Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Huang, Chong; Chen, Gaojie; Gong, Yu; Xu, Peng; Han, Zhu; Chambers, Jonathon A.

doi:10.1109/jsac.2021.3087225

Cited by 35 publications

(18 citation statements)

References 36 publications

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“…We assume the wavelength λ = 20 cm. According to (9), the channels are correlated in this case. The average achievable rate with uncorrelated channels is provided as a benchmark 5 .…”

Section: Correlated Channels Versus Uncorrelated Channelsmentioning

confidence: 99%

“…As pointed in [7], NOMA has better performance than orthogonal multiple access (OMA) when the channels gains of users are remarkably different. Conventionally, the channel gains of the users are determined by the uncontrollable wireless propagation environment [8], [9]. Hence, the applications of NOMA are limited in conventional wireless networks [10].…”

mentioning

confidence: 99%

“…In practice, the channels associated with the IOS are uncorrelated when the elements are placed in a linear array, and the spacing of elements equals λ/2 times integers. Besides, the covariance matrix R of uncorrelated channels in(9) is the identity matrix.…”

mentioning

confidence: 99%

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Performance Analysis of IOS-Assisted NOMA System With Channel Correlation and Phase Errors

Wang

Badiu

Chen

et al. 2022

IEEE Trans. Veh. Technol.

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In this paper, we investigate the performance of an intelligent omni-surface (IOS) assisted downlink non-orthogonal multiple access (NOMA) network with phase quantization errors and channel estimation errors, where the channels related to the IOS are spatially correlated. First, upper bounds on the average achievable rates of the two users are derived. Then, channel hardening is shown to occur in the proposed system, based on which we derive approximations of the average achievable rates of the two users. The analytical results illustrate that the proposed upper bound and approximation on the average achievable rates are asymptotically equivalent in the number of elements. Furthermore, it is proved that the asymptotic equivalence also holds for the average achievable rates with correlated and uncorrelated channels. Additionally, we extend the analysis by evaluating the average achievable rates for IOS assisted orthogonal multiple access (OMA) and IOS assisted multi-user NOMA scenarios. Simulation results corroborate the theoretical analysis and demonstrate that: i) low-precision elements with only two-bit phase adjustment can achieve the performance close to the ideal continuous phase shifting scheme; ii) The average achievable rates with correlated channels and uncorrelated channels are asymptotically equivalent in the number of elements; iii) IOS-assisted NOMA does not always perform better than OMA due to the reconfigurability of IOS in different time slots.Index Terms-Intelligent omni-surface (IOS), NOMA, spatial correlation, average achievable rate I. INTRODUCTIONThe future sixth-generation (6G) is expected to support billions of connected devices with significant demands in spectrum efficiency, reliability, latency, and connectivity [1]. To handle these challenging requirements, a wide range of technologies, including massive multiple-input multipleoutput (MIMO), network densification, and millimeter-wave (mm-wave) communications, have been investigated extensively in recent years [2]. Among various technologies, nonorthogonal multiple access (NOMA) has been regarded as a superior multiple access technology in the future wireless networks due to its promising advantages in terms of spectrum

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“…We assume the wavelength λ = 20 cm. According to (9), the channels are correlated in this case. The average achievable rate with uncorrelated channels is provided as a benchmark 5 .…”

Section: Correlated Channels Versus Uncorrelated Channelsmentioning

confidence: 99%

mentioning

confidence: 99%

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Performance Analysis of IOS-Assisted NOMA System With Channel Correlation and Phase Errors

Wang

Badiu

Chen

et al. 2022

IEEE Trans. Veh. Technol.

Self Cite

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“…Therefore, the user's data can be decoded from the simultaneously transmitted signals via successive interference cancellation. In particular, DL provides a powerful method to design and optimize NOMA systems [193], [194], [195]. Under analog uncoded transmission, interference can be harnessed via the new massive access techniques AirComp, for which Dong et al further proposed a blind AirComp for low-latency model aggregation without channel state information (CSI) access [79].…”

Section: B Wireless Techniques For Edge Trainingmentioning

confidence: 99%

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Letaief¹,

Shi²,

Lu³

et al. 2021

Preprint

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The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

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“…Huang et. al [20] proposed asynchronous DRL approaches to maximize system throughput, and Zhang et. al [21] designed a double DQN architecture to minimize transmission delay.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Robust Policy Design for Relay and Power Optimization in DF Relaying Networks

Geng¹,

Liu²,

Wang³

et al. 2022

Preprint

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In this paper, we study the outage minimization problem in a decode-and-forward cooperative network with relay uncertainty. To reduce the outage probability and improve the quality of service, existing researches usually rely on the assumption of both exact instantaneous channel state information (CSI) and environmental uncertainty. However, it is difficult to obtain perfect instantaneous CSI immediately under practical situations where channel states change rapidly, and the uncertainty in communication environments may not be observed, which makes traditional methods not applicable. Therefore, we turn to reinforcement learning (RL) methods for solutions, which do not need any prior knowledge of underlying channel or assumptions of environmental uncertainty. RL method is to learn from the interaction with communication environment, optimize its action policy, and then propose relay selection and power allocation schemes. We first analyse the robustness of RL action policy by giving the lower bound of the worst-case performance, when RL methods are applied to communication scenarios with environment uncertainty. Then, we propose a robust algorithm for outage probability minimization based

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Buffer-Aided Relay Selection for Cooperative Hybrid NOMA/OMA Networks With Asynchronous Deep Reinforcement Learning

Cited by 35 publications

References 36 publications

Performance Analysis of IOS-Assisted NOMA System With Channel Correlation and Phase Errors

Performance Analysis of IOS-Assisted NOMA System With Channel Correlation and Phase Errors

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Reinforcement Learning Based Robust Policy Design for Relay and Power Optimization in DF Relaying Networks

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