Development of a convolutional neural network joint detector for non-orthogonal multiple access uplink receivers
We present a novel approach to signal detection for Non-Orthogonal Multiple Access (NOMA) uplink receivers using Convolutional Neural Networks (CNNs) in a single-shot fashion. The defacto NOMA detection method is the so-called Successive Interference Cancellation which requires precise channel estimation and accurate successive detection of the user equipment with the higher powers. It is proposed converting incoming packets into 2D image-like streams. These images are fed to a CNN-based deep learning network commonly used in the image processing literature for image classification. The classification label for each packet converted to an image is the transmitted symbols by all user equipment joined together. CNN network is trained using uniformly distributed samples of incoming packets at different signals to noise ratios. Furthermore, let’s performed hyperparameter optimization using the exhaustive search method. Our approach is tested using a modeled system of two user equipment systems in a 64-subcarrier Orthogonal Frequency Division Multiplexing (OFDM) and Rayleigh channel. It is found that a three-layer CNN with 32 filters of size 7×7 has registered the highest training and testing accuracy of about 81. In addition, our result showed significant improvement in Symbol Error Rate (SER) vs. Signal to Noise Ratio (SNR) compared to other state-of-the-art approaches such as least square, minimum mean square error, and maximum likelihood under various channel conditions. When the channel length is fixed at 20, our approach is at least one significant Figure better than the maximum likelihood method at (SNR) of 2 dB. Finally, the channel length to 12 is varied and it is registered about the same performance. Hence, our approach is more robust to joint detection in NOMA receivers, particularly in low signal-to-noise environments
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