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This work proposes a transmitter antenna selection (TAS) method for multiple-input multiple-out (MIMO) nonorthogonal multiple access (NOMA) that is a promising multiple access technique for the fifth generation mobile communications systems. Specifically, we propose to activate the more suitable subset of base station antennas while allocating users into appropriate NOMA clusters such that the system operates in energy efficiency mode, selecting such appropriate antenna subset indexes that maximize the sum-rate (SR) of the NOMA-MIMO system. Once that the TAS based on exhaustive-search is very complex to be implemented in real communication systems, we propose an effective TAS method based on machine learning while keeping very promising performance. A convolutional neural network-based transmitter antenna selection (CNN-TAS) method is proposed for efficiently select antennas aiming at maximizing the system SR. Hence, extensive numerical results demonstrate that the CNN-TAS can suitably learn the problem, performing with very high accuracy choosing properly the antenna subset that maximizes the system spectral efficiency while reducing substantially the processing time of real-time operations. INTRODUCTIONNonorthogonal multiple access (NOMA) has been considered as a promising multiple access technology for the fifth generation (5G) cellular systems once that can significantly enhance system overall spectral efficiency (SE), accommodating two or more users on the same frequency and multiplexing the users in the power-domain, differently of others types of multiple-access techniques, for example, time division multiple access, frequency division multiple access, code division

In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM), are developed to provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit-error-rate (BER) performance vs computational complexity trade-off is analyzed, demonstrating the superiority of the proposed DNN-OFDM and ELM-OFDM detectors methodologies.

msu.ac.zw/ About TCC Africa The Training Centre in Communication (TCC Africa), is an award-winning Trust, established as a non-profit entity in 2006 and is registered in Kenya. TCC Africa provides capacity support in improving researchers output and visibility through training in scholarly and science communication. We are located at the

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