Rate-Splitting Multiple Access for URLLC Uplink in Physical Layer Network Slicing With eMBB

This paper considers the coexistence of Ultra Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) services in the uplink of Cloud Radio Access Network (C-RAN) architecture based on the relaying of radio signals over analog fronthaul links. While Orthogonal Multiple Access (OMA) to the radio resources enables the isolation and the separate design of different 5G services, Non-Orthogonal Multiple Access (NOMA) can enhance the system performance by sharing wireless and fronthaul resources. This paper provides an information-theoretic perspective in the performance of URLLC and eMBB traffic under both OMA and NOMA. The analysis focuses on standard cellular models with additive Gaussian noise links and a finite inter-cell interference span, and it accounts for different decoding strategies such as puncturing, Treating Interference as Noise (TIN) and Successive Interference Cancellation (SIC). Numerical results demonstrate that, for the considered analog fronthauling C-RAN architecture, NOMA achieves higher eMBB rates with respect to OMA, while guaranteeing reliable low-rate URLLC communication with minimal access latency. Moreover, NOMA under SIC is seen to achieve the best performance, while, unlike the case with digital capacity-constrained fronthaul links, TIN always outperforms puncturing.

Ultra-reliable low latency communication (URLLC) is one of the key services offered by the 5G wireless networks. This service type is characterized by stringent high reliability and low latency criteria, thus require efficient multiple access techniques to facilitate various mission-critical services. Non-orthogonal multiple access (NOMA) effectively enhances spectral efficiency and ensures fairness by exploiting the power allocation at receivers compared to orthogonal multiple access (OMA). Therefore, it becomes suitable for facilitating URLLC service. The addition of the cooperative relaying technique to NOMA can enhance the reliability of the data transmission. This motivated us to analyze the performance of cooperative NOMA (C-NOMA) for short packet URLLC data transmission. Hence, we have proposed a C-NOMA based system using the decode and forward (DF) relaying technique and successive interference cancellation (SIC) at the receiver to minimize the overall error probability for URLLC. The minimization of overall outage probability has been achieved by optimizing power allocation at the receiver considering the decoding error and error due to queuing delay violation while ensuring fair throughput among users. The simulation study has shown that the proposed C-NOMA based short packet transmission improves overall outage probability, energy efficiency, and ensures fair throughput among users in comparison to only NOMA and OMA based techniques.

Current research on Rate-Splitting Multiple Access (RSMA) focuses mostly on the case where the base station is fixed, and the use of UAVs as base stations for wireless communication is growing. The article introduces RSMA, a new type of non-orthogonal multiple access technique, to study the uplink transmission optimization problem of UAVs as base stations, and designs an algorithm based on the Augmented Lagrangian Method (ALM) and the Artificial Fish Swarm Algorithm (AFSA), which adds to the dynamic step size and field of view strategy, as well as the memory behavior of artificial fish. To solve the optimization problem further under the constraints of linear equations, a correction factor is introduced to the algorithm to constrain the behavior of the artificial fish. Finally, we simulate UAV-assisted uplink communication using RSMA, and the results demonstrate that this scheme has higher throughput than Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), which is 11.60% higher than NOMA.

The fifth generation (5G) of wireless communication accompanied by the advancement of mobile internet and the Internet of Things (IoT) will create stormy data traffic. The 5G challenges like higher spectral efficiency, massive connectivity, and low latency are addressed by the Non-Orthogonal Multiple Access (NOMA) in recent years. NOMA is proven to be a promising technique from its capability to accommodate a larger number of users in contrast to the conventional orthogonal multiple access (OMA) schemes. NOMA comprises two distinct techniques such as Power-Domain NOMA and Code-Domain NOMA. The non-orthogonal resource allocation in Power-Domain NOMA is practically realized by the Superposition Coding (SC) at the Base Station (BS) and an advanced Successive Interference Cancellation at the User Equipment (UE). In this manuscript, the basic concepts of NOMA, on comparing its channel gain with OMA are discussed first. Specifically, a comprehensive survey of various SIC techniques applied under perfect and imperfect Channel State Information (CSI) uncertainties are summarized in terms of outage probability, ergodic capacity, and system performance. In addition, the sophisticated SIC techniques, when NOMA is integrated with modern wireless communication technologies such as multiple-input multiple-output (MIMO-NOMA) and Cooperative (C-NOMA) communications are investigated. Furthermore, the challenges to be solved with their corresponding opportunities and future research directions of NOMA are explored.

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.

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.

Non-orthogonal multiple access (NOMA) and mmWave are considered to be promising techniques for 5G and beyond cellular network communication systems. While the mmWave band provides a very wide under-utilized bandwidth, NOMA enhances the spectral efficiency compared to orthogonal multiple access (OMA). The combination of these two techniques could be considered as the key solution to the high required data rates in next-generation communication systems. The implementation of NOMA is studied and verified under an ideal condition with perfect knowledge of the channel state information (CSI) at the base station (BS). However, under the practical conditions, the fluctuation of the wireless channel makes perfect CSI unachievable at the BS. Hence, we proposed to use the angle of departure (AoD) as feedback information from UEs to the BS. We assume that, mobile users (UEs) perfectly estimate the channel by detecting the pilot signals. Then, UEs quantize the AoD and feed back to the BS. Finally, the BS uses the AoD to perform user clustering, power allocation and beamforming. To reduce the feedback overhead further, we proposed a user clustering algorithm which uses one-bit feedback to determine the change of the AoD. Numerical results demonstrate that the proposed NOMA system outperforms the conventional orthogonal multiple-access (OMA) system with the same amount of feedback information.

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility.

Rate-splitting multiple access (RSMA) has recently gained attention in low earth orbit (LEO) satellite-terrestrial networks (LSTNs), due to its ability to provide high spectral efficiency in the context of constrained energy resources of LEO satellites. However, the impracticality of acquiring perfect real-time channel state information (CSI), due to high satellite mobility and long link delay, poses significant challenges to effective utilization of RSMA in LSTNs. To tackle this challenge, we propose a location-based robust RSMA scheme for downlink data transmission in LSTNs. First, we establish an optimization problem to minimize the power consumption of LEO satellites, while meeting user requirement on the real-time data rate violation probability. Subsequently, we transfer the probability constraints of rate violation probabilities into closed-form inequalities, by utilizing Markov inequality, Jensen’s inequality, and Cauchy-Schwarz inequality. The original problem is then transformed into a Markov decision process (MDP), and a Transformer encoder-based deep reinforcement learning (TDRL) algorithm is proposed to solve the complex problem based on the real-time locations of users and the LEO satellite. Additionally, a multi time-frame location-based training dataset generation method is proposed for the training of TDRL model, considering the mobility of LEO satellite. Simulation results demonstrate that the proposed scheme is effective in guaranteeing the rate violation probability requirement of each user, and RSMA significantly outperforms space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), with TDRL achieving faster convergence than other baselines.

Rate‐splitting multiple access (RSMA) is a promising enabler for improving spectral efficiency, interference management, and user fairness in satellite downlink systems. Yet, most RSMA studies are either tailored to terrestrial settings or rely primarily on numerical optimization, offering limited analytical insight under satellite‐realistic propagation such as shadowing. Motivated by geostationary Earth orbit (GEO) downlink operation, this paper provides a tractable performance analysis of an RSMA‐enabled single‐beam multiuser downlink over shadowed Rician fading. We consider a ‐antenna GEO satellite serving single‐antenna users with maximum‐ratio transmission (MRT)–based precoding and perfect successive interference cancellation (SIC). Closed‐form expressions are derived for the distribution of the effective channel gain, leading to exact and high‐SNR outage probability characterizations. The resulting formulas explicitly reveal the roles of the antenna count (defined as the number of satellite transmit antennas ), shadowed Rician parameters, and RSMA power allocation. Furthermore, the analysis yields simple feasibility conditions that delineate practical operating regions for common/private power splitting. We obtain user and system throughput expressions and identify that the achieved diversity order equals . Monte Carlo simulations validate the analysis and benchmark RSMA against nonorthogonal multiple access (NOMA) under various shadowing levels and power‐allocation settings, highlighting reliability–rate trade‐offs and providing actionable guidance for GEO satellite downlink design.

We study whether using non-orthogonal multiple access (NOMA) in the uplink of a mobile network can reduce the queueing delay compared to orthogonal multiple access (OMA) when the system requires communications at very low latency and high reliability. We first consider an ideal system model with perfect channel state information (CSI) at the transmitter and long codewords, where we determine the optimal decoding orders when the decoder uses successive interference cancellation (SIC) and derive closed-form expressions for the optimal rate when joint decoding is used. While joint decoding performs well even under tight delay constraints, NOMA with SIC decoding often performs worse than OMA. For low-latency systems, we must also consider the impact of finite-length channel coding, as well as rate adaptation based imperfect CSI. We derive closed-form approximations for the corresponding outage or error probabilities and find that those effects create a larger performance penalty for NOMA than for OMA. Thus, NOMA with SIC decoding may often be unsuitable for low-latency systems.

The feasibility of implementing non-orthogonal multiple access (NOMA) in massive multiple-input multiple-output (MIMO) downlink with distributed antenna arrays is investigated. The achievable downlink rates are derived for two channel state information (CSI) cases, namely (i) estimated uplink CSI at the access points (APs) via user uplink pilots and statistical CSI at the users, and (ii) estimated uplink CSI at the APs via uplink user pilots and estimated downlink CSI at the users via downlink pilots beamformed by the APs. The trade-off between the number of served user nodes and their achievable rates is investigated. The adverse effects of imperfect successive interference cancellation (SIC) and detection uncertainty on the achievable downlink rates are quantified. We show that the achievable downlink rate of NOMA can be inferior to that of the orthogonal multiple-access (OMA) counterpart in the low-user regime. This is a direct consequence of residual interference caused by intra-cluster pilot contamination and error propagation from imperfect SIC. However, the proposed massive MIMO NOMA downlink outperforms the OMA counterpart in the highuser regime in terms of the achievable downlink rate. Moreover, the impact of downlink pilots on the sum rate is also investigated.

Integrating intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) are effective technologies to improve the spectral/energy efficiency in next-generation (beyond 5G(B5G) and 6G) wireless networks. In this paper, we investigate a rate-splitting (RS)-based transmission technique for an IRS-aided communication network involving both near and cell-edge users. In particular, we derive a new architecture called IRS-RS that leverages the interplay between RS and IRS, with an aim to maximize the weighted sum-rate (WSR) of users by selecting the reflecting coefficients at the IRS and designing beamformers at the BS under the constraints of power at the base station (BS), quality of service (QoS) at each user and finite resolution at the IRS. To solve the non-convex WSR maximization problem, we propose an alternating algorithm and compare its performance with baseline non-orthogonal multiple access (NOMA) based transmission for an IRS-aided communication network for both perfect and imperfect channel state information (CSI) cases. Through numerical results, it is shown that the proposed IRS-RS architecture yields better QoS with respect to the cell-edge users when compared to an IRS-NOMA transmission scheme.

Non-Orthogonal Multiple Access (NOMA) is known as one of the promising techniques in the fifth generation of wireless communication. The classification of users in (NOMA) is based on power, while it is based on time, frequency and code in Orthogonal Multiple Access (OMA). In this study the downlink NOMA includes two users, one strong and one weak. The comparison of NOMA and OMA properties shows a range of Power Allocation Factors (PAF) for the two users within which NOMA properties have proved to be better than OMA. It was also shown that the achieved PAF is a function of channel gains of two users. Then, the coverage region is defined for multiple access channel; and by considering the Base Station (BS) as a fixed one in the center of the cell, the coverage region for both NOMA and OMA is calculated. The theoretical and analytical results show that the achieved coverage region of NOMA for both weak and strong users has been increased compared to OMA.

Reconfigurable intelligent surface (RIS) has recently been envisioned to provide an adaptable channel for futuristic wireless communication networks. This work characterized the impact of hardware impairments in the downlink RIS-aided non-orthogonal multiple access (NOMA) wireless networks. We considered the hardware impairments in the transceivers of the base station (BS) and users, phase error at the RIS and the imperfect successive interference cancellation (SIC) for the NOMA users. Specifically, the performance is evaluated by deriving the closed-form expressions for the outage probability (OP), spectral efficiency (SE) and energy efficiency (EE). Additionally, the performance of the orthogonal multiple access (OMA) system is also evaluated and compared with its NOMA counterpart. The RIS-aided NOMA outperforms OMA for the ideal hardware scenario. However, the hardware impairments significantly deteriorate RIS-aided NOMA's performance compared to RIS-aided OMA. Further, we have also compared the proposed RIS-aided NOMA with the baseline full-duplex relaying (FDR) based NOMA. The results show that the gains are more significant for a RIS-aided system than FDR.