Design and Analysis of NOMA With Adaptive Modulation and Power Under BLER Constraints

Yahya, Hamad; Alsusa, Emad; Al-Dweik, Arafat

doi:10.1109/tvt.2022.3184974

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Moreover, the authors in [9] proposed a resource allocation algorithm to tackle the problem of fair resource allocation and AMC for downlink NOMA systems. Different from [1], [7], [9], the block error rate requirement for each user was considered as a constraint in [8] when optimizing the AMC scheme. For uplink NOMA systems, an asymmetric adaptive modulation framework was introduced in [3] to address the distinct uncertainty of the bit error rate (BER) and throughput.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Aided Link Adaptation for Downlink NOMA Systems With Channel Imperfections

Luo

Mheich

Chen

et al. 2023

2023 IEEE Wireless Communications and Networking Conference (WCNC)

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Non-orthogonal multiple access (NOMA) is a promising candidate radio access technology for future wireless communication systems, which can achieve improved connectivity and spectral efficiency. Without sacrificing error rate performance, link adaptation combining with adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) can provide better spectral efficiency and reliable data transmission by allowing both power and rate to adapt to channel fading and enabling re-transmissions. However, current AMC or HARQ schemes may not be preferable for NOMA systems due to the imperfect channel estimation and error propagation during successive interference cancellation (SIC). To address this problem, a reinforcement learning based link adaptation scheme for downlink NOMA systems is introduced in this paper. Specifically, we first analyze the throughput and spectrum efficiency of NOMA system with AMC combined with HARQ. Then, taking into account the imperfections of channel estimation and error propagation in SIC, we propose SINR and SNR based corrections to correct the modulation and coding scheme selection. Finally, reinforcement learning (RL) is developed to optimize the SNR and SINR correction process. Comparing with a conventional fixed look-up table based scheme, the proposed solutions achieve superior performance in terms of spectral efficiency and packet error performance.Index Terms-Non-orthogonal multiple access (NOMA), adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ), reinforcement learning (RL).

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