actor critic (A3C) agent on the MDP-reformulated problem for optimizing the transmit power of the BS and the electromagnetic response of the SIM, in a joint fashion. Subsequently, by taking into account the mobility of terminals, we further enrich the adaptability of the trained A 3 C agent via meta-learning. It is numerically revealed that incorporating SIM leads to an approximate 30 % enhancement in the energy efficiency of existing SWIPT systems.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been benefited from the assistance of optical reflecting intelligent surface (ORIS) to support energy-limited devices via simultaneous lightweight information and power transfer (SLIPT). This article studies the application of rate splitting multiple access (RSMA) for effective interference management and enhancing the data rate of these systems. Regarding the considerable bandwidth of the OWC band and also considerable energy consumption of the multi-LED transmitter, we formulate an energy efficiency (EE) maximization problem to jointly optimize the system variables, including transmit beamforming, LED selection, rate adaptation and ORIS element association, while adhering to the system requirements. Accordingly, we propose a dynamic resource allocation mechanism, leveraging proximal policy optimization (PPO) to accommodate system dynamism and optimize its variables. Concerning the frequent obstruction of OWC Line-of-Sight (LoS) links and consequently swift system reconfiguration, we improve the adaptability and predictability of the PPO agent by integrating Meta-learning technique. Simulations reveal that the proposed Meta-PPO algorithm has superior performance compared to the PPO method in the presence of ORIS with 76% gain. Furthermore, employing an ORIS in the proposed system model improves the performance by 51% compared to a scenario without ORIS.

In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem that jointly optimises the gain, element selection and phase shift matrices of the active STAR-RIS, the transmit beamforming of the BS and the power splitting ratio of the users. With respect to the highly coupled and non-convex form of this problem, an alternating optimisation solution approach is proposed, using tools from convex optimisation and reinforcement learning. Specifically, semi-definite relaxation (SDR), difference of convex functions (DC), and fractional programming techniques are employed to transform the non-convex optimisation problem into a convex form for optimising the BS beamforming vector and the power splitting ratio of the SWIPT. Then, by integrating meta-learning with the modified deep deterministic policy gradient (DDPG) and soft actor-critical (SAC) methods, a combinatorial reinforcement learning network is developed to optimise the element selection, gain and phase shift matrices of the active STAR-RIS. Our simulations show the effectiveness of the proposed resource allocation scheme. Furthermore, our proposed active STAR-RIS-based SWIPT system outperforms its passive counterpart by 57% on average.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS’s transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of active RIS elements, which adaptively and remarkably manages the system’s power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the active RIS, the RSMA common data rate at users, as well as the UAV’s trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. According to simulations, integrating meta-learning yields a notable 36% increase in system EE. Additionally, substituting AARIS for fixed terrestrial active RIS results in a 26% EE enhancement.

Abstract In this paper, a downlink multi‐user communication of an intelligent reflecting surface (IRS)‐assisted multiple‐input single‐output (MISO) power‐domain non‐orthogonal multiple access (NOMA) system is investigated. Considering multi‐carrier (MC) transmission and to enhance user fairness, two users are assigned to the same subcarrier. For such a system, the authors optimize active beamforming at the base station (BS), subcarrier allocation policy, and phase shifts at the IRS to maximize the system throughput. A semi‐definite relaxation (SDR) is applied to tackle the non‐convex optimization problem, and an alternating optimization (AO) algorithm is proposed to obtain a suboptimal solution. Numerical results illustrate the higher throughput of the proposed MC multi‐user IRS‐aided MISO‐NOMA system as compared to the conventional IRS‐assisted orthogonal multiple access (OMA) system.