Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

Jiao, Shiyu; Xie, Ximing; Ding, Zhiguo; Fellow,

doi:10.48550/arxiv.2106.09616

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Due to their superior spectrum-, energy-, and cost-efficiency [ 149 , 150 ], intelligent reflecting surfaces (IRS) have been identified as a potentially game-changing technology for sixth-generation (6G) wireless communications [ 151 ]. Essentially, IRS is a cheap antenna array with a high number of tunable reflecting pieces [ 152 ].…”

Section: Integration Of Dl-based Noma With Emerging Technologiesmentioning

confidence: 99%

“…In an article [ 151 ], the authors look at how DRL-based approaches may be used for the IRS-UAV-NOMA downlink system for multiple users, as shown in Figure 10 . To maximize the efficiency of the BS’s power distribution, the IRS’s phase shifts, and the UAV’s horizontal position all at once, the DDPG algorithm is included in the DRL framework.…”

Section: Integration Of Dl-based Noma With Emerging Technologiesmentioning

confidence: 99%

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A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Mohsan

Shvetsov

et al. 2023

Sensors

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Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system’s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.

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Section: Integration Of Dl-based Noma With Emerging Technologiesmentioning

confidence: 99%

Section: Integration Of Dl-based Noma With Emerging Technologiesmentioning

confidence: 99%

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

Mohsan

Shvetsov

et al. 2023

Sensors

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“…In [47], the authors investigated the application of deep deterministic policy gradient to the AIRS-NOMA network with multiple users. The power allocation of the BS, the phase shifting of the IRS, and the horizontal position of the UAV were jointly optimized.…”

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.

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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.

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Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks

Cited by 2 publications

References 16 publications

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends

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

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