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This paper investigates an unconventional superposition scheme, i.e., rate-splitting multiple access (RSMA) to maximize the overall user fairness and high system performance gain for ultra-reliable low-latency communication (URLLC), enhanced mobile-broadband (eMBB) traffic coexistence in uplink scenarios. In particular, we focus on maximizing the worst-case performance of uplink eMBB and URLLC users when multiplexed in a given resource block using an effective rate-splitting approach among multiple sub-messages. Subsequently, a multi-objective optimization problem (MOOP) is formulated to jointly maximize the worst-case rate and minimize the worst-case packet-error probability (PEP) for eMBB and URLLC users, respectively, using effective power splitting and successive interference cancellation (SIC) decoding of the sub-messages. To solve the non-convexity of the formulated MOOP, we adopt a priori articulation scheme combined with the weighted product approach to transforming the MOOP into a single objective optimization problem (SOOP) and later, solve it using a low complex differential evolution (DE)-based meta-heuristic algorithm. We derive an optimal decoding strategy for sub-messages to ensure better user fairness among eMBB-URLLC traffic. Numerical simulations demonstrate the superiority of the considered RSMA-based superposition for hybrid eMBB-URLLC traffic over conventional slicing and superposition techniques. Moreover, the adopted weighted product method-based DE algorithm outperforms the state-of-art solutions.

This letter examines an integrated sensing and communication (ISAC) scheme for multi-user systems under full-duplex (FD) architecture. We examine an FD-ISAC system where the base station is responsible for target detection and shares the same resources for simultaneous downlink (DL) and uplink (UL) communication. For a green FD-ISAC architecture, our primary focus is addressing the robust energy efficiency maximization (EEM) problem. This involves the integrated beamforming design for the DL, UL, and radar users and UL transmission power. The optimization process follows constraints such as worst-case rate constraints, limitations imposed by bounded channel state information (CSI) error, transmit power constraints, and specific quality of service requirements. To address the EEM problem, we use an iterative algorithm that employs successive convex approximation (SCA) and second-order cone programming (SOCP) to achieve near-optimal resource allocation. Average energy efficiency and average spectral efficiency were compared for the proposed EEM algorithm and the benchmark spectral efficiency maximization (SEM) algorithm. Simulation results show that the FD-ISAC scheme significantly outperforms the conventional half-duplex scheme w.r.t. system performance.

As a revolutionary paradigm for green communication architecture for next-generation, reconfigurable intelligent surfaces (RISs) has been considered for simultaneous wireless information and power transfer (SWIPT). Nevertheless, the performance gain achieved by the conventional passive RISs is limited due to the multiplicative fading effect. In this paper, we investigate an unconventional framework of active reconfigurable intelligent surface (ARIS) aided multi-user (MU) multi-input multi-output (MIMO) system to captivate better performance for the SWIPT system. Particularly, we focus on the problem of power minimization via joint beamforming design at the base station (BS) and the ARIS for the considered SWIPT system under statistical channel estimation error (CEE) while guaranteeing the minimum rate requirement and the minimum energy-harvested constraints for information and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) approach to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at the BS and the ARIS independently in an iterative manner using general approximations. Simulation results confirm that the ARIS can significantly reduce the required transmission power by 50-60% when compared to passive RIS while satisfying given QoS constraints for SWIPT system under the CEE model.

Re-configurable intelligent surface (RIS)-aided communication has been envisaged as a frontier scheme to enable ultra-high spectral efficiency (SE) and energy efficiency (EE) for next-generation communication. This paper investigates an unconventional framework of RIS-aided full-duplex (FD) multi-user multiple-input multiple-output (MIMO) communication and analyzes its resource efficiency (RE), a preferable performance metric for realizing trade-off between SE and EE maximization. In particular, we focus on the RE maximization problem via a joint optimization of transmit covariance, optimal receive covariance, and phase-shift matrices for each RIS subject to the given constraint on the power budget. To solve the formulated non-convex problem, we propose two optimization approaches: a) policy gradient-based deep-reinforcement learning (DRL) algorithm based on a Markov decision process formulation for a stochastic-time varying channel and b) alternate optimization (AO) algorithm based on general approximations and majorization-minimization (MM) for static channel conditions. Simulation results validate the out-performance of the considered RIS-aided FD-MIMO system compared to the counterpart system with half-duplex (HD) mode and without RIS case. The proposed DRL algorithm achieves comparable RE performance with reduced computational complexity and running time compared to the traditional AO-based algorithm.

This letter investigates a scalable hybrid non-orthogonal multiple access (NOMA) scheme termed partial NOMA (pNOMA) based on the partial sharing of the frequency resources among the users. In particular, we aim to design a spectrally-efficient multi-user downlink scheme under imperfect successive interference cancellation (SIC) and channel state information (CSI) via a joint control on the power allocation and bandwidth overlap for all users. In particular, we formulate the joint optimization problem of the power allocation for each user and channel allocation for overlapped and non-overlapped signals and later solve it using a meta-heuristic search-based differential evolution algorithm. Simulation results validate that the considered pNOMA scheme flexibly integrates OMA and multi-channel NOMA schemes, and outperforms them in terms of the sum rate under stochastic imperfection in SIC and CSI.

Next-generation wireless applications are expected to enable extended ultra-reliable low-latency communication (URLLC) to support high data rates along with ultra-reliability and low-latency features beyond the capabilities of existing core services. There is a need to transition from conventional architectures to more efficient and robust multiple-access schemes to meet these consolidated requirements in resource-constrained systems. This study explores the utilization of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems to enable spectrally efficient URLLC, even under the imperfect channel state information. In particular, we focus on maximizing spectral efficiency by jointly designing robust beamforming at the base station and STAR-RIS subject to given URLLC requirements. Due to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively by exploiting $\mathcal {S}-$ procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode provides better spectral efficiency than other modes owing to its better interference management.

In this paper, an intelligent reflecting surface (IRS) aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated for dead-zone users where the direct link between the users and the base station (BS) is unavailable and the UL transmission is carried out only through IRS. In the considered RSMA system, a message of each user is split into several sub-messages and each part contributes to the rate of that user and depending upon split proportions BS decodes them using appropriate decoding order. The problem of sum-rate maximization is formulated to jointly design the optimal power allocation at each UL user, passive beamforming at the IRS under optimal decoding order of sub-messages. Due to non-convexity and discrete non-linear programming of the formulated problem, the original problem is intractable and hence, we decouple the problem into different sub-problems in which the problems of power allocation and passive beamforming are alternatively solved under using successive convex approximation and Riemaniann conjugate gradient algorithms, respectively. Moreover, the decoding order strategy is analytically derived which confirm that the optimal decoding order strategy depend upon decreasing order of channel gain of users and increasing order of split proportions of sub-messages. Later, the unified solution based on block-coordinate descent (BCD) algorithm is proposed. Simulation results validate that the proposed decoding order scheme attains performance closer to the optimal solution with low computational complexity. Moreover, the proposed IRS aided RMSA system outperforms the system with non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in terms of achievable sum-rate throughput.