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User Selection in Millimeter Wave Massive MIMO System using Convolutional Neural Networks

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Document pages: 4 pages

Abstract: A hybrid architecture for millimeter wave (mmW) massive MIMO systems isconsidered practically implementable due to low power consumption and highenergy efficiency. However, due to the limited number of RF chains, userselection becomes necessary for such architecture. Traditional user selectionalgorithms suffer from high computational complexity and, therefore, may not bescalable in 5G and beyond wireless mobile communications. To address thisissue, in this letter we propose a low complexity CNN framework for userselection. The proposed CNN accepts as input the channel matrix and gives asoutput the selected users. Simulation results show that the proposed CNNperforms close to optimal exhaustive search in terms of achievable rate, withnegligible computational complexity. In addition, CNN based user selectionoutperforms the evolutionary algorithm and the greedy algorithm in terms ofboth achievable rate and computational complexity. Finally, simulation resultsalso show that the proposed CNN based user selection scheme is robust tochannel imperfections.

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