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Non-Orthogonal Multiple Access (NOMA) schemes are being actively explored toaddress some of the major challenges in 5th Generation (5G) Wirelesscommunications. Channel estimation is exceptionally challenging in scenarioswhere NOMA schemes are integrated with millimeter wave (mmWave) massivemultiple-input multiple-output (MIMO) systems. An accurate estimation of thechannel is essential in exploiting the benefits of the pairing of the duo-NOMAand mmWave. This paper proposes a convolutional neural network (CNN) basedapproach to estimate the channel for NOMA based millimeter wave (mmWave)massive multiple-input multiple-output (MIMO) systems built on a hybridarchitecture. Initially, users are grouped into different clusters based ontheir channel gains and beamforming technique is performed to maximize thesignal in the direction of desired cluster. A coarse estimation of the channelis first made from the received signal and this estimate is given as the inputto CNN to fine estimate the channel coefficients. Numerical illustrations showthat the proposed method outperforms least square (LS) estimate, minimum meansquare error (MMSE) estimate and are close to the Cramer-Rao Bound (CRB).

CNN based Channel Estimation using NOMA for mmWave Massive MIMO System