Rate-splitting Multiple Access Research Articles

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.

A Joint Multiservice Transmission Scheme for RSMA-Aided Cell-Free mMIMO System

In this letter, we propose a joint multi-service (MS) transmission scheme based on cell-free (CF) massive MIMO architecture. Based on the user-centric (UC) principle, we perform access point (AP) selection and form broadcast, multicast, and unicast messages at each AP according to the rate splitting multiple access (RSMA) technique, then we carry out the joint transmission of MS signals. In terms of system optimization, a novel three-step resource allocation algorithm is proposed to deal with the non-convex problem of achievable sum-rate maximization which downscales multidimensional variables to solve the one-dimensional problems of AP selection, intra-AP power ratio determination, and inter-AP power allocation in steps. We provide achievable sum-rate analyses backed by numerical results to show the superiority of the proposed scheme and optimization algorithm compared with the baselines.

Rate-Splitting Multiple Access for Intelligent Reflecting Surface-Aided Secure Transmission

In this letter, we study a rate-splitting multiple access (RSMA)-based intelligent reflecting surface (IRS)-aided multi-user multiple-input single-output (MISO) secure communication system with a potential eavesdropper (Eve). Aiming to maximize the minimum secrecy rate (SR) among all the legitimate users (LUs), a design problem for jointly optimizing the transmit beamforming with artificial noise (AN), the IRS beamforming, and the secrecy common rate allocation is formulated. Since the design problem is highly non-convex with coupled optimization variables, we develop a computationally efficient algorithm based on the alternating optimization (AO) technique to solve it suboptimally. Numerical results demonstrate that the proposed design can significantly improve the max-min SR over the benchmark schemes without IRS or adopting other multiple access techniques. In particular, employing the RSMA strategy can substantially reduce the required numbers of IRS elements for achieving a target level of secrecy performance compared with the benchmark schemes.

Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.

Rate-Splitting Multiple Access for UAV-Based RIS-Enabled Interference-Limited Vehicular Communication System

Rate-splitting multiple access (RSMA) has emerged as a novel generalized multiple access technology which can control the interference in multi-user communication systems. Reconfigurable intelligent surface (RIS) has also been proven to improve the spectral efficiency in the next-generation (beyond 5 G (B5G) and 6 G) wireless communication networks. Thus, in this paper, we explore the RSMA scheme for RIS-enabled unmanned aerial vehicle (UAV) based multi-user vehicular communication network in the presence of co-channel interference. The communication between the UAV and the desired vehicle takes place through an RIS along with the direct link and is interfered by multiple interfering vehicles operating in the same spectrum. After deriving the approximate cumulative distribution function (CDF) of common and private stream signal-to-interference-plus-noise ratios (SINRs) at the desired vehicle in the interference limited scenario, we obtain an expression for the average outage probability (AOP) over the complete flying duration of the UAV. Furthermore, the optimum RSMA power allocation coefficients in each time slot of UAV flight duration are obtained by minimizing the Sum-AOP over all the desired vehicles. The performance of the proposed RSMA based RIS-aided vehicular communication network is compared with (a) RSMA based network without RIS and (b) non-orthogonal multiple access (NOMA) based RIS-aided counterpart. The numerical results reveal the superiority of RSMA over NOMA and the significance of deploying RIS in considered vehicular communication network. The analytical results are corroborated with Monte Carlo simulations.

Machine Learning for Predictive Deployment of UAVs With Multiple Access

This paper presents a machine learning-based framework for the predictive deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from ground BSs. To account for time-varying traffic distribution, a long short-term memory (LSTM)-based prediction algorithm is introduced to predict future cellular traffic. A joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture models (GMM) is proposed to determine the service area of each UAV based on the predicted user service distribution. Based on the predicted traffic, the optimal positions of UAVs are derived, and four multiple access techniques, namely, rate splitting multiple access (RSMA), frequency domain multiple access (FDMA), time domain multiple access (TDMA), and non-orthogonal multiple access (NOMA), are compared to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Furthermore, RSMA is found to require the lowest transmit power among the four multiple access techniques. Therefore, this paper focuses on the comparison of multiple access techniques for UAV deployment, which is essential for the efficient and effective use of UAVs as flying BSs.

Max-Min Fairness Precoder Design for Rate-Splitting Multiple Access: Impact of Imperfect Channel Knowledge

Rate-splitting multiple access (RSMA) is a key enabling technology for beyond 5G networks due to its robustness against imperfect channel knowledge. In this paper, we consider both the imperfect channel state information (CSI) at the receiver (CSIR) and imperfect CSI at the transmitter (CSIT) and propose a max-min fairness optimization framework relying on RSMA to guarantee each user's minimum level of quality of service (QoS). First, we formulate the max-min fairness optimization problem that has non-convexity. To transform the non-convex problem into a convex problem, we apply the semi-definite relaxation (SDR) and convex-concave procedure (CCP) to the proposed algorithm. From simulation results, we confirm that RSMA is still a robust scheme over conventional multiple access schemes for both imperfect CSIT and CSIR.

Max-min fair RIS-aided rate-splitting multiple access for multigroup multicast communications

Rate-splitting multiple access (RSMA) can cope with a wide range of propagation conditions in multigroup multicast communications through rate splitting optimization. To breakthrough the group-rate limited bottleneck, reconfigurable intelligent surface (RIS) technique can be introduced to assist wireless communications through enhancing the channel quality. In RIS-aided RSMA multigroup multicasting, how to provide fair and high-quality multiuser service under power and spectrum constraints is essential. In this paper, we propose a max-min fair RIS-aided rate-splitting multiple access (MMF-RIS-RSMA) scheme for multigroup multicast communications, where the rate fairness is obtained by maximizing the minimum group-rate. In doing so, we jointly optimize the beamformers, the rate splitting vector at the transmitter, as well as the phase shifts at RIS. To solve it, we divide the original optimization problem into two subproblems and alternately optimize the variables. The beamforming and rate splitting optimization subproblem is solved by using the successive convex approximation technique. The phase shift optimization subproblem is solved through the penalty function method to achieve a rank-one locally optimal solution. Simulations demonstrate that the proposed MMF-RIS-RSMA scheme can obtain significant performance gain in terms of the minimum group-rate.

Using a Software-Defined Air Interface Algorithm to Improve Service Quality

In the digital era, the Narrowband Internet of Things (Nb-IoT) influences the massive Machine-Type-Communication (mMTC) features to establish secure routing among the 5G/6G mobile networks. It supports global coverage to the low-cost IoT devices distributed in terrestrial networks. Its key traffic characteristics include robust uplink, moderate data rate/device, extremely high energy efficiency, prolonging device lifetime, and Quality of Service (QoS). This paper proposes a Deep Reinforcement Learning (DRL) combined software-defined air interface algorithm applied on the switching system, satisfying the user requirement and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric. In this framework, Non-Orthogonal Multiple Accesses (NOMA) and Rate-Splitting Multiple Access (RSMA) are combined to accommodate massive (Nb-IoT) devices that can be utilized the entire resource (frequency band) for tackling the unknown dynamics prohibitive. The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimization. Therefore, it has potential benefits of high scalability, low latency, energy efficiency, and spectrum utility.

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

In this paper, we propose a long short-term memory-based deep learning (DL) architecture for signal detection in uplink and downlink rate-splitting multiple access systems with multi-carrier modulation, over Nakagami-m fading and generalized Gaussian noise (GGN). The proposed DL detector completely eliminates the need for the use of successive interference cancellation (SIC), which suffers from disadvantages such as error propagation. In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard SIC receivers such as the least squares detector and the minimum mean-squared error receiver, and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate (SER). Furthermore, we study the effects of the shaping parameter of GGN, hyperparameters of the DL network such as batch size and learning rate on the SER performance.

Rate-Splitting Multiple Access for Satellite-Terrestrial Integrated Networks: Benefits of Coordination and Cooperation

This paper investigates the joint beamforming design problem to achieve max-min rate fairness in a satellite-terrestrial integrated network (STIN) where the satellite provides wide coverage to multibeam multicast satellite users (SUs), and the terrestrial base station (BS) serves multiple cellular users (CUs) in a densely populated area. Both the satellite and BS operate in the same frequency band. Since rate-splitting multiple access (RSMA) has recently emerged as a promising strategy for non-orthogonal transmission and robust interference management in multi-antenna wireless networks, we present two RSMA-based STIN schemes, namely the coordinated scheme relying on channel state information (CSI) sharing and the cooperative scheme relying on CSI and data sharing. Our objective is to maximize the minimum fairness rate amongst all SUs and CUs subject to transmit power constraints at the satellite and the BS. A joint beamforming algorithm is proposed to reformulate the original problem into an approximately equivalent convex one, which can be iteratively solved. Moreover, an expectation-based robust joint beamforming algorithm is proposed against the practical environment when the satellite channel phase uncertainties are considered. Simulation results demonstrate the effectiveness and robustness of our proposed RSMA schemes for STIN and exhibit significant performance gains compared with various baseline strategies.

On the Ergodic Rate of Cognitive Radio Inspired Uplink Multiple Access

With the exponential increase of the number of devices in the communication ecosystem toward the upcoming sixth generation (6G) of wireless networks, more enabling technologies and potential wireless architectures are necessary to fulfill the networking requirements of high throughput, massive connectivity, ultra reliability, and heterogeneous quality of service (QoS). In this work, we consider an uplink network consisting of a primary user (PU) and a secondary user (SU) and, by integrating the concept of cognitive radio and multiple access, two protocols based on rate-splitting multiple access and non-orthogonal multiple access with successive interference cancellation are investigated in terms of ergodic rate. The considered protocols aim to serve the SU in a resource block which is originally allocated solely for the PU without negatively affecting the QoS of the PU. We extract the ergodic rate of the SU considering a specific QoS for the PU for the two protocols and perform a qualitative and quantitative comparison between the two protocols. In the numerical results, we validate the theoretical analysis and illustrate the superiority of the considered protocols over two benchmark schemes.

Rate-Splitting Multiple Access-Based Satellite–Vehicular Communication System: A Noncooperative Game Theoretical Approach

Rate-Splitting Multiple Access (RSMA) has recently found favor in high-mobility scenarios due to the benefits of relaxing the accuracy of Channel State Information at the Transmitter (CSIT) while maintaining high spectral efficiency. These benefits are particularly important in Satellite-Vehicular Networks (SVNs), where the mobility of the vehicles significantly affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based satellite-vehicular communication system for reliable data transmission. Specifically, we investigate the outage probability of the RSMA-based satellite-vehicular communication system under the Shadowed-Rician model. Considering the satellite-vehicular communication outage problem, an optimization problem is formulated to maximize the Weighted Sum Rate (WSR) of the communication system. However, due to communication overhead and privacy concerns, not all vehicles are willing to participate in interference management. In this study, we exploit an incentive mechanism to efficiently solve this problem. Specifically, we formulated a non-cooperative game to motivate users to report the CSIT and participate in the power allocation. In addition, we have proved the existence and uniqueness of Nash Equilibrium (NE) in the game formulated. Our simulation results show that the proposed non-cooperative game theoretical approach achieves the effectiveness of the RSMA-based satellite-vehicular system. The game theoretical framework is shown to motivate users to participate in interference management, so as to maximize their transmission rates.

Joint Rate Allocation and Power Control for RSMA-Based Communication and Radar Coexistence Systems

We consider a rate-splitting multiple access (RSMA)-based communication and radar coexistence (CRC) system. The proposed system allows an RSMA-based communication system to share spectrum with multiple radars, which significantly improves spectral efficiency, energy efficiency and quality of service (QoS) of communication users (CUs). Due to the spectrum sharing, the communication network and the radars cause interference to each other, which reduces the signal-to-interference-plus-noise ratio (SINR) of the radars as well as the data rate of the CUs. Therefore, a major problem is to maximize the sum rate of the CUs while guaranteeing their QoS requirements of data transmissions and the SINR requirements of multiple radars. To achieve the objective, we formulate a new problem that optimizes i) common rates of the CUs, ii) transmit power of the common message of the CUs, iii) transmit power of the private messages for the intended CUs, and iv) transmit power of the radar systems. The problem is non-convex, and thus we propose two algorithms. The first sequential quadratic programming (SQP) as the state-of-the-art in nonlinear programming method can quickly return a local optimal solution. The second is an additive approximation scheme (AAS) which solves the problem globally in a reasonable amount of time. Simulation results show the significant improvement of the AAS compared with the SQP in terms of sum rate. Furthermore, with the AAS, the sum rate of the CUs only slightly decreases when the radars' SINR is significantly increased. This implies that the AAS supports the RSMA-based communication system which allows to well coexist with the radars.

Outage Performance Analysis of RSMA-Aided Semi-Grant-Free Transmission Systems

In this paper, we analyze the outage performance of a rate-splitting multiple access (RSMA)-aided semi-grant-free (SGF) transmission system, in which a grant-based user (GBU) and multiple grant-free users (GFUs) access the base station by sharing the same resource blocks. In the RSMA-aided SGF (RSMA-SGF) transmission system, the GBU and admitted GFU are respectively treated as the primary and secondary users by using the cognitive radio principle. With the aid of RSMA, the admitted GFU’s transmit power allocation, target rate allocation, and successive interference cancellation decoding order are jointly optimized to attain the maximum achievable rate for the admitted GFU, without deteriorating the GBU’s outage performance compared to orthogonal multiple access. Taking into account the extended non-outage zone achieved by rate-splitting, a closed-form expression is derived for the outage probability of the admitted GFU in the considered RSMA-SGF system. Asymptotic analysis for the admitted GFU’s outage probability is also provided. The superior outage performance and full multiuser diversity gain achieved by the RSMA-SGF transmission system are verified by the analytical and simulation results.

Rate-Splitting Multiple Access in a MISO SWIPT System Assisted by an Intelligent Reflecting Surface

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.