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Author: search.filters.author.Thipchaksurat, SakchaiSubject: search.filters.subject.Imperfect SIC

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    Application of Deep Learning to Fairness-Based Power Allocation for 5G NOMA System with Imperfect SIC
    (2020-01-01)
    Saetan, Worawit
    ;
    Thipchaksurat, Sakchai
    Non-orthogonal multiple access (NOMA) has been a promising technique for 5G communication system, which has higher spectrum efficiency, energy efficiency and fairness than that of orthogonal multiple access (OMA). NOMA serves more than one user by sharing the same time-frequency resource block and uses successive interference cancellation (SIC) to separate multiuser signal. However, the error propagation in the SIC procedure, which is called the imperfect SIC, can cause a severe performance loss. In this paper, we apply the deep learning to power allocation in order to mitigate the impact of imperfect SIC under fairness perspective for downlink NOMA system. Firstly, we formulate an optimization problem aiming to maximize the minimum user rate to provide fairness for all users. Secondly, exhaustive search method is used to solve the formulated problem and thus the optimal power allocation factor is obtained. Lastly, we train the deep neural network to predict the obtained power allocation factor. The simulation results show that our proposed scheme provides the performance close to that provided by exhaustive search. Furthermore, the proposed scheme has much lower complexity than the exhaustive search scheme.
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    Application of deep learning to energy-efficient power allocation scheme for 5G SC-NOMA system with imperfect SIC
    (2019-07-01)
    Saetan, Worawit
    ;
    Thipchaksurat, Sakchai
    Non-orthogonal multiple access (NOMA) is a multiuser access technique for 5G communication system, which has drawn more attention from both academia and industry because of its higher spectrum efficiency than that of orthogonal multiple access (OMA). NOMA allows more than one user to access the same time-frequency resource and separates multiuser signal by successive interference cancellation (SIC). However, error propagation in SIC procedure called imperfect SIC can cause a severe performance degradation. In this paper, we propose the application of deep learning to power allocation to reduce the effect of imperfect SIC under energy-efficient perspective for downlink NOMA system. Firstly, we formulate a non-convex optimization problem aiming to maximize energy efficiency subject to a minimum data rate of each user and a total transmit power of base station (BS). Secondly, on the formulated problem, we obtain the optimal power allocation via exhaustive search method. Lastly, we train a deep neural network to predict the obtained power allocation. Simulation results verify that the proposed scheme provides the near optimal performance with very low computational complexity.