This paper investigates the maximization of energy efficiency in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTARS) empowered non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) with finite blocklength (FBL) transmission. The activated IoT devices (AIoTDs) in the coverage area can be divided into activated reflection-region IoTDs (ARDs) and activated transmission-region IoTDs (ATDs). Drawing on the capability of ASTARS to efficiently establish cascaded links across full-space, thereby enabling more effective adjustment of uplink signal transmission. The ASTARS can properly amplify signal to mitigate its multiplicative fading and long-distance uplink transmission attenuation, which introduce new degrees of freedom (DoF) into the optimization process. Considering the massive-access requirements of the constrained battery-life IoTDs, two device pairing strategies, namely strong-ARD poor-ATD pairing (SPP) and strong-ARD strong-ATD pairing (SSP), are proposed to enable short-packet data transmission. The penalty alternating iterative algorithm (PAIA) is proposed by jointly optimizing the binary matching coefficient, ASTARS coefficient matrix and AIoTDs transmission power. Simulation results illustrate that 1) the ASTARS can achieve the highest energy efficiency than the passive simultaneously transmitting and reflecting surface (PSTARS) and without reconfigurable intelligent surface (RIS); 2) SPP demonstrates superior performance compared to SSP; 3) an optimal number of ASTARS elements exists for each distinct ASTARS maximum amplification coefficient.

Low earth orbit (LEO) satellite-assisted edge computing is a promising paradigm for providing seamless connectivity to remote areas and enabling reliable services in dense urban scenarios. By establishing virtual line-of-sight (LoS) links and coherently combining reflected signals, the reconfigurable intelligent surface (RIS) can significantly enhance the channel conditions of satellite-terrestrial communication links. This paper integrates the novel simultaneously transmitting and reflecting beyond-diagonal RIS (STAR-BD-RIS) technology into the LEO satellite collaborative edge computing (LSCEC) system, where each ground user equipment (GUE) can simultaneously offload task bits to multiple satellites via a hybrid non-orthogonal multiple access (NOMA) scheme with the aid of STAR-BD-RIS. To minimize the weighted sum energy consumption of GUEs and LEO satellites, the transmit power, CPU frequency, offloading strategy, bandwidth allocation, and STAR-BD-RIS beamforming are jointly optimized by an iterative algorithm based on the successive convex approximation (SCA) technique and the penalty dual decomposition (PDD) method. Simulation results illustrate that: 1) the STAR-BD-RIS scheme achieves lower energy consumption than traditional RIS (T-RIS), simultaneously transmitting and reflecting RIS (STAR-RIS) and beyond-diagonal RIS (BD-RIS) schemes in LSCEC systems; 2) the proposed hybrid NOMA demonstrates superior scalability and performance over both NOMA and orthogonal frequency division multiple access (OFDMA); 3) the proposed satellite collaboration scheme significantly reduces the energy consumption compared to the case without collaboration, while exhibiting diminishing energy-saving gains as the number of satellites increases.

To address the issues of limited computational capability and constrained battery life faced by users in the Internet of Things, wireless-powered mobile edge computing (MEC) has been proposed as a promising solution. However, the efficiency of its key functions, namely task offloading and energy transfer, can be significantly impaired if the direct links between the access point (AP) and users are obstructed. Inspired by the potentials of active simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) for achieving full-space coverage and mitigating multiplicative fading effects, this paper investigates the incorporation of active STAR-RIS in wireless-powered MEC. To meet the high data rate requirements in future smart environments, we aim to maximize the total number of completed task bits. To address the formulated challenging non-convex problem, a resource allocation and active beamforming algorithm (RAABA) is first proposed for a basic two-user non-orthogonal multiple access (NOMA) scenario, jointly optimizing the energy transfer time, decoding order, transmit power, CPU frequency of users, and beamforming of STAR-RIS. We then extend the RAABA to general multi-user scenarios (RAABAM) by leveraging a matching-theory-based user pairing algorithm. Furthermore, a low-complexity RAABAM (L-RAABAM) is proposed by simplifying the matching process and deriving a closed-form expression for the optimal transmit power of users. Simulation results show that: i) by jointly optimizing multiple highly-coupled variables, our proposed RAABAM and L-RAABAM schemes achieve a higher total number of completed task bits; ii) the active STAR-RIS significantly outperforms passive/active traditional RIS and passive STAR-RIS; iii) the deployment rules for active STAR-RIS differ from those for passive STAR-RIS in wireless-powered MEC, where the optimal deployment location of active STAR-RIS depends on the number of its elements.

This article investigates the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted nonorthogonal multiple access (NOMA) systems with one cooperative jammer and dual eavesdroppers. To guarantee the uplink secure transmission, we maximize the sum secrecy rate under both the perfect and imperfect channel state information (CSI) by jointly optimizing the channel allocation, transmit power, and coefficient matrices. For the problem with perfect CSI, a deep reinforcement learning algorithm is proposed based on the deep deterministic policy gradient (DDPG) framework. Then, by introducing the arbitrary distorted noise to the state space, the proposed algorithm is extended to solve the problem under imperfect CSI without causing additional computational complexity. Simulation results illustrate that: 1) the symmetry of STAR-RIS results in severe information leakage and the sum secrecy rate further degrades when the dual eavesdroppers collaborate with each other; 2) the STAR-RIS with independent phase shift can achieve higher sum secrecy rate than that with coupled phase shift, while the performance gap is trivial when there are fewer STAR-RIS elements; and 3) our proposed algorithm can compensate for the impacts of the imperfect CSI, and the sum secrecy rate decreases with the increase of CSI uncertainty.

In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals, and also provides new degrees-of-freedom (DoFs) for manipulating signal propagation. We aim to maximize the total computation rate of all users, where the energy transfer time, transmit power and CPU frequencies of users, and the configuration design of STAR-RIS are jointly optimized. Considering the characteristics of STAR-RIS, three operating protocols, namely energy splitting (ES), mode switching (MS), and time splitting (TS) are studied, respectively. For the ES protocol, based on the penalty method, successive convex approximation (SCA), and the linear search method, an iterative algorithm is proposed to solve the formulated non-convex problem. Then, the proposed algorithm for ES protocol is extended to solve the MS and TS problems. Simulation results illustrate that the STAR-RIS outperforms traditional reflecting/transmitting-only RIS. More importantly, the TS protocol can achieve the largest computation rate among the three operating protocols of STAR-RIS.

The wireless-powered mobile edge computing (MEC) has been considered as a promising approach to enhance the computation capability and prolong the lifetime of user equipments (DEs). To further improve the efficiency of energy transfer and task offloading, in this paper, a novel concept called simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is introduced into the wireless-powered MEC. The total computation rate maximization problem is investigated by jointly optimizing the energy transfer time, UEs' resource allocation, and configuration design of STAR-RIS via a linear search algorithm based on the block coordinate decent (BCD) and successive convex approximation (SCA) techniques. Simulation results illustrate that our proposed algorithm can achieve a higher computation rate than the scheme with conventional RIS and that without local computing.