Beamforming Design for Active IOS Aided NOMA Networks

Luo, Hao; Lv, Lu; Wu, Qingqing; Ding, Zhiguo; Al-Dhahir, Naofal; Chen, Jian

doi:10.1109/lwc.2022.3223906

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…With the help of the reinforcement learning method, the authors in [38] researched the communication probability of the ARIS-aided energy harvesting NOMA system. An active surface-assisted NOMA scheme was outlined in [39], where the sum achievable capacity was derived to satisfy user requirements. In addition, an ARIS was used to aid rate-splitting multiple access [40], which has the ability to strike a tradeoff between resource efficiency and computational complexity.…”

Section: Previous Workmentioning

confidence: 99%

Performance Analysis of ARIS-NOMA Systems under Cascade Rician Channels

Liu,

Yue,

et al. 2024

Symmetry

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Active reconfigurable intelligent surface (ARIS) has sparked more attention due to its capability to overcome the impact of double fading. This paper introduces using an ARIS to aid non-orthogonal multiple access (NOMA) communications over cascade Rician fading channels, where the direct links between the base station and users are seriously blocked. By applying ARIS, it can amplify the superposed signals to overcome the double pass loss effect caused by passive RIS. Both ARIS and NOMA can have synergistic impacts on sixth-generation communication systems. New approximated and asymptotic expressions in terms of outage probability and ergodic data rate of the k-th user are deduced for ARIS-NOMA systems. Based on asymptotic analytical results, we further calculate the diversity order and high signal-to-noise ratio slope of the k-th user. Finally, the system throughput of ARIS-NOMA is discussed in the delay-constrained transmission mode. Monte Carlo numerical results are performed to verify that: (1) the outage behaviors of ARIS-NOMA are better than that of ARIS-assisted orthogonal multiple access (OMA); (2) as the impact of thermal noise caused by ARIS becomes larger, the communication performance from the base station to ARIS, then to users, becomes worse; (3) the ARIS-NOMA systems have the ability to provide the improved ergodic data rate relative to ARIS-OMA.

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Section: Previous Workmentioning

confidence: 99%

Performance Analysis of ARIS-NOMA Systems under Cascade Rician Channels

Liu,

Yue,

et al. 2024

Symmetry

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“…Among the various candidates, a novel energy-efficient mode, known as reconfigurable intelligent surface (RIS), has widely been applied to improve communication security and network performance [ 5 , 6 ]. Each of the RIS reflective elements is a varactor diode that allows the amplitude and/or phase shift of the incident signal to be independently controlled by an embedded RIS central controller [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

Zhu

et al. 2023

Sensors

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In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

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