Detection for 5G-NOMA: An online adaptive machine learning approach

Daniyal Amir Awan, Renato L.G. Cavalcante, Masahiro Yukawa, Slawomir Stanczak

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    9 Citations (Scopus)


    Non-orthogonal multiple access (NOMA) has emerged as a promising radio access technique for enabling the performance enhancements promised by the fifth-generation (5G) networks in terms of connectivity, latency, and spectrum efficiency. In the NOMA uplink, detection based on successive interference cancellation (SIC) with device clustering has been suggested. If the receivers are equipped with multiple antennas, SIC can be combined with minimum mean-squared error (MMSE) beamforming. However, there exists a tradeoff between the NOMA cluster size and the incurred SIC error. Larger clusters lead to larger errors but they are desirable from the spectrum efficiency and connectivity point of view. To enable the deployment of large clusters, we propose a novel online learning detection method for the NOMA uplink. We design an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design is robust against variations of a dynamic wireless network that can deteriorate the performance of a purely nonlinear adaptive filter. We demonstrate by simulations that the proposed method outperforms (symbol level) MMSE-SIC based detection for large cluster sizes.

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781538631805
    Publication statusPublished - 2018 Jul 27
    Event2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
    Duration: 2018 May 202018 May 24


    Other2018 IEEE International Conference on Communications, ICC 2018
    CountryUnited States
    CityKansas City

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Electrical and Electronic Engineering

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