We propose a new partial joint processing architecture for distributed massive multiple-input multiple-output (MIMO) networks. As opposed to the traditional full-joint processing architecture, where the channel coefficients of all the users within the cooperating cluster are learnt at the base stations, in the proposed architecture, we allow each base station to learn the channel coefficients only of the users that maintain a strong average received signal-to-noise ratio to that base station based on a predefined threshold. This threshold provides extra flexibility, trading-off channel estimation for performance. We assume a zero-forcing receiver at the central processing unit using estimated channels and unknown terms are set to zero. We then derive an accurate approximation for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user. We use this approximation to derive closed-form expressions for the achievable rate and symbol error probability of an arbitrary user. Numerical examples are used to illustrate the accuracy of the analysis.


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    Title :

    A Novel Partial Joint Processing Architecture for distributed Massive MIMO


    Contributors:


    Publication date :

    2022-06-01


    Size :

    482651 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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