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.

As the landscape of sixth-generation (6G) wireless networks advances, low-Earth-orbit (LEO) satellite communication emerges as a promising solution for offering comprehensive global communication services, though its potential is challenged by severe large-scale path loss that significantly impairs the received channel capacity available to terrestrial users. To address this limitation, this paper investigates a federated learning aided distributed beamforming network for LEO satellite communications, namely the FederSat network. In order to enhance the average achievable rate of the LEO satellite networks, we propose a novel code-book-based distributed beamforming strategy in the FederSat networks. Through numerical analysis, we demonstrate that the proposed FederSat networks substantially outperform in average achievable rate. Additionally, more LEO satellites are encouraged for further enhancing the received signal power, which indicates that a mega-constellation LEO satellite network is preferable.

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.

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes; 2) fading factors have negligible effects on OP and spectral efficiency; and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users.

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.

A joint cascaded channel estimation scheme is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems with hardware imperfections. In particular, the practical hybrid near- and far-field electromagnetic radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations between different users and between different STAR-RIS elements, a multi-task learning (MTL)-based channel estimation framework is proposed. This framework is capable of estimating the cascaded channels for transmission and reflection simultaneously based on noisy observations of the mixture channel. Following the design guideline of the proposed MTL framework, an efficient multi-task network (MTN) is developed to reconstruct the high-dimensional channels with limited pilot overhead. In the proposed MTN architecture, a mixed convolution and multilayer perception module is exploited to capture the effective hybrid-field channel features. This module integrates the locality bias modeling of the channel-wise convolution and the long-range dependency modeling of MLP, which finely learns both local spatial correlations and specific spatial non-stationarity of the hybrid-field cascaded channels. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with the existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

In this paper, we study a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) supported simultaneous wireless information and power transfer (SWIPT) system, in which a multi-antenna access point (AP) provides power supply and communication services for multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) respectively. To maximize the performance of the STAR-RIS, we utilize the energy splitting (ES) operation protocol in this paper. By optimizing the AP beamforming and the STAR-RIS transmission and reflection coefficients, we create a weighted sum-power maximization problem. We demonstrate that the AP can service both the IDRs and the EHRs by merely delivering information signals, which simplifies the initial optimization challenge. Then, the initial problem is split into two subproblems, and an alternative optimization method is utilized to produce high-precision suboptimal solutions to the optimization problem. Finally, we testify that the sum power received by EHRs is remarkably enhanced by the STAR-RIS-aided SWIPT systems compared with benchmark schemes in numerical results.

A joint cascaded channel estimation framework is proposed for simultaneously transmitting and reflecting recon-figurable intelligent surfaces (STAR-RIS) systems with hardware imperfection, in which practical the hybrid-field electromagnetic wave radiation with spatial non-stationarity is investigated. By exploiting the cascaded channel correlations in user domain and STAR-RIS element domain, we propose a multitask network (MTN) with multi-expert branches to simultaneously reconstruct the high-dimensional transmitting and reflecting channels from the observed mixture channel with noise. In the proposed MTN architecture, a learnable shrinkage module is exploited to constrict the communication noise, and self-attention mechanism-based Transformer layers are utilized to extract the nonlocal feature of the non-stationary cascaded channel. Numerical results show that the proposed MTN achieves superior channel estimation accuracy with less training overhead compared with existing state-of-the-art benchmarks, in terms of required pilots, computations, and network parameters.

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.

This paper investigates a novel design for joint radar-communication (JRC) waveform, which is capable of improving the network’s performance by solving the phase shift mutation of the Chirp-BPSK waveform. We aim for jointly designing chirp waveform and improving Binary Phase Shift Keying (BPSK), as well as the hybrid chaotic spread spectrum code (CSSC). As a benefit, the JRC waveform can be successfully decoded, and the accumulation of phase shift can be suppressed by hybrid CSSC. The detailed deduction on the short-time Fourier transform (STFT) of the JRC waveform is adopted to analyse the complicated AF characters. Our analytical results demonstrate that the proposed JRC signal has superior spectrum performance, ambiguity function (AF), and transmission rate than conventional Chirp and Chirp-BPSK waveform. Numerical results are provided to confirm that (i) the proposed waveform is capable of providing better communication and radar performance; (ii) the performance of AF is not affected by the transmission rate for the proposed waveform.