On the Performance of Joint Channel Estimation and MUD for CS-Based Random Access in Multi-Cell Environment

Ameha T. Abebe, Chung G. Kang

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

    1 Citation (Scopus)

    Abstract

    Synchronization, channel estimation, and multi-user detection (MUD) can be performed in a single shot for a comprehensive grant-free access [1]. The scheme employs compressive sensing by exploiting two sparse phenomena: sparsity in users' activity and sparsity in channel delay spread. The performance of compressive sensing based schemes should be thoroughly studied in a multi-cell environment as the other-cell interference (OCI) may affect the underlying sparsity. In this paper, we provide a performance analysis of the comprehensive grant-free access scheme in a multi-cell environment and showed that OCI would not affect the sparsity of the received signal, and rather can be considered as a dispersed noise, if signature allocation among cells is properly handled. Furthermore, we show the performance & complexity of the receiver in contrast with other multiple measurement vector-based receivers modified for joint & blind channel estimation and (MUD).

    Original languageEnglish
    Title of host publication2017 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781509059089
    DOIs
    Publication statusPublished - 2017 May 3
    Event2017 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2017 - San Francisco, United States
    Duration: 2017 Mar 192017 Mar 22

    Publication series

    Name2017 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2017

    Other

    Other2017 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2017
    Country/TerritoryUnited States
    CitySan Francisco
    Period17/3/1917/3/22

    Bibliographical note

    Publisher Copyright:
    © 2017 IEEE.

    Copyright:
    Copyright 2017 Elsevier B.V., All rights reserved.

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Signal Processing
    • Renewable Energy, Sustainability and the Environment
    • Media Technology
    • Software

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