The max-min fairness (MMF) problem in rate-splitting multiple access (RSMA) is known to be challenging due to its non-convex and non-smooth nature, as well as the coupled beamforming and common rate variables. Conventional algorithms to address this problem often incur high computational complexity or degraded MMF rate performance. To address these challenges, in this work, we propose a novel optimization algorithm named extragradient-fractional programming (EG-FP) to address the MMF problem of downlink RSMA. The proposed algorithm first leverages FP to transform the original problem into a block-wise convex problem. For the subproblem of precoding block, we show that its Lagrangian dual is equivalent to a variational inequality problem, which is then solved using an extragradient-based algorithm. Additionally, we discover the optimal beamforming structure of the problem and based on which, we introduce a low-dimensional EG-FP algorithm with computational complexity independent of the number of transmit antennas. This feature is especially beneficial in scenarios with a large number of transmit antennas. The proposed algorithms are then extended to handle imperfect channel state information at the transmitter (CSIT). Numerical results demonstrate that the MMF rate achieved by our proposed algorithms closely matches that of the conventional successive convex approximation (SCA) algorithm and significantly outperforms other baseline schemes. Remarkably, the average CPU time of the proposed algorithms is less than 10% of the runtime required by the SCA algorithm, showing the efficiency and scalability of the proposed algorithms.

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MU-MIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

Beyond Diagonal Reconfigurable Intelligent Surfaces (BD-RIS) has emerged as a promising evolution of RIS technology. By enabling interconnections between RIS elements, BD-RIS architectures offer greater flexibility in wave manipulation compared to traditional diagonal RIS designs. However, these interconnections introduce new research challenges for channel estimation, making existing approaches developed for conventional diagonal RISs ineffective and significantly increasing pilot overhead. To address these challenges, we propose a novel individual channel estimation framework that separately estimates the BS-RIS channel, which typically remains static over time, and the RIS-user channels, which vary rapidly due to user mobility. Specifically, we develop a full-duplex (FD) approach to estimate the BS-RIS channel by leveraging its inherent sparsity. Following this, the RIS-user channels are estimated using a least squares (LS) approach. Numerical results demonstrate that the proposed framework achieves significantly higher channel estimation accuracy, particularly when the number of RIS elements is large, while substantially reducing pilot overhead compared to conventional cascaded channel estimation methods.

This paper initiate the application of rate splitting multiple access (RSMA) for simultaneous lightwave information and power transfer (SLIPT), where users require to decode information and harvest energy. We focus on a time-splitting (TS) mode where information decoding and energy harvesting are separated in two different phases. Based on the proposed system model, we design a constrained-concave-convex programming (CCCP) algorithm to solve the optimization problem of maximizing the worst-case rate among users subject to the harvested energy constraint at each user. Specifically, the proposed algorithm exploits transformation of the bilinear function, semidefinite relaxation (SDR), CCCP, and a penalty method to effectively deal with the non-convex constraints and objective function. Numerical results show that our proposed RSMAaided SLIPT outperforms the existing baselines based on spacedivision multiple access (SDMA) and non-orthogonal multiple access (NOMA).

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has garnered significant research interest recently due to its ability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide enhanced performance through flexible inter-connection among RIS elements. However, current BD-RIS designs often face challenges related to high circuit complexity and computational complexity, and there is limited study on the trade-off between system performance and circuit complexity. To address these issues, in this work, we propose a novel BD-RIS architecture named Q-stem connected RIS that integrates the characteristics of existing single connected, tree connected, and fully connected BD-RIS, facilitating an effective trade-off between system performance and circuit complexity. Additionally, we propose two algorithms to design the RIS scattering matrix for a Q-stem connected RIS aided multiuser broadcast channels, namely, a low-complexity least squares (LS) algorithm and a suboptimal LS-based quasi-Newton algorithm. Simulations show that the proposed architecture is capable of attaining the sum channel gain achieved by fully connected RIS while reducing the circuit complexity. Moreover, the proposed LS-based quasi-Newton algorithm significantly outperforms the baselines, while the LS algorithm provides comparable performance with a substantial reduction in computational complexity.

Recent advancements in space computing power networks, particularly the integration of onboard computing capabilities in Low Earth Orbit (LEO) satellites, have paved the way for federated learning (FL) in satellite networks. Despite its potential, satellite FL faces unique challenges, such as the dynamic nature of satellite networks and the instability of inter-orbit communication links, which complicate global model aggregation. To address these challenges, we explore FL over multi-layer satellite networks, incorporating LEO, Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO) satellites. Specifically, by modeling the dynamic network as a series of time-varying graph snapshots, we propose a novel topology-aware FL framework. To optimize the aggregation routing in the multi-layer satellite network, we leverage the directed minimum spanning tree (DMST) problem in graph theory and introduce a communication-efficient satellite aggregation routing algorithm (CESAR), which effectively reduces communication overhead and aggregation delays, ensuring efficient training and model updates across the satellite network. Extensive experimental results validate the efficacy of the proposed framework, demonstrating its potential to overcome the inherent challenges of satellite FL and significantly advance the capabilities of multi-layer satellite networks.

Low-earth orbit (LEO) satellite Internet of Things (IoT) has emerged as a promising solution to address the limitations of terrestrial IoT by providing global coverage and seamless connectivity. Among the various techniques enhancing LEO satellite IoT, beam hopping (BH) stands out as an efficient approach that dynamically adjusts beam illumination to match the varying traffic demands of diverse IoT devices. This flexibility enables optimal utilization of limited on-board resources. However, while BH allows adaptive beam illumination planning, it can also introduce severe inter-beam interference, particularly when adjacent beams are simultaneously activated. To address this challenge, we propose a novel rate-splitting multiple access (RSMA)-enabled cluster-based beam hopping (CBH) LEO satellite IoT system. By leveraging RSMA, the proposed framework supports large-scale IoT devices access, and mitigates inter-beam interference introduced by CBH. Within this framework, we introduce a metric—the ratio of offered capacity to traffic demand (ROCD)—to quantify how well the required traffic sum rate aligns with the achievable sum rate for each beam. We then focus on jointly optimizing the precoding vector, common rate allocation, and CBH pattern design to maximize the worst-case ROCD among beams. To solve this problem efficiently, we decompose the original problem into three sub-problems and propose a two-stage algorithm. Numerical results demonstrate that our proposed scheme improves the minimum satisfaction rate by 14.10% and 39.59% compared to the non-orthogonal multiple access and space-division multiple access baselines, achieving effective interference mitigation.

This article addresses the robust beamforming design for rate splitting multiple access (RSMA)-aided visible light communication (VLC) networks with imperfect channel state information at the transmitter (CSIT). In particular, we first derive the theoretical lower bound for the channel capacity of RSMA-aided VLC networks. Then we investigate the beamforming design to solve the max–min fairness (MMF) problem of RSMA-aided VLC networks under the practical optical power constraint and electrical power constraint while considering the practical imperfect CSIT scenario. To address the problem, we propose a constrained-concave-convex programming (CCCP)-based beamforming design algorithm which exploits semidefinite relaxation (SDR) technique and a penalty method to deal with the rank-one constraint caused by SDR. Numerical results show that the proposed robust beamforming design algorithm for RSMA-aided VLC network achieves a superior performance over the existing ones for space-division multiple access (SDMA) and nonorthogonal multiple access (NOMA).

Rate-splitting multiple access (RSMA) is gaining increasing recognition as a pivotal technology for advancing green communication networks, primarily due to its proficiency in boosting energy efficiency (EE) and lowering power consumption at the transmitter. In this article, we commence by offering a concise overview of the latest advancements in RSMA for green communications. Motivated by the limitations identified in existing studies, we then focus on robust beamforming design of RSMA to optimize the ergodic EE with imperfect channel state information at the transmitter (CSIT). We first introduce an enhanced successive convex approximation (ESCA) algorithm, which expands upon the traditional successive convex approximation (SCA) approach for maximizing EE with perfect CSIT and adapts it to the imperfect CSIT scenario. To further reduce the computational complexity, we develop a novel and efficient beamforming optimization algorithm to tackle the ergodic EE problem. A key feature of our proposed approach is the use of the semi-closed-form optimal beamforming structure identified for the ergodic EE problem. Subsequently, we propose a fixed-point-iteration (FPI)-based algorithm to determine the optimal Lagrange dual variables within the optimal beamforming structure. Numerical results show that both proposed algorithms achieve near-optimal solutions and the efficient semi-closed-form optimization algorithm remarkably reduces the computational complexity. Moreover, this study is the first to present an extensive numerical comparison of the ergodic EE between RSMA and other baseline multiple access techniques under imperfect CSIT. These results further highlight the superior EE gains offered by RSMA, reinforcing its potential as a key enabler for green communication networks.

This paper explores the potential of employing rate-splitting multiple access to enhance the achievable rate and energy efficiency (EE) of an uplink cell-free massive multiple-input multiple-output (MIMO) system, where the access points (APs) are configured with low-resolution analog-to-digital converters (ADCs) to minimize the hardware expense and power consumption. Taking the large-scale fading decoding, ADC quantization, and imperfect successive interference cancellation into consideration, a rigorous closed-form rate expression is derived within Ricean fading environments. This analytical framework facilitates an in-depth analysis of the rate performance with respect to various system parameters. To quantify the benefits of low-resolution ADCs, a power consumption model is subsequently incorporated into the analysis, facilitating an evaluation of the system’s EE. Furthermore, the optimization of power control coefficients and receiver weights is tackled through the formulation of weighted sum-rate (WSR) and EE maximization problems. Two efficient alternative algorithms are then proposed to determine their optimal solutions. The theoretical propositions and the efficacy of the proposed WSR and EE optimization algorithms are substantiated through comprehensive simulations.

Rate-splitting multiple access (RSMA) emerges as a compelling physical-layer transmission paradigm for effectively managing interference in 6G networks. Within the realm of RSMA transmission frameworks, generalized rate-splitting (GRS) stands out as a versatile strategy that embraces existing multiple access (MA) schemes, including space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and orthogonal multiple access (OMA) as specific instances. Despite its versatility, GRS encounters significant design challenges, particularly in dealing with the resource optimization complexities resulting from the exponential growth in the number of common streams with the number of users. To tackle the issue, in this work, we propose a novel and highly efficient beamforming optimization algorithm for GRS to maximize the ergodic sum rate (ESR) with imperfect channel state information at the transmitter (CSIT). Specifically, the stochastic ESR maximization problem is first transformed into a deterministic one using sampled average approximation (SAA). This transformed problem is further decomposed into a series of convex subproblems by the fraction programming (FP) approach. Based on the Karush-Kuhn-Tucker (KKT) conditions of each subproblem, we derive the optimal beamforming structure (OBS) of GRS. To determine the Lagrange dual variables within the OBS, we then propose a fixed point iteration (FPI)-based method. Through extensive numerical results, we show that the proposed algorithm significantly reduces the computational complexity without sacrificing ESR performance compared to conventional optimization algorithms. Thanks to the efficiency of our algorithm, we illustrate, for the first time, the performance of GRS with more than three users. We draw the conclusion that our proposed algorithm shows promise in advancing the practical application of RSMA in 6G.

Extremely Large-scale Antenna Array (ELAA) is increasingly recognized as a promising solution for enhancing spectral efficiency and spatial resolution in the 6G mobile system. However, realizing these benefits necessitates the development of sophisticated interference management strategies, which typically rely on perfect Channel State Information at the Transmitter (CSIT) and involve computationally intensive operations. In real-world scenarios, perfect CSIT is typically infeasible due to inherent channel estimation errors and hardware impairments, which also lead to imperfect Successive Interference Cancellation (SIC). Additionally, the computational complexity associated with precoding schemes poses a formidable challenge. To address these issues, this study proposes a Deep Learning (DL)-assisted Rate-Splitting Multiple Access (RSMA) scheme for ELAA systems. The primary objective is to maximize the geometric mean of ergodic user-rates under imperfect CSIT and SIC, thereby optimizing both fairness and system throughput. Given the prohibitively high computational complexity of conventional optimization approaches to address this optimization problem, we introduce a DL model, named GruCN, to optimize precoder design. Simulation results demonstrate that the proposed RSMA-enabled ELAA system achieves better performance in terms of fairness and robustness under imperfect CSIT. Moreover, the GruCN model exhibits remarkable efficiency and effectiveness in precoder optimization.

Despite the allure of ubiquitous, high-speed, and low-latency connectivity offered by Space-Air-Ground Integrated Networks (SAGINs), the co-existence of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) within the same frequency band poses significant challenges in interference management. Traditional optimization approaches, requiring seconds or even minutes for beamforming design, simply cannot keep pace with this dynamic environment. This work addresses these challenges by proposing a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA), which enables efficient cross-system interference management in SAGINs with statistical Channel State Information (CSI) at the Transmitter (CSIT). Building upon FD-RSMA, we study the precoder design of LEO satellites and UAVs along with common rate allocations of RSMA to maximize Weighted Ergodic Sum Rate (WESR). To handle channel randomness, we employ a Sample Average Approximation (SAA) approach. Furthermore, a Deep Learning (DL)-based precoder design algorithm, called GruCN, which marries the advantages of Gate Recurrent Unit (GRU) and Convolutional Neural Network (CNN), is proposed to efficiently tackle the non-convex optimization problem. Numerical results demonstrate the effectiveness and efficiency of our proposed DL-assisted FD-RSMA. Compared to conventional RSMA approaches, FD-RSMA improves up to 20% of WESR performance, while the GruCN achieves around 50% higher WESR performance and up to four orders of magnitude lower processing time than the conventional optimization approaches.

In this paper, we aim at maximizing the weighted sum-rate (WSR) of rate splitting multiple access (RSMA) in multi-user multi-antenna transmission networks through the joint optimization of rate allocation and beamforming. Unlike conventional methods like weighted minimum mean square error (WMMSE) and standard fractional programming (FP), which tackle the non-convex WSR problem iteratively using disciplined convex subproblems and optimization toolboxes, our work pioneers a novel toolbox-free approach. For the first time, we identify the optimal beamforming structure and common rate allocation for WSR maximization in RSMA by leveraging FP and Lagrangian duality. Then we propose an algorithm based on FP and fixed point iteration to optimize the beamforming and common rate allocation without the need for optimization toolboxes. Our numerical results demonstrate that the proposed algorithm attains the same performance as standard FP and classical WMMSE methods while significantly reducing computational time.

Cell-free (CF) massive multiple-input multiple-output (MIMO) system has emerged as a highly promising technology, primarily due to its ability to improve coverage and performance. However, one of the key challenges is their reliance on perfect channel state information (CSI). To address this issue, we propose the incorporation of a rate-splitting (RS) strategy, which has been proven to effectively mitigate the negative impact of imperfect CSI. In this paper, we investigate CF massive MIMO systems that utilize the RS strategy. We derive a closed-form expression for the RS-assisted CF massive MIMO system in the uplink, while accounting for pilot contamination. We also present four decoding schemes that can be implemented in practical systems. Our extensive simulations reveal that CF massive MIMO systems utilizing the RS strategy outperform those that do not in terms of sum spectral efficiency (SE). These findings emphasize the effectiveness of RS technology in mitigating the negative effects of imperfect CSI in CF massive MIMO systems. The insights gained from this research can serve as a basis for the design and optimization of future CF massive MIMO systems, ultimately improving their performance and expanding their applicability in diverse scenarios.

In this paper, we propose a multi-device co-inference system based on a task-oriented over-the-air computation (Air-Comp) via reconfigurable intelligent surface (RIS). Specially, local feature vectors extracted from the real-time noisy sensory data on devices are aggregated over-the-air by exploiting the waveform superposition in a multi-user channel. Then the aggregated features received at the server are fed into an inference model for decision making or control of actuators. Based on the proposed multi-device co-inference system, we jointly optimize the receive signal strength of the device, the beamforming vector, and RIS phase shifts to suppress the sensing and channel noise and maximize the inference accuracy. To solve the problem, we first transform the original problem into a convex difference (d.c.) problem, and convert the d.c. problem from the complex domain to the real domain. Then, we propose a successive convex approximation based approach to solve the problem in the real domain. With the supportive data and results from the application of human motion recognition, we show the proposed scheme achieves a higher inference accuracy then the conventional approaches.

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.

Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer interference management scheme for spectrum sharing in heterogeneous satellite networks with statistical Channel State Information (CSI) at the Transmitter (CSIT) and Receivers (CSIR). In the proposed scheme, Rate-Splitting Multiple Access (RSMA), as a general and powerful framework for interference management and multiple access strategies, is implemented distributedly at Geostationary Orbit (GEO) and LEO satellites, coined Distributed-RSMA (D-RSMA). By doing so, D-RSMA aims to mitigate the interference and boost the user fairness of the overall multilayer satellite system. Specifically, we study the problem of jointly optimizing the GEO/LEO precoders and message splits to maximize the minimum rate among User Terminals (UTs) subject to a transmit power constraint at all satellites. A robust algorithm is proposed to solve the original non-convex optimization problem. Numerical results demonstrate the effectiveness and robustness towards network load and CSI uncertainty of our proposed D-RSMA scheme. Benefiting from the interference management capability, D-RSMA provides significant max-min fairness performance gains compared to several benchmark schemes.

Rate-splitting multiple access (RSMA) has emerged as a potent paradigm shift in wireless communications, demonstrating resilience to channel state information (CSI) inaccuracies and significant rate enhancements. This work investigates RSMA’s application within the context of ultra-reliable and low-latency communication (URLLC) for the forthcoming Internet-of-Everything networks. Specifically, we integrate RSMA with a cell-free massive multiple-input multiple-output (MIMO) architecture to support URLLC demands. Considering the imperfect CSI, attributable to pilot contamination and thermal noise, we derive rigorous lower-bound expressions for the downlink achievable rates. These expressions are applicable to short-packet communication scenarios and RSMA strategy over spatially correlated Rician fading channels. Utilizing these analytical expressions, we perform an exhaustive rate performance evaluation, varying system parameters such as the numbers of pilots, access points (APs), devices, and antennas per AP, alongside different multiple access techniques. Furthermore, we address the power control coefficient design for both common and private streams, framing it as an optimization problem aimed at maximizing the weighted sum-rate and enhancing URLLC service quality. To tackle this non-convex challenge, we introduce a geometric programming-based path-following algorithm, which iteratively converges to the solution. The theoretical underpinnings and the efficacy of the proposed power optimization algorithm are corroborated through extensive simulation results.

Joint optimization for common rate allocation and beamforming design have been widely studied in rate splitting multiple access (RSMA) empowered multiuser multi-antenna transmission networks. Due to the highly coupled optimization variables and non-convexity of the joint optimization problems, emerging algorithms such as weighted minimum mean square error (WMMSE) and successive convex approximation (SCA) have been applied to RSMA which typically approximate the original problem with a sequence of disciplined convex subproblems and solve each subproblem by an optimization toolbox. While these approaches are capable of finding a viable solution, they are unable to offer a comprehensive understanding of the solution structure and are burdened by high computational complexity. In this work, for the first time, we identify the optimal beamforming structure and common rate allocation for the weighted sum-rate (WSR) maximization problem of RSMA. We then propose a computationally efficient optimization algorithm that jointly optimizes the beamforming and common rate allocation without relying on any toolbox. Specifically, we first approximate the original WSR maximization problem with a sequence of convex subproblems based on fractional programming (FP). By exploiting the Karush-Kuhn-Tucker (KKT) conditions of each subproblem, the optimal beamforming structure is derived. An efficient hyperplane fixed point iteration method is then proposed to find the optimal Lagrangian dual variables. Numerical results show that the proposed algorithm achieves the same performance but takes only 0.5% or less simulation time compared with the state-of-the-art WMMSE, SCA, and FP algorithms. The proposed algorithms pave the way for the practical and efficient optimization algorithm design for RSMA and its applications in 6G.

Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing local raw data. However, as the number of the local intermediate outputs is proportional to the training samples, it is critical to develop communication-efficient techniques for wireless vertical FL to support high-dimensional model aggregation with full device participation. In this paper, we propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation by leveraging over-the-air computation (AirComp) and alleviating communication straggler issue with cooperative model aggregation among geographically distributed edge servers. However, the model aggregation error caused by AirComp and quantization errors caused by the limited fronthaul capacity degrade the learning performance for vertical FL. To address these issues, we characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. To improve the learning performance, we establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed. We conduct extensive simulations to demonstrate the effectiveness of the proposed system architecture and optimization framework for vertical FL.

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL model and transmits the quantized model parameter to the remote radio heads (RRHs). Each RRH receives the signals from devices over the wireless link and forwards the signals to the server via the fronthaul link. We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN. Based on the proposed energy consumption model, we formulate an energy minimization problem that optimizes the fronthaul rate allocation, device transmit power allocation, and QNN precision levels while satisfying the limited fronthaul capacity constraint and ensuring the convergence of the proposed FL model to a target accuracy. To solve this problem, we analyze the convergence rate and propose efficient algorithms based on the alternative optimization technique. Simulation results show that the proposed FL framework can significantly reduce energy consumption compared to other conventional approaches. We draw the conclusion that the proposed framework holds great potential for achieving a sustainable and environmentally-friendly FL in Cloud-RAN.

Rate-Splitting Multiple Access (RSMA) is an emerging and powerful multiple access scheme that relies on splitting and encoding user messages encoded into common and private streams, so as to partially decode multi-user interference and partially treat it as noise. In this paper, the secrecy rate constraint of each user is taken into consideration and a RSMA-based secure beamforming approach is proposed to maximize the weighted sum-rate (WSR). A generalized receiver model is considered where each user is also a potential eavesdropper wiretapping confidential messages for other users after decoding its own message. To solve the intractable non-convexity caused by security constraints in the formulated problem, a novel joint weighted minimum mean square error and successive convex approximation based alternate optimization algorithm is proposed and extended to maximize the instantaneous WSR with perfect channel state information at the transmitter (CSIT) and the weighted ergodic sum-rate with imperfect CSIT. Numerical results validate the effectiveness of the proposed design, which significantly improve the sum-rate performance and robustness to channel errors while guaranteeing message confidentiality and also unveil a better trade-off between message confidentiality and sum-rate performance thanks to its powerful interference management capability.

The growth of vehicular applications will inevitably require Base Stations (BSs) to simultaneously serve more Connected Vehicles (CVs) within limited bandwidth resources, which imposes a great challenge in interference management. Effective management of this interference is crucial for reliable Vehicle-to-Everything (V2X) communication, and necessitates accurate Channel State Information at the Transmitter (CSIT). In practice, the dynamic and unpredictable nature of CV movements prevents BS from obtaining perfect CSIT, leading to outdated information and threatening communication performance. In this study, we propose a Rate-Splitting Multiple Access (RSMA)-enabled V2X communication system to efficiently manage interference channels. We leverage a 1-layer RSMA scheme to relax the stringent requirement for perfect CSIT and enhance robustness to outdated information. Furthermore, we introduce Gruformer, a transformer-based model for improved CSIT prediction utilizing historical data. While longer forecasting horizons decrease accuracy, we present a game theory-based approach that significantly reduces processing time for power allocation, enabling timely decisions before CSIT becomes outdated. Simulation results reveal that Gruformer allows for more accurate predictions during rapid changes in channel conditions. Leveraging this high-quality CSIT, the proposed V2X system achieves a 20% increase in Weighted Ergodic Sum-Rate (WESR). Furthermore, the game theory-based approach delivers a 60% reduction in processing time while maintaining near-optimal performance.

Channel state information (CSI) feedback has been a challenging task for downlink frequency division duplex (FDD) system. Meanwhile, sensory data collection in large-scale network is pivotal to support intelligent applications in the central server. In this paper, we propose a deep-learning-empowered adaptive CSI feedback compression and quantization based on the information-bottleneck principle, where the sensory data transmission is hidden within the CSI feedback to eliminate extra communication cost and preserve the data privacy at the same time. To reduce the impact of information hiding on CSI feedback, we focus on hiding data in the semantic level. The tradeoffs among communication efficiency, CSI accuracy, hidden information transfer accuracy, and privacy are jointly optimized. Simulations further verify that the proposed scheme can achieve accurate sensory data collection without resource occupation and accurate CSI feedback with limited feedback overhead simultaneously.

Rate-splitting multiple access (RSMA) has emerged as a potential non-orthogonal transmission strategy and powerful interference management scheme for 6G. Most of the existing works on RSMA beamforming design assume instantaneous or statistical channel state information (CSI) is available at the transmitter. Such an assumption however is impractical especially in massive multiple-input multiple-output (MIMO) due to the dynamic wireless environments and the challenges in channel estimation. In this work, we propose a novel beamforming design framework based on online learning and online control to adaptively learn the best precoding action for a RSMA-aided downlink massive MIMO without explicit CSI feedback. In particular, we first formulate the precoder selection problem that maximizes the ergodic sum-rate subject to a long-term transmit power constraint as a constrained combinatorial multi-armed bandit (CMAB) problem. Then we propose a precoder selection with bandit learning algorithm for RSMA (PBR). Our theoretical analysis shows that PBR achieves a sublinear regret bound with a long-term power constraint guarantee. Through experimental results, we not only verify our theoretical analysis but also demonstrate the outperformance of PBR in terms of sum-rate and power consumption compared with the conventional transmission schemes without using RSMA.

Cooperative rate-splitting (CRS), which takes the advantages of cooperative user relaying and rate-splitting multiple access (RSMA), has gained recognition for its potential in enhancing the spectral efficiency, user fairness, and coverage of wireless networks. In this work, to further strengthen the performance of CRS, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled full-duplex (FD) CRS transmission framework. By jointly designing the active beamforming, common rate allocation, and the STAR-RIS passive transmission and reflection beamforming to maximize the worst case rate among users, we show that the proposed STAR-RIS assisted FD CRS transmission scheme considerably enhances user fairness compared to conventional FD CRS approaches.

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) have been both recognized as promising techniques for 6G. The benefits of combining the two techniques to enhance the spectral and energy efficiency have been recently exploited in communication-only networks. Inspired by the recent advances, in this work we investigate the use of RIS empowered RSMA for simultaneous wireless information and power transfer (SWIPT) with one transmitter concurrently sending information to multiple information receivers (IRs) and transferring energy to multiple energy receivers (ERs). Specifically, we jointly optimize the transmit beamformers and the RIS reflection coefficients to maximize the weighted sum-rate (WSR) of IRs under the harvested energy constraint of ERs and the transmit power constraint. An alternating optimization and successive convex approximation (SCA)-based optimization framework is then raised to address the problem. Numerical results demonstrate that by marrying the benefits of RSMA and RIS, the proposed RIS empowered RSMA achieves a better tradeoff between the WSR of IRs and energy harvested at ERs. In addition, the rate region of RSMA almost coincides with that of SDMA+RIS especially when the two users have similar weights. Therefore, we conclude that RIS empowered RSMA is a promising strategy for SWIPT.

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency; 2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service; 5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and energy-efficient technique for 6G. By adjusting the phase shifts of passive reflecting elements, RIS is capable of suppressing the interference and combining the desired signals constructively at receivers, thereby significantly enhancing the performance of communication system. In this paper, we consider a green multi-user multi-antenna cellular network, where multiple RISs are deployed to provide energy-efficient communication service to end users. We jointly optimize the phase shifts of RISs, beamforming of the base stations, and the active RIS set with the aim of minimizing the power consumption of the base station (BS) and RISs subject to the quality of service (QoS) constraints of users and the transmit power constraint of the BS. However, the problem is mixed combinatorial and non-convex, and there is a potential infeasibility issue when the QoS constraints cannot be guaranteed by all users. To deal with the infeasibility issue, we further investigate a user admission control problem to jointly optimize the transmit beamforming, RIS phase shifts, and the admitted user set. A unified alternating optimization (AO) framework is then proposed to solve both the power minimization and user admission control problems. Specifically, we first decompose the original non-convex problem into several rank-one constrained optimization subproblems via matrix lifting. A difference-of-convex (DC) algorithm is then developed to solve each decomposed subproblem. The proposed AO framework efficiently minimizes the power consumption of wireless networks as well as user admission control when the QoS constraints cannot be guaranteed by all users. To further address the complexity-sensitive issue for practical implementation, we propose an alternative low-complexity beamforming and RISs phase shifts design algorithm based on zero-forcing (ZF) to enable the green cellular networks.

Massive multiple-input multiple-output (MaMIMO) has become an integral part of the fifth-generation (5G) standard, and is envisioned to be further developed in beyond 5G (B5G) networks. With a massive number of antennas at the base station (BS), MaMIMO is best equipped to cater prominent use cases of B5G networks such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) or combinations thereof. However, one of the critical challenges to this pursuit is the sporadic access behaviour of a massive number of devices in practical networks that inevitably leads to the conspicuous pilot contamination problem. Conventional linearly precoded physical layer strategies employed for downlink transmission in time division duplex (TDD) MaMIMO would incur a noticeable spectral efficiency (SE) loss in the presence of this pilot contamination. In this paper, we aim to integrate a robust multiple access and interference management strategy named rate-splitting multiple access (RSMA) with TDD MaMIMO for downlink transmission and investigate its SE performance. We propose a novel downlink transmission framework of RSMA in TDD MaMIMO, devise a precoder design strategy and power allocation schemes to maximize different network utility functions. Numerical results reveal that RSMA is significantly more robust to pilot contamination and always achieves a SE performance that is equal to or better than the conventional linearly precoded MaMIMO transmission strategy.

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming design, where user messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop the private streams of other users. We formulate a weighted sum-rate (WSR) maximization problem subject to the secrecy rate requirements of all users. To tackle the non-convexity of the formulated problem, a successive convex approximation (SCA)-based approach is adopted to convert the original non-convex and intractable problem into a low-complexity suboptimal iterative algorithm. Numerical results demonstrate that the proposed secure beamforming scheme outperforms the conventional multi-user linear precoding (MULP) technique in terms of the WSR performance while ensuring user secrecy rate requirements.

Rate-Splitting Multiple Access (RSMA) has recently appeared as a powerful and robust multiple access and interference management strategy for downlink Multi-user (MU) multi-antenna communications. In this work, we study the precoder design problem for RSMA scheme in downlink MU systems with both perfect and imperfect Channel State Information at the Transmitter (CSIT) and assess the role and benefits of transmitting multiple common streams. Unlike existing works which have considered single-antenna receivers (Multiple-Input Single-Output–MISO), we propose and extend the RSMA framework for multi-antenna receivers (Multiple-Input Multiple-Output–MIMO) and formulate the precoder optimization problem with the aim of maximizing the Weighted Ergodic Sum-Rate (WESR). Precoder optimization is solved using Sample Average Approximation (SAA) together with the proposed vectorization and Weighted Minimum Mean Square Error (WMMSE) based approach. Achievable sum-Degree of Freedom (DoF) of RSMA is derived for the proposed framework as an increasing function of the number of transmitted common and private streams, which is further validated by the Ergodic Sum Rate (ESR) performance using Monte Carlo simulations. Conventional MU–MIMO based on linear precoders and Non-Orthogonal Multiple Access (NOMA) schemes are considered as baselines. Numerical results show that with imperfect CSIT, the sum-DoF and ESR performance of RSMA is superior to those of the two baselines, and is increasing with the number of transmitted common streams. Moreover, by better managing the interference, RSMA not only has significant ESR gains over baseline schemes but is more robust to CSIT inaccuracies, network loads and user deployments.

Rate-splitting (RS) has recently been recognized as a promising physical-layer technique for multi-antenna broadcast channels (BC). Due to its ability to partially decode the interference and partially treat the remaining interference as noise, RS is an enabler for a powerful multiple access, namely rate-splitting multiple access (RSMA), that has been shown to achieve higher spectral efficiency (SE) and energy efficiency (EE) than both space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in a wide range of user deployments and network loads. As SE maximization and EE maximization are two conflicting objectives in the moderate and high signal-to-noise ratio (SNR) regimes, the study of the tradeoff between the two criteria is of particular interest. In this work, we address the SE-EE tradeoff by studying the joint SE and EE maximization problem of RSMA in multiple input single output (MISO) BC with rate-dependent circuit power consumption at the transmitter. To tackle the challenges coming from multiple objective functions and rate-dependent circuit power consumption, we first propose two models to transform the original problem into two single-objective problems, namely, weighted-sum method and weighted-power method. A low-complexity algorithm with closed-form solution is proposed to solve each single-objective problem in the two-user system. For the generalized$K$-user system, a successive convex approximation (SCA)-based algorithm is then proposed to optimize the precoders of each transformed problem. Numerical results show that our algorithm converges much faster than existing algorithms. In addition, the performance of RSMA is superior to or equal to SDMA and NOMA in terms of SE, EE and their tradeoff.

We consider globally optimal precoder design for rate splitting multiple access in Gaussian multiple-input single-output downlink channels with respect to weighted sum rate and energy efficiency maximization. The proposed algorithm solves an instance of the joint multicast and unicast beamforming problem and includes multicast-and unicast-only beamforming as special cases. Numerical results show that it outperforms state-of-the-art algorithms in terms of numerical stability and converges almost twice as fast.

Cloud radio access networks (C-RAN) enable a network platform for beyond the fifth generation of communication networks (B5G), which incorporates the advances in cloud computing technologies to modern radio access networks. Recently, rate splitting multiple access (RSMA), relying on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been shown to manage the interference in multi-antenna communication networks efficiently. This paper considers applying RSMA in C-RAN. We address the practical challenge of a transmitter that only knows the statistical channel state information (CSI) of the users. To this end, the paper investigates the problem of stochastic coordinated beamforming (SCB) optimization to maximize the ergodic sum-rate (ESR) in the network. Furthermore, we propose a scalable and robust RS scheme where the number of the common streams to be decoded at each user scales linearly with the number of users, and the common stream selection only depends on the statistical CSI. The setup leads to a challenging stochastic and non-convex optimization problem. A sample average approximation (SAA) and weighted minimum mean square error (WMMSE) based algorithm is adopted to tackle the intractable stochastic non-convex optimization and guarantee convergence to a stationary point asymptotically. The numerical simulations demonstrate the efficiency of the proposed RS strategy and show a gain up to 27% in the achievable ESR compared with state-of-the-art schemes, namely treating interference as noise (TIN) and non-orthogonal multiple access (NOMA) schemes.

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA under the important setup of imperfect Channel State Information at the Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speed and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to the degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.

In the near future, it is envisioned that cellular networks will have to cope with extensive internet of things (IoT) devices. Therefore, a required feature of cellular IoT will be the capability to serve simultaneously a large number of devices with heterogeneous demands and qualities of channel state information at the transmitter (CSIT). In this paper, we focus on an overloaded multiple-input single-output (MISO) broadcast channel (BC) with two groups of CSIT qualities, namely, one group of users (representative of high-end devices) for which the transmitter has partial knowledge of the CSI, the other group of users (representative of IoT devices) for which the transmitter only has knowledge of the statistical CSI (i.e., the distribution information of the user channels). We introduce rate-splitting multiple access (RSMA), a new multiple access based on multi-antenna rate-splitting (RS) technique for cellular IoT. Two strategies are proposed, namely, time partitioning-RSMA (TP-RSMA) and power partitioning-RSMA (PP-RSMA). The former independently serves the two groups of users over orthogonal time slots while the latter jointly serves the two groups of users within the same time slot in a non-orthogonal manner. We first show at high signal-to-noise ratio (SNR) that PP-RSMA achieves the optimal degrees-of-freedom (DoF) in an overloaded MISO BC with heterogeneous CSIT qualities. We then show at finite SNR that PP-RSMA achieves explicit sum rate gain over TP-RSMA and all baseline schemes by marrying the benefits of PP and RSMA. Furthermore, PP-RSMA is robust to CSIT inaccuracy and flexible to cope with quality of service (QoS) rate constraints of all users. The DoF and rate analysis helps us in drawing the conclusion that PP-RSMA is a powerful framework for cellular IoT with a large number of devices.

Rate-splitting multiple access (RSMA), recognized as a promising technique for future communication systems to generalize and outperform existing multiple access techniques, has been shown to enhance the spectral and energy efficiencies of multi-user multi-antenna broadcast channels (BCs). In this work, motivated by the benefits of RSMA discovered in multi-antenna BCs, we investigate the performance of RSMA in cloud radio access networks (C-RANs). Specifically, the beamforming vectors, message splits, and stream-to-base stations (BSs) allocation are jointly designed with the aim to maximize the sum rate subject to per-BS power constraints and fronthaul capacity constraints. Numerical results demonstrate that RSMA boosts the sum rate in C-RAN especially in strong interference regimes. Therefore, RSMA is a more promising transmission technique for C-RAN than other conventional transmission schemes such as treating interference as noise (TIN) or orthogonal multiple access schemes.

Rate-Splitting Multiple Access (RSMA) is an emerging flexible, robust and powerful multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) strategies at the transmitter and Successive Interference Cancellation (SIC) at the receivers, and has the unique ability to partially decode interference and partially treat interference as noise so as to softly bridge the two extremes of fully decoding interference (as in Non-Orthogonal Multiple Access, NOMA) and treating interference as noise (as in Space Division Multiple Access, SDMA or Multi-User Multiple-Input Multiple-Output, MU-MIMO). RSMA has been shown to provide significant room for spectral efficiency, energy efficiency, Quality-of-Service enhancements, robustness to Channel State Information (CSI) imperfections, as well as feedback overhead and complexity reduction, in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities). RSMA is also deeply rooted and motivated by recent advances in understanding the fundamental limits of multi-antenna networks with imperfect CSI at the Transmitter (CSIT). In this work, we leverage recent results on the optimization of RSMA and design for the first time its physical layer, accounting for modulation, coding (using polar codes), message split, adaptive modulation and coding, and SIC receiver. Link-level evaluations confirm the significant throughput benefits of RSMA over various baselines as SDMA and NOMA.

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes. For CRS based on the three-node relay channel, the transmitter splits and encodes the messages of legitimate users into common and private streams based on Rate-Splitting (RS). With the goal of maximizing SSR, the proposed CRS strategy opportunistically asks the relaying legitimate user to forward its decoded common message. During the transmission, the eavesdropper keeps wiretapping silently. To ensure secure transmission, the common message is used for the dual purpose, serving both as a desired message and Artificial Noise (AN) without consuming extra transmit power comparing to the conventional AN design. Taking into account the total power constraint and the Physical Layer (PHY) security, the precoders and timeslot allocation are jointly optimized by solving the nonconvex SSR maximization problem based on Sequential Convex Approximation (SCA) algorithm. Numerical results show that the proposed CRS secure transmission scheme outperforms existing Multi-User Linear Precoding (MU-LP) and Cooperative Non-Orthogonal Multiple Access (C-NOMA) strategies. Therefore, CRS is a promising strategy to enhance the PHY security in Multi-antenna BC systems.

In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from conventional interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in Non-Orthogonal Multiple Access, NOMA). This paper is dedicated to the theory and applications of a more general and powerful transmission framework based on Rate-Splitting Multiple Access (RSMA) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect Channel State Information at the Transmitter (CSIT), and complexity reduction. This paper provides an overview of RSMA and its potential to address the requirements of 6G.

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate region than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region of DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies.

Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying. In this work, the existing twouser CRS transmission strategy is generalized to the K-user case. We study the problem of jointly optimizing the precoders, message split, time slot allocation, and relaying user scheduling with the objective of maximizing the minimum rate among users subject to a transmit power constraint at the base station. As the user scheduling problem is discrete and the entire problem is non-convex, we propose a two-stage low-complexity algorithm to solve the problem. Both centralized and decentralized relaying protocols based on selecting K1(K1<; K) strongest users are first proposed followed by a Successive Convex Approximation (SCA)-based algorithm to jointly optimize the time slot, precoders and message split. Numerical results show that by applying the proposed two-stage algorithm, the worst-case achievable rate achieved by CRS is significantly increased over that of NRS and SDMA in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel strengths). Importantly, the proposed SCA-based algorithm dramatically reduces the computational complexity without any rate loss compared with the conventional algorithm in the literature of CRS. Therefore, we conclude that the proposed K-user CRS combined with the two-stage algorithm is more powerful than the existing transmission schemes.

Millimeter wave (mm-wave) communication technology promises to provide higher data rates as the spectrum is highly under-utilized and more amount of spectrum can be allocated. However, mm-wave cannot travel longer distances and it is sensitive to blockages. The former issue can be dealt with via the small cell technology. Small cell technology can increase the spectral efficiency of the system if the resources are efficiently utilized. The transmission distance between the mobile user and the small cell access point is reduced and it makes an ideal candidate for the use of mm-wave. However, the latter issue of blockages still needs to be handled in order to exploit the full benefits of mm-wave in small cell network. In this paper, a blockage-aware allocation of resources for a mm-wave small cell network is investigated. Our objective is to maximize the downlink sum-rate of all users such that the quality of service (QoS) and power constraints are satisfied. The formulated problem takes into consideration the presence of blockages and the best link (both LOS and relay, only LOS, only relay) possible. We propose a blockage-aware relay selection and power allocation algorithm (BARSPAA) for mm-wave in small cells. The BARSPAA algorithm is compared with exhaustive and bounded exhaustive search algorithms. Numerical results show that the proposed BARSPAA achieves a significantly close performance while the algorithm complexity is much reduced.

Due to its promising performance in a wide range of practical scenarios, Rate-Splitting (RS) has recently received significant attention in academia for the downlink of communication systems. In this letter, we propose and analyse a Cooperative Rate-Splitting (CRS) strategy based on the three-node relay channel where the transmitter is equipped with multiple antennas. By splitting user messages and linearly precoding common and private streams at the transmitter, and opportunistically asking the relaying user to forward its decoded common message, CRS can efficiently cope with a wide range of propagation conditions (disparity of user channel strengths and directions) and compensate for the performance degradation due to deep fading. The precoder design and the resource allocation are optimized by solving the Weighted Sum Rate (WSR) maximization problem. Numerical results demonstrate that our proposed CRS scheme can achieve an explicit rate region improvement compared to its non-cooperative counterpart and other cooperative strategies (such as cooperative NOMA).

In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1-layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU-LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission.