Energy-Efficient Design for a NOMA Assisted STAR-RIS Network With Deep Reinforcement Learning

Guo, Yi; Fang, Fang; Cai, Donghong; Ding, Zhiguo

doi:10.1109/tvt.2022.3224926

Cited by 36 publications

(19 citation statements)

References 10 publications

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“…Moreover, the work in [12] have exploited alternating optimization method for achievable secrecy rate maximization in RIS-enabled NTNs by optimizing beamforming, artificial noise, and phase shift design. Besides the resource optimization, some researchers have also investigated performance analysis of RIS-enabled NTNs [13]- [15]. Guo et al [13] have derived an exact expression of outage probability in RIS-enabled NTNs.…”

Section: Recent Advances In Ris-enabled Ntnsmentioning

confidence: 99%

“…Besides the resource optimization, some researchers have also investigated performance analysis of RIS-enabled NTNs [13]- [15]. Guo et al [13] have derived an exact expression of outage probability in RIS-enabled NTNs. Another work [14] has investigated closedform expression of average symbol error rate and ergodic capacity in RIS-enabled NTNs.…”

Section: Recent Advances In Ris-enabled Ntnsmentioning

confidence: 99%

“…Such systems also possess the inherent capacity to significantly enhance the security of wireless systems, and some preliminary results on the physical layer security for RIS-integrated NTNs are available in [10]- [12]. Performance analysis for such systems has been recently presented in [13]- [15]. Yet, amid these valuable contributions, it remains imperative to underscore the necessity for a holistic, long-term vision concerning next-generation RISenhanced NTNs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Khan,

lagunas,

Mahmood

et al. 2023

Preprint

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<p>This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.</p>

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Section: Recent Advances In Ris-enabled Ntnsmentioning

confidence: 99%

Section: Recent Advances In Ris-enabled Ntnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Khan,

lagunas,

Mahmood

et al. 2023

Preprint

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“…In [31], a STAR-RIS aided NOMA system was investigated to maximize the achievable sum rate. In [32], a STAR-RIS assisted downlink MISO-NOMA network was considered for maximizing the system energy efficiency. In [33], a STAR-RIS assisted downlink MIMO-NOMA network was considered to investigate the energy-efficient resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

Xie

Cai

Kaddoum³

2023

IEEE Trans. Commun.

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In this paper, we propose a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and energy buffer aided multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) system, which consists of a STAR-RIS, an access point (AP), and reflection users and transmission users with energy buffers. In the proposed system, the multi-antenna AP can transmit information and energy to several single-antenna reflection and transmission users simultaneously in a NOMA fashion, where the power transfer and information transmission states of the users are modeled using Markov chains. The reflection and transmission users harvest and store the energy in energy buffers as additional power supplies. The power outage probability, information outage probability, sum throughput, and joint outage probability closed-form expressions of the proposed system are derived over Nakagami-m fading channels, which are validated via simulations. Results demonstrate that the proposed system achieves better performance in comparison to the STAR-RIS aided MISO SWIPT-NOMA buffer-less, conventional RIS and energy buffer aided MISO SWIPT-NOMA, and STAR-RIS and energy buffer aided MISO SWIPT-time-division multiple access (TDMA) systems. Furthermore, a particle swarm optimization based power allocation (PSO-PA) algorithm is designed to maximize the sum throughput with a constraint on the joint outage probability. Simulation results illustrate that the proposed PSO-PA algorithm can achieve an improved sum throughput performance of the proposed system. Index Terms-Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), energy buffer, simultaneous wireless information and power transfer (SWIPT), non-orthogonal multiple access (NOMA), Markov chains.

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“…Numerical results demonstrated a significant improvement for the proposed system over the conventional active RIS-aided cell-free massive MIMO system. The authors of [18] and [19] investigated the potential of deep reinforcement learning (DRL) for optimising the performance of STAR-RIS-aided systems. Specifically, in [18], an online joint active and passive beamforming framework was proposed to maximise the long-term EE of a multi-cell STAR-RIS-enabled system using a parallel DRL technique.…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model

Zargari

Khalili

et al. 2021

IEEE Trans. Veh. Technol.

105

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In this paper, we consider the downlink transmission of a multi-antenna base station (BS) supported by an active simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS) to serve single-antenna users via simultaneous wireless information and power transfer (SWIPT). In this context, we formulate an energy efficiency maximisation problem that jointly optimises the gain, element selection and phase shift matrices of the active STAR-RIS, the transmit beamforming of the BS and the power splitting ratio of the users. With respect to the highly coupled and non-convex form of this problem, an alternating optimisation solution approach is proposed, using tools from convex optimisation and reinforcement learning. Specifically, semi-definite relaxation (SDR), difference of concave functions (DC), and fractional programming techniques are employed to transform the non-convex optimisation problem into a convex form for optimising the BS beamforming vector and the power splitting ratio of the SWIPT. Then, by integrating meta-learning with the modified deep deterministic policy gradient (DDPG) and soft actor-critical (SAC) methods, a combinatorial reinforcement learning network is developed to optimise the element selection, gain and phase shift matrices of the active STAR-RIS. Our simulations show the effectiveness of the proposed resource allocation scheme. Furthermore, our proposed active STAR-RISbased SWIPT system outperforms its passive counterpart by 57% on average. Index Terms-Active simultaneously transmitting and reflecting intelligent surface (STAR-RIS), simultaneously wireless information and power transfer (SWIPT), energy efficiency (EE), convex optimisation, meta-learning, deep deterministic policy gradient (DDPG), soft actor-critic (SAC).

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Energy-Efficient Design for a NOMA Assisted STAR-RIS Network With Deep Reinforcement Learning

Cited by 36 publications

References 10 publications

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Design and Performance Analysis of RIS-Aided DCSK-WPC System With Energy Buffer

Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model

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