AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning?

Zhong, Ruikang; Liu, Yuanwei; Mu, Xidong; Chen, Yue; Song, Lingyang

doi:10.1109/jsac.2021.3126068

Cited by 66 publications

(28 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, deep reinforcement learning (DRL) and reinforcement learning (RL) algorithms, branches of machine learning, have been proposed and applied to solve different problems related to IRS-enabled communication systems. In [44], the authors investigated a single-antenna transmission of IRS-assisted NOMA network and two RL algorithms were proposed to solve the problem caused by the overhead. In [45], two RL-based algorithms were adopted to solve a resource allocation problem in order to minimize the average age-ofinformation of the users in IoT networks.…”

Section: ) Machine Learning In Airs-noma Networkmentioning

confidence: 99%

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Lima

Sena

Dinis

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) technique and unmanned aerial vehicles (UAVs) have been recognized as promising technologies for enabling the stringent requirements of the different network infrastructures expected for the next generation of wireless networks. In parallel, intelligent reconfigurable surfaces (IRSs) have been widely pointed out as an auspicious solution to further improve spectral efficiency, coverage range, and connectivity. By integrating IRS with UAV and NOMA schemes with multiple-input multiple-output (MIMO) it is possible to smartly improve the overall network performance. In order to explore some of these potentials, this paper provides a comprehensive discussion about the interplay of aerial IRS in MIMO-NOMA (AIRS-NOMA) networks, as well its architecture, functionality principles, and performance gains. In particular, attractive gains related to the data rate maximization, user fairness, energy efficiency, and coverage range are highlighted. Simulation results are provided to support our insightful discussions, in which it is revealed that the performance gains of AIRS-NOMA networks are superior when compared to terrestrial deployment. In addition, to guide new studies perspectives, it is addressed some issues and research opportunities associated with this potential integration.

show abstract

Section: ) Machine Learning In Airs-noma Networkmentioning

confidence: 99%

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Lima

Sena

Dinis

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…In contrast to the alternating optimization techniques, the authors in [24] conceived a novel smart reconfigurable terahertz (THz) multiple-input multipleoutput (MIMO)-NOMA framework, where a novel multi-agent deep reinforcement learning algorithm was proposed by exploiting the decentralized partially-observable Markov decision process. As a step further, in [25], both a deep learning approach and a reinforcement learning approach were developed for the RIS-Assisted NOMA networks to maximize the effective throughput of the entire transmission period. Furthermore, instead of signal enhancement, a signal cancellation based design was proposed in [26] for the passive beamforming weight at RISs in a MIMO assisted NOMA network.…”

Section: A Related Work 1) Ris-enabled Noma Communicationsmentioning

confidence: 99%

Queue-Aware STAR-RIS Assisted NOMA Communication Systems

Zhang¹,

Liu²,

Mu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been receiving great attention nowadays due to the capability of achieving full-space coverage. In this paper, the queue-aware STAR-RIS assisted non-orthogonal multiple access (NOMA) communication system is investigated to ensure the system stability. To tackle the challenge of infinite time periods required for stability, the long-term stability-oriented problem is reformulated as a queue-weighted sum rate (QWSR) maximization problem in each single time slot based on the Lyapunov drift theory. In particular, the rate weight allocated to each user is determined by the length of a data queue, which is maintained at the base station (BS) and pending delivery to each user. Then, the QWSR is maximized by jointly optimizing the NOMA decoding order, the active beamforming coefficients (ABCs) at the BS, and the passive transmission and reflection coefficients (PTRCs) at the STAR-RIS, where three STAR-RIS operating protocols are considered, namely energy splitting (ES), mode switching (MS), and time switching (TS). For ES, to handle the highly-coupled and non-convex problem, the blocked coordinate descent and the successive convex approximation methods are invoked to iteratively and alternatively optimize the problem. Moreover, the proposed iterative algorithm is further extended to a penalty-based two-loop algorithm to solve the binary amplitude constrained problem for MS. For TS, the formulated problem is decomposed into two subproblems, each of which can be solved in a similar manner as introduced for ES. Simulation results show that: i) our proposed STAR-RIS assisted NOMA communication achieves better performance compared with the conventional schemes; ii) the reformulated QWSR maximization problem is proven to ensure the system stability; and iii) the TS protocol achieves superior performance with respect to both the QWSR and the average queue length.

show abstract

“…Moreover, in terms of artificial intelligence (AI) empowered downlink RIS systems, the authors in [19] investigated the benefits of deep learning (DL) and reinforcement learning (RL) approaches in empowering both RIS-NOMA and RIS-OMA multiuser downlink communication systems over fading channels. The authors also considered the time overhead required for configuring the RIS elements at the start of each fading channel.…”

Section: Related Work On Downlink Ris-noma Systemsmentioning

confidence: 99%

Uplink Multiple Access for Reconfigurable Intelligent Surface-Aided Wireless Systems

Nguyen

Munochiveyi

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The integration of reconfigurable intelligent surface- (RIS-) aided wireless communication and multiple access is an attractive and promising scheme for next-generation wireless networks. In this research work, separate uplink RIS-aided nonorthogonal multiple access (NOMA) and uplink relay-assisted NOMA schemes are studied, where the RIS and relay devices are deployed to enhance the coverage of an obstructed single-antenna far user by assisting it to communicate with a single-antenna base station. In each scenario, both perfect successive interference cancellation (pSIC) and imperfect successive interference cancellation (ipSIC) operations are considered in the proposed multiple access network. To characterize the system performance, the associated residual interference caused by ipSIC and relay loop self-interference is characterized using the Rayleigh fading model; subsequently, new channel statistics are derived based on the Gauss-Laguerre polynomial. Consequently, the closed-form approximate outage probability expressions are derived for each scenario in the high signal-to-noise ratio (SNR) regime. To gain further insight, the system throughput in the delay-limited transmission is also obtained for each scenario. The formulated expressions are validated via Monte-Carlo simulations. Finally, the obtained simulation results demonstrate and validate the superiority of the RIS system over the relay device under several system parameters of interest despite the limitation of ipSIC.

show abstract

AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning?

Cited by 66 publications

References 49 publications

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Queue-Aware STAR-RIS Assisted NOMA Communication Systems

Uplink Multiple Access for Reconfigurable Intelligent Surface-Aided Wireless Systems

Contact Info

Product

Resources

About