Rate-splitting Multiple Access Research Articles

Energy Efficiency Maximization in RIS-Assisted SWIPT Networks With RSMA: A PPO-Based Approach

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

Cooperative ISAC With Direct Localization and Rate-Splitting Multiple Access Communication: A Pareto Optimization Framework

Integrated sensing and communication (ISAC) has been a promising technology in beyond 5G and 6G network to simultaneously support high-speed information transfer and high-quality environmental perception. Cloud radio access networks (C-RAN) enables the cooperation among multiple base stations (BSs) to provide additional cooperation gains for both communication and sensing functionalities. In this paper, we investigate the cooperative ISAC (CoISAC) system with rate-splitting multiple access (RSMA) transmission scheme for advanced interference management and direct localization sensing scheme to provide high-accuracy positioning services. Specifically, we adopt Pareto optimization framework to characterize the achievable performance region of CoISAC system built on the sum rate of multiple communication users and positioning error bound of radar target. Then, we formulate communication-centric and radar-centric optimization problems in CoISAC system to systematically search for the optimal Pareto boundary of achievable performance region. Two iterative algorithms based on successive convex approximation (SCA) are proposed to effectively solve these two optimization problems, respectively, thereby near-optimal Pareto boundary can be found. Numerical results show that RSMA-assisted CoISAC system achieves the best trade-off performance among various baselines. The cooperative scheme can fully unveil the potential of RSMA in ISAC system by providing more freedom for rate-splitting policy design under limited resources. Therefore, incorporating RSMA in CoISAC system can significantly boost the overall performance.

Dual-Polarized Massive MIMO-RSMA Networks: Tackling Imperfect SIC

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users' data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users' messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.

Rate-Splitting Multiple Access for Uplink Massive MIMO With Electromagnetic Exposure Constraints

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

Evolutionary Games for Dynamic Network Resource Selection in RSMA-Enabled 6G Networks

In this paper, we address a dynamic network resource selection problem for mobile users in a rate-splitting multiple access (RSMA)-enabled network by leveraging evolutionary games. Particularly, mobile users are able to locally and dynamically make their selection on orthogonal resource blocks (RBs), which are also considered as network resources (NRs), over time to achieve their desired utilities. Then, RSMA is used for each group of users selecting the same NR. With the use of RSMA, the main goal is to optimize the beamformers of the common and private messages for users in the same group to maximize their sum rate. The resulting problem is generally non-convex, and thus we develop a successive convex approximation (SCA)-based algorithm to efficiently solve it in an iterative fashion. To model the NR adaptation of users, we propose to use two evolutionary games, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> . a traditional evolutionary game (TEG) and fractional evolutionary game (FEG). The FEG approach enables users to incorporate memory effects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> . their past experiences) for their decision-making, which is more realistic than the TEG approach. We then theoretically verify the existence of the equilibrium of the proposed game approaches. Simulation results are provided to validate their consistency with the theoretical analysis and merits of the proposed approaches. They also reveal that, compared with TEG, FEG enables users to leverage past information for their decision-making, resulting in less communication overhead, while still guaranteeing convergence.

Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels

In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output system (MIMO) operating in frequency-division-duplex (FDD) mode. We propose a statistical precoding design relying on the channels’ second-order information when one-layer RS is implemented. The advantage of the statistical precoding lies in simplifying the precoder design by basing the optimization objective on the slowly-varying channel covariance matrices instead of the fast-changing instantaneous channel estimates. To this end, we use the so-called bilinear precoder, which was shown in [1] and [2] to have limited performance in the high SNR regime due to the imperfect channel state information (CSI) available at the base station (BS). We formulate the DL throughput maximization problem based on a widely used lower bound on the achievable sum rate and propose an iterative approach to solve the underlying optimization problem. Numerical results demonstrate the benefit of implementing one-layer RS. Furthermore, the proposed iterative approach achieves excellent results in terms of spectral efficiency compared to the state-of-the-art techniques.

Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU–LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.

Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff

With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks.

Rate-Splitting Multiple Access Aided Mobile Edge Computing With Randomly Deployed Users

In this paper, a rate-splitting multiple access (RSMA) scheme is proposed to aid a mobile edge computing (MEC) system where multiple randomly deployed users offload their computation tasks to a MEC server. Considering that users are divided into the center and edge groups, a cognitive radio (CR)-inspired rate-splitting is designed to enable the paired users to simultaneously offload to MEC server. Under the CR principles, the rate-splitting parameters are jointly designed to attain the maximum achievable rate for the secondary user, meanwhile maintaining the primary user’s offloading performance same as in orthogonal multiple access. For the case of the paired users with fixed locations, we derive a closed-form expression for the successful computation probability (SCP) achieved by the RSMA-aided MEC (RSMA-MEC) scheme and formulate a SCP maximization problem to obtain the optimal offloading parameters. To reveal the impact of the user locations on the offloading performance of the RSMA-MEC system, various distance-based user pairing schemes are investigated by invoking stochastic geometry techniques. We provide closed-form expressions for the SCPs achieved by the user pairing schemes to characterize the offloading performance. Pros and cons of the user locations to maximize the SCP are highlighted. Simulation results verify the accuracy of the analytical results and clarify the superior offloading performance achieved by the RSMA-MEC scheme, which attains a higher SCP than the existing schemes.

Energy-Efficient RSMA for Multigroup Multicast and Multibeam Satellite Communications

This letter studies energy-efficient rate-splitting multiple access (RSMA) for multigroup multicast and multibeam satellite communication systems. Specifically, we minimize the unmet system capacity, which means the rate difference between the requested user rate and the effective achieved rate, and communication energy consumption by optimizing the power allocation and rate allocation under the per-feed power and sum radiated power constraints. To solve the non-convex optimization problem, auxiliary variables and epigraph method are introduced to transform the original problem into a difference of convex functions programming. Then, the successive convex approximation (SCA) technique is used to obtain a suboptimal solution. Numerical results verify the superiority of our proposed solution compared to the benchmark methods.

Group-Connected Impedance Network of RIS-Assisted Rate-Splitting Multiple Access in MU-MIMO Wireless Communication Systems.

The reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) are considered as promising technologies for the beyond Fifth-Generation (B5G) and Sixth-Generation (6G) wireless systems by controlling the propagation environment, which attenuates the transmitted signal, and by managing the interference by splitting the user message into common and private messages. Because conventional RIS elements have each impedance connected to the ground, the sum-rate performance improvement of the RIS is limited. Therefore, the new RISs, which have impedance elements connected to each other, have been proposed recently. To be more adaptive to each channel, the optimization of the grouping of the RIS elements is required. Furthermore, since the solution of the optimal rate-splitting (RS) power-splitting ratio is complex, the value should be simply optimized to be more practical in the wireless system. In this paper, the grouping scheme of the RIS elements according to the user scheduling and the solution of the RS power-splitting ratio based on fractional programming (FP) are proposed. The simulation results showed that the proposed RIS-assisted RSMA system achieved a high sum-rate performance compared to the conventional RIS-assisted spatial-division multiple access (SDMA) system. Therefore, the proposed scheme can perform adaptively for the channel and has a flexible interference management. Furthermore, it can be a more suitable technique for B5G and 6G.

Joint Power Allocation and Rate Control for Rate Splitting Multiple Access Networks With Covert Communications

Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.

Rate Splitting Multiple Access for Sum-Rate Maximization in IRS Aided Uplink Communications

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.

Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems

With the explosive increase in demand for wireless communication, the issue of wireless communication security has also become a growing concern. In this paper, we investigate a novel covert communication for unmanned aerial vehicle (UAV)-assisted uplink rate-splitting multiple access (RSMA) systems, where a UAV adopts the rate-splitting (RS) strategy to increase the total transmission rate while avoiding deteriorating the covert transmission of a ground user. In the proposed system, a ground user and a UAV adopt the RSMA scheme to simultaneously communicate with a base station surveilled by an evil monitor. The UAV acts as both the transmitter and the friendly jammer to cover the ground user’s transmission with random power. To maximize the expected sum rate (ESR), we first study the RS strategy and obtain the optimal power allocation factor. Then, the closed-form of minimum detection error probability (DEP), ESR, and optimal target rate of the UAV are derived. Constrained by the minimum DEP and expected covert rate (ECR), we maximize the ESR by optimizing the position and target rate of the UAV. Numerical results show that the proposed scheme outperforms the traditional NOMA systems in terms of ESR with the same DEP and ECR.

Rate-Splitting Multiple Access for Downlink MIMO: A Generalized Power Iteration Approach

Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access. Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem. In this paper, we put forth a novel precoder design framework that jointly finds the linear precoders for the common and private messages for RSMA. Our approach is first to approximate the non-smooth minimum function part in the sum spectral efficiency of RSMA using a LogSumExp technique. Then, we reformulate the sum spectral efficiency maximization problem as a form of the log-sum of Rayleigh quotients to convert it into a tractable form. By interpreting the first-order optimality condition of the reformulated problem as an eigenvector-dependent nonlinear eigenvalue problem, we reveal that the leading eigenvector of the derived optimality condition is a local optimal solution. To find the leading eigenvector, we propose an algorithm inspired by a power iteration. Simulation results show that the proposed RSMA transmission strategy provides significant improvement in the sum spectral efficiency compared to the state-of-the-art RSMA transmission methods.

Capacity Analysis of Intelligent Reflecting Surface assisted RSMA System with Perfect and Imperfect CSI for 6G

Rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) are the promising candidates and front runners for 5G and beyond wireless communication. Recent studies have proved RSMA-IRS outperforms non-orthogonal multiple access (NOMA) and enhances the quality of service (QoS). The performance of the downlink RSMA-IRS system is investigated in this paper using perfect and imperfect channel state information (CSI) conditions over the Rayleigh fading channel. Furthermore, the numerical analysis for the sum rate capacity is carriedout for the users under perfect and imperfect conditions. To assess the system’s performance, two distinct scenarios are explored. Initially, the system total capacity of each user’s common and private parts is determined without considering the channel estimate error (CEE). The total capacity for each user’s common part is 10 b/s, for private1 is 8.9 b/s, and for private2 is 6.5 b/s. After introducing CEE, the sum rate for the common part is 8.5 b/s, and the total capacity for private1 and private2 is 7.5 b/s and 6 b/s, respectively. The simulation results show that the perfect CSI, achieves enhanced sum rate than channel with CEE. however, to tolerate the imperfect scenario (CEE) optimum power allocation is determined for attaining the QoS.

Outage Analysis of Millimeter Wave RSMA Systems

Millimeter-wave (mmWave) communication has attracted considerable attention from academia and industry, providing multi-gigabits per second rates due to the substantial bandwidth. Rate splitting multiple access (RSMA) is an effective technology that provides a generalized multiple access framework. Regarding the new propagation characteristics of the mmWave, we investigate the outage performance of the mmWave RSMA multiple-input-single-output system with a fixed-located user and a randomly-located user. Based on the spatial correlation of the paired users, the user’s paths are divided into overlapped and non-overlapped paths. Two beamforming schemes are proposed to improve the reliability of the mmWave RSMA system. The common stream is transmitted on the overlapped paths or all the paths. By utilizing stochastic geometry theory, the closed-form expressions of the outage probability (OP) with proposed schemes are derived. To obtain more insights, the expressions for the asymptotic OP are derived. Monte Carlo simulation results are presented to validate the analysis and the effects of the system parameters, such as power allocation coefficients and the number of resolvable paths, on the outage performance are investigated.

Resource Allocation for RSMA-Based Coordinated Direct and Relay Transmission

In this letter, we investigate a rate splitting multiple access (RSMA) based coordinated direct and relay transmission (CDRT) system, where a base station (BS) serves two users using the rate splitting strategy, and a decode-and-forward (DF) relay aids the BS in establishing a communication link with a far user. We focus on a max-min fairness problem aiming at maximizing the minimum achievable rate of two users. The precoding vectors of the BS and the relay, the common rate of each user and the time allocation coefficients are jointly optimized under the constraint of limited system resources. Unfortunately, the optimization problem is non-convex due to the coupled optimization variables and the non-convex objective. Therefore, we transform the original problem into a difference of convex (DC) program and obtain a sub-optimal solution by adopting an iterative constrained concave convex procedure (CCCP) based algorithm. Simulation results demonstrate substantial performance gains of our proposed RSMA-CDRT over the conventional CDRT strategies and the flexibility in adapting to various channel environments.

Max–Min Fairness Beamforming With Rate-Splitting Multiple Access: Optimization Without a Toolbox

We propose a max-min fairness (MMF) design method in a multi-antenna downlink network, where rate-splitting multiple access (RSMA) is adopted. A main aim in the considered MMF problem is providing uniformly good rates to users, by carefully controlling the rate of each private and common message, respectively. Solving this problem is challenging since multiple optimization variables, i.e., beamforming vectors and common message portions, are intricately intertwined in a non-tractable objective function. To resolve these obstacles, we first split a whole problem into two stages. In the first stage, we identify beamforming vectors given common message portion by exploiting the LogSumExp (LSE) approximation technique and the novel generalized power iteration (GPI) framework. In the second stage, we determine common message portions under fixed beamforming vectors. Iterating these two stages, we jointly design beamforming vectors and common message portion accordingly. Via simulations, we demonstrate that our method outperforms existing other frameworks in terms of the minimum rate, while requiring extremely small computational complexity.

Rate-Splitting Multiple Access for Short-Packet Uplink Communications: A Finite Blocklength Analysis

In this letter, we investigate the performance of Rate-Splitting Multiple Access (RSMA) for an uplink communication system with finite blocklegnth (FBL). Considering a two-user Single-Input Single-Output (SISO) Multiple Access Channel (MAC), we study the impact of blocklength and target rate on the throughput and error probability performance of RSMA where one user message is split. We demonstrate that RSMA can outperform Non-Orthogonal Multiple Access (NOMA) in terms of throughput and error probability performance.