Rate-splitting Multiple Access Research Articles

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Rate-Splitting Multiple Access With Finite Blocklength and High Mobility for URLLC Transmissions

Rate-Splitting Multiple Access With Finite Blocklength and High Mobility for URLLC Transmissions

Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-Assisted URLLC Systems

Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-Assisted URLLC Systems

Joint optimal beam forming and resource allocation in intelligent reflecting surface aided wireless power transfer rate splitting multiple access system

SummaryThe intelligent reflecting surface (IRS) has recently become the most promising technology to achieve maximum beam‐forming gain with a simultaneous wireless information and power transfer (SWIPT) system. Many existing studies perform a single IRS deployment or utilize space division multiple access (SDMA) and non‐orthogonal multiple access (NOMA) schemes. However, the existing schemes face high time complexity and block signal transmission for larger indoor communications. Hence, this article introduces a dual IRS‐aided multiuser SWIPT system by implementing rate‐splitting multiple access (RSMA) systems. To enhance the minimum achievable rate and ensure the minimum harvesting energy (HE), this study proposes a joint successive convex approximation with an integrated optimization algorithm (SCA‐IOA). The proposed algorithm jointly optimizes the reflecting beam forming and power splitting (PS) coefficient of all the users effectively. The simulation results demonstrate the effectiveness of the proposed techniques and produce an outstanding performance for deploying dual IRS and implementing RSMA compared to traditional SDMA and NOMA systems.

Covert communications in STAR-RIS-aided rate-splitting multiple access systems

In this paper, an simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is deployed in the downlink rate-splitting multiple access (RSMA) covert system to enhance the covert communication performance. According to the RSMA transceiver mechanism, the messages for the covert user (Bob) and public user (Grace) are converted to the common and private streams at the legitimate transmitter (Alice) to realize downlink transmissions, while the STAR-RIS not only assists Alice in transmitting public messages to Grace but also protects the covert messages Alice sends to Bob by mixing up Warden (Willie). To characterize the covert performance of the considered STAR-RIS-aided RSMA (STAR-RIS-RSMA) system, We derive a closed expression for its detection error probability in the most favorable case for Willie, based on which a covert rate maximization problem is formulated. To maximize Bob’s covert rate while confusing Willie’s monitoring, the power allocation factor, common rate allocation factor, and STAR-RIS reflection/transmission beamforming are jointly optimized subject to Grace’s quality of service (QoS) requirements. The complex non-convex problem of maximizing covert rate, involving coupled system parameters, thus is split into three separate sub-issues: the allocation of transmit power, allocation of common rates, and STAR-RIS reflection/transmission beamforming, respectively. To obtain the rank-one constrained optimal solution for the sub-problem of optimizing the STAR-RIS reflection/transmission beamforming, a penalty-based successive convex approximation scheme is developed. Moreover, an alternative optimization (AO) algorithm is designed to determine the optimal solution for the sub-problem of optimizing the transmit power allocation, while the original problem is overall solved by a new AO algorithm. Simulation results corroborate the accuracy of the derived analytical results and demonstrate that the proposed STAR-RIS-RSMA scheme outperforms the benchmark scheme in achieving the covert rate.

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.

A sum rate maximization problem in uplink MIMO with RSMA systems

This study explores the problem of maximizing the sum rate in uplink multi-user Multiple-Input Multiple-Output (MIMO) using Rate-Splitting Multiple Access (RSMA) systems. The investigation revolves around the scenario where the Users (UEs) are single-antenna nodes transmitting data to a multi-antenna Base Station (BS) through the RSMA technique. The optimization process encompasses determining parameters such as UEs’ transmit powers, decoding order, and detection vector at the BS. An approach based on Deep Reinforcement Learning (DRL) is introduced to address this challenge. This DRL framework involves an action-refined stage and applies a Deep Deterministic Policy Gradient (DDPG)-based strategy. Simulation outcomes effectively demonstrate the convergence of the proposed DRL framework, where it converges after approximately 1,800 episodes. Also, the results prove the superior performance of the proposed method when compared to established benchmark strategies, where it is up to 45% and 86% higher than the local search and random schemes, respectively.

Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

Rate-Splitting Multiple Access in Wireless Backhaul HetNets: A Decentralized Spectral Efficient Approach

Rate-Splitting Multiple Access in Wireless Backhaul HetNets: A Decentralized Spectral Efficient Approach

Securing RSMA-Based Communications at Physical Layer

Rate-splitting multiple access (RSMA) has emerged as a promising enabling technique for next generation wireless communications. Its unique structure and powerful interference management capability has brought new challenges and opportunities to safeguarding communications at the physical layer. In this article, we review security issues in the presence of internal and external eavesdroppers, respectively. Some mainstream techniques for addressing the introduced security concerns are presented. The role of optimizing the successive interference cancellation (SIC) decoding order in securing RSMA is further discussed explicitly. Finally, some open problems and research directions are envisioned.

Network Slicing for eMBB, URLLC, and mMTC: An Uplink Rate-Splitting Multiple Access Approach

Network Slicing for eMBB, URLLC, and mMTC: An Uplink Rate-Splitting Multiple Access Approach

Joint wireless resource allocation and bitrate adaptation for QoE improvement in IRS-aided RSMA-enabled IoMT streaming systems

In the context of the increasing demand for Internet of Multimedia Things (IoMT) services, rate splitting multiple access (RSMA) and intelligent reflecting surface (IRS) technologies have been considered potential networking enablers to provide ultra-throughput wireless access. However, challenges arise due to the heterogeneity of IoMT devices and arbitrary network quality changes, resulting in unwanted service quality fluctuations and downgradation. This study addresses this problem by jointly optimizing wireless resource allocation and bitrate adaptation with Deep Reinforcement Learning (DRL)-based QoE management for IRS-aided RSMA-enabled IoMT streaming systems. We formulated the problem as a Markov decision process (MDP) and apply Proximal Policy Optimization (PPO) method to flexibly adjust IoMT bitrate, transmission beamforming, IRS phase shift, and RSMA parameters. As a result, our algorithm mitigates overestimation of client-side bandwidth, leading to smoother playback and reduced quality fluctuations. Simulations show that our approach outperforms baseline methods in terms of video resolution (up to 2.5 times) and achievable sum-rate (up to 50%), contributing to a superior streaming experience in IoMT systems.

Effective capacity of rate-splitting multiple access with channel estimation errors

In view of its advantages to adeptly reduce interference and low complexity, rate-splitting multiple access (RSMA) is emerging as an innovative framework for the next generation of the wireless communication networks. In this paper, we conduct the performance analysis of a two user RSMA network under delay constrains, taking channel estimation errors into account as well. Utilizing the effective capacity (EC) as our metric, we investigate the effects of delay on a pair of RSMA users. Specifically, we derive analytical expressions for the EC concerning both the near user (SRn) and the far user (SRf). Additionally, we offer asymptotic results under high signal-to-noise ratio (SNR) conditions. From our analysis, several noteworthy conclusions can be drawn and outlined below: (1) The effective capacities (ECs) of SRn and SRf exhibit improvement with SNR increasing, while at high SNR, the ECs of SRn and SRf converge to the constants. (2) Compared to the non-orthogonal multiple access, the considered RSMA network exhibits superior EC performance due to its inherent advantages in interference management. (3) The presence of channel estimation errors adversely affects EC performance of RSMA networks, with this impact being more pronounced in the high SNR regime.

Performance analyses of intelligent reflecting surface aided downlink multi-user rate-splitting multiple access system for 6G applications

The future wireless networks will handle extremely high data rates and fundamentally new applications, demanding a paradigm leap in wireless radio technology. The combination of rate-splitting multiple access (RSMA) and an intelligent reflecting surface (IRS) is an efficient way for improving the spectral/energy efficiency of next-generation wireless networks, such as 5G and beyond and 6G. In this paper, we propose an IRS-assisted downlink multi-user RSMA system towards 6G applications. Near users (NU) and cell edge users (CEU) are the two types of users considered in the proposed system, with NUs directly connected to the BS and CEUs connected via IRS. During transmission, the IRS acts as a relay and we investigate the performance of an IRS-aided RSMA system in terms of sum rate and outage probability under various NU and CEU scenarios. The novelty of the work is to derive the sum rate of the system model over Rayleigh and Rician fading channel we also derive the outage probability expression of the system for direct and indirect transmission to the CEUs and direct transmission to the NUs. Here we plot and analyzed the sum rate and outage probability results by varying the user power allocation, transmit power, transmit signal to noise ratio (SNR) ρB and other parameters. The results show that at 7dBm of transmit power, the NU achieves an outage probability greater than 10−1 and the CEU achieves an outage probability less than 10−2.

Rate-Splitting Multiple Access for Semi-Grant-Free Transmissions With MMD-Aided Grant-Based Users

Rate-Splitting Multiple Access for Semi-Grant-Free Transmissions With MMD-Aided Grant-Based Users

Coordinated Rate-Splitting Multiple Access for Integrated Satellite-Terrestrial Networks With Super-Common Message

Coordinated Rate-Splitting Multiple Access for Integrated Satellite-Terrestrial Networks With Super-Common Message

A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA

Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA

Synergizing Beyond Diagonal Reconfigurable Intelligent Surface and Rate-Splitting Multiple Access

Synergizing Beyond Diagonal Reconfigurable Intelligent Surface and Rate-Splitting Multiple Access

Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-Assisted Networks With Rate-Splitting Multiple Access

Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-Assisted Networks With Rate-Splitting Multiple Access