As energy concerns grow with the rise of energy-constrained devices, it becomes imperative to design an energy-efficient and adaptive multiple access (MA) scheme, supported with accurate energy efficiency (EE) evaluation. Non-orthogonal multiple access (NOMA) enhances EE, yet downlink NOMA faces challenges in terms of computational complexity and power demands of successive interference cancellation (SIC), problematic particularly for energy-limited devices. Existing studies overlook the additional SIC power consumption at NOMA receivers, thus overestimating EE, and giving misleading insights for real system design. Besides the need for more accurate EE evaluation, an adaptive MA approach based on this additional power consumption is required. This paper proposes a SIC-power-aware adaptive SDMA/cooperative NOMA system. An optimization problem is formulated by optimizing MA mode decision, BS beamforming, power allocation factors, and strong user relaying power, to maximize the system EE. We decouple the problem into SDMA/NOMA selection and power allocation sub-problems, solved via a modified semi-orthogonal user selection (SUS) algorithm, successive convex approximation (SCA), difference-of-convex (DC) programming, and semidefinite programming (SDP) approaches. Numerical evaluation confirms the efficiency of the proposed scheme, compared to the baseline schemes.

Beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as an innovative and generalized RIS framework that provides greater flexibility in wave manipulation and enhanced coverage. In comparison to conventional RIS, optimization of BD-RIS is more challenging due to the large number of optimization variables associated with it. Typically, optimization of large-scale optimization problems utilizing traditional optimization methods results in high complexity. To tackle this issue, we propose a gradient-based meta learning algorithm which works without pre-training and is able to solve largescale optimization problems. With the objective to maximize the sum rate of the system, to the best of our knowledge, this is the first work considering joint optimization of receiving beamforming vectors at the base station (BS), scattering matrix of BD-RIS and transmission power of users equipment (UEs) in uplink rate-splitting multiple access (RSMA) communication. Numerical results demonstrate that our proposed scheme can outperform the conventional RIS RSMA framework by 22.5 %.

This paper investigates joint user pairing, power, and time slot duration allocation in the uplink multiple-input single-output (MISO) multi-user cooperative rate-splitting multiple access (C-RSMA) networks in half-duplex (HD) mode. We assume two types of users: cell-center users (CCU) and cell-edge users (CEU); first, we propose a user pairing scheme utilizing a semi-orthogonal user selection (SUS) and a matching-game (MG)-based approach where the SUS algorithm is used to select CCU in each pair. Afterward, the CEU in each pair is selected by considering the highest channel gain between CCU and CEU. After pairing is performed, the communication occurs in two phases: in the first phase, in a given pair, CEUs broadcast their signal, which is received by the base station (BS) and CCUs. In the second phase, in a given pair, the CCU decodes the signal from its paired CEU, superimposes its own signal, and transmits it to the BS. Moreover, utilizing uplink RSMA principle, only the CCUs split their messages into two sub-messages. Meanwhile, the messages of CEUs are kept without splitting. We formulate a joint optimization problem in order to maximize the sum rate subject to the power budget constraints of the user equipment (UE) and minimum data rate requirements at each UE. Since the formulated optimization problem is non-convex, we adopt a bi-level optimization to make the problem tractable. We decompose the original problem into two sub-problems: the user pairing sub-problem and the resource allocation sub-problem, where the user pairing sub-problem is independent of the resource allocation sub-problem, and once pairs are identified, the resource allocation sub-problem is solved for a given pair. The resource allocation sub-problem is solved by invoking a low-complexity pre-training free gradient-based meta-learning (GML) algorithm. Simulation results demonstrate that our proposed C-RSMA scheme can achieve around 100%, 51%, 53%, and 215% improvement over C-NOMA with fixed time slot allocation, RSMA, NOMA, and C-RSMA random pairing, respectively at CEU power budget of 17 dBm.

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, a cell-center user (CCU), and a cell-edge user (CEU) located at the edge of adjacent cells. Through JT-CoMP, all BSs collaborate to simultaneously transmit the data to all users including the CCUs and CEU. To enhance the signal quality for the CEU, CCUs relay the common stream to the CEU by operating in half-duplex (HD) relaying mode. We aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCU s, and the time slot fraction aiming to maximize the minimum achievable data rate. The formulated problem is non-convex and challenging to solve directly. To address this, we employ change-of-variables, first-order Taylor approximations and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show improvements of 12% and 41 % over RSMA and HD C-NOMA, respectively in high channel disparity between the BS and UEs.

This paper introduces a 2-layer cooperative rate-splitting multiple access (C-RSMA) framework designed for multiple groups of two users. Within each user group, the message is divided into three components: an inter-group common message, an inner-group common message, and a private message. Our framework incorporates a novel user-pairing policy, leveraging a combination of semi-orthogonal user selection (SUS) and a matching-game (MG)-based algorithm to identify user pairs, which allows for selecting the cell-center-users (CCUs) and cell-edge-users (CEUs) for each pair. To enhance signal quality at the CEUs, we employ cooperative communication, where each CCU relays the inner-group common message to its paired CEU. This framework is formulated as an optimization problem by jointly optimizing user pairing, beamforming vectors at the base station (BS), common stream split, time slot duration, and transmit power of CCUs to maximize the network sum rate. The formulated problem is highly non-convex and difficult to solve, and hence, we adopt bi-level optimization which breaks the original problem into outer and inner problems. The outer problem is considered as the user pairing problem and we solve it using the SUS-MG algorithm. Once the users are paired, we solve the inner optimization problem for each pair using a successive convex approximation (SCA) approach. Finally, numerical results demonstrate that our proposed approach can outperform baseline schemes.

This paper studies the downlink scenario of an unmanned aerial vehicle (UAV)-assisted rate-splitting multiple access (RSMA). The UAV serves as a full duplex (FD) amplify-and-forward relay to assist the base station (BS) in its communication with a set of user equipments (UEs). In this framework, we formulate an optimization problem with the goal of maximizing the minimum achievable rate by jointly optimizing the BS precoding vectors, the common-stream split, UAV transmit power, and the UAV location subject to the power budget constraints of the BS and UAV. Due to the non-convex nature of the problem, we propose an alternating optimization algorithm that decomposes the main problem into a power allocation subproblem and a UAV location subproblem, which are solved in an alternative way. Both subproblems are solved using a successive convex approximation approach. Our numerical results show that the proposed model outperforms traditional RSMA, non-orthogonal multiple access (NOMA), and UAV-assisted NOMA, demonstrating the efficacy of our approach in achieving higher minimum achievable rates.

This paper proposes a reconfigurable intelligent surface (RIS)-assisted cooperative rate-splitting multiple access (C-RSMA) framework with simultaneous wireless information and power transfer (SWIPT). In the proposed framework, the user with good channel gain can act as a full-duplex (FD) relay to forward the common stream to the user with poor channel gain. Moreover, by leveraging SWIPT technology, the user with good channel gain can simultaneously receive information and harvest energy from the base station (BS). This framework is formulated as an optimization problem by jointly optimizing beamforming vectors at the BS, common stream split, power splitting factor, and phase shift configuration at the RIS with the objective of maximizing the sum rate of both users. To tackle this challenging problem, an alternating optimization algorithm based on the successive convex approximation and difference-of-convex approach is proposed. Numerical results demonstrate that our proposed approach can outperform the baseline schemes.

This paper presents a downlink reconfigurable intelligent surface (RIS)-assisted half-duplex (HD) cooperative rate-splitting multiple access (C-RSMA) networks. The proposed system model is built up considering one base station (BS), one RIS, and two users. With the goal of minimizing the network energy consumption, a joint framework to optimize the precoding vectors at the BS, common stream split, relaying device transmit power, the time slot allocation, and the passive beamforming at the RIS subject to the power budget constraints at both the BS and the relaying node, the quality of service (QoS) constraints at both users, and a common stream rate constraint is proposed. The formulated problem is a non-convex optimization problem due to the high coupling among the optimization variables. To tackle this challenge, an efficient algorithm is presented by invoking the alternating optimization (AO) technique, which decomposes the original problem into two sub-problems; namely, sub-problem-1 and sub-problem-2, which are alternatively solved. Specifically, sub-problem-1 jointly optimizes the precoding vectors, common stream split, and relaying device power. Meanwhile, sub-problem-2 is to optimize the phase shift matrix at the RIS. In order to solve sub-problem-1, an efficient low-complexity solution based on the successive convex approximation (SCA) is proposed. Meanwhile, and with the aid of difference-of-convex (DC) rank-one representation and the SCA approach, an efficient solution for the phase shift matrix at the RIS is obtained. The simulation results demonstrate that the proposed RIS-assisted HD C-RSMA achieves a significant gain in minimizing the total energy consumption compared to the RIS-assisted RSMA scheme, RIS-assisted HD cooperative non-orthogonal multiple access (C-NOMA), RIS-assisted NOMA, HD C-RSMA without RIS, and HD C-NOMA without RIS.