Residual self-interference cancellation and data detection in full-duplex communication systems

Abbas Koohian*, Hani Mehrpouyan, Ali Arshad Nasir, Salman Durrani, Steven D. Blostein

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    Abstract

    Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual self-interference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.

    Original languageEnglish
    Title of host publication2017 IEEE International Conference on Communications, ICC 2017
    EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781467389990
    DOIs
    Publication statusPublished - 28 Jul 2017
    Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
    Duration: 21 May 201725 May 2017

    Publication series

    NameIEEE International Conference on Communications
    ISSN (Print)1550-3607

    Conference

    Conference2017 IEEE International Conference on Communications, ICC 2017
    Country/TerritoryFrance
    CityParis
    Period21/05/1725/05/17

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