Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

Abstract

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

Phase center motion based time modulated arrays with preprocessed time schemes for selective harmonic beamforming in B5G communication systems

“…The proposed methods are also compared with other state of the art methods, and the comparison is presented in Table 6. The power calculated for the fundamental (P 0 ) and sideband patterns (P SB ) using the proposed Equation (41) for asymmetric time scheme is also reported in Table 6. So, an efficient PCM-based TMLA is designed by proposing preprocessed time schemes for beamforming and beam scanning applications in B5G communication systems.…”

“…• The proposed BFNs can be an efficient alternative to the transmit receive modules (TRMs) of the conventional phased arrays for enhancing the channel or beam diversities. 38 It can also be applicable for direction finding, 39 adaptive antenna arrays, 40 multi-input multi-output (MIMO) antenna array systems, 41 and femtocell applications. 42,43 The complete article is decorated as follows: The theoretical insights of time modulated linear arrays (TMLAs) are presented in Section 2.…”

“…Then, the switch‐ON times are optimized for unidirectional PCM to control the fundamental and scanned beam radiation patterns. For the scanned beam patterns, ultra‐low SLLs are targeted in the proposed work. Bidirectional PCM‐based switching scheme is employed for monopulse beam scanning. Non‐optimized as well as optimized switching sequences are designed and compared. As only one optimized time scheme is used to control both the fundamental and monopulse patterns, the BFN complexities are reduced in comparison to conventional arrays. The proposed BFNs can be an efficient alternative to the transmit receive modules (TRMs) of the conventional phased arrays for enhancing the channel or beam diversities 38 . It can also be applicable for direction finding, 39 adaptive antenna arrays, 40 multi‐input multi‐output (MIMO) antenna array systems, 41 and femtocell applications 42,43 …”

Application of Machine Learning in Electromagnetics: Mini-Review

“…To select a suitable antenna subset for large-scale MIMO systems, a study [70] proposed a dynamic generalized spatial modulation framework with Euclidean distance-optimized antenna selection (EDAS) and a Multi-layer perception (MLP) model, which enabled higher diversity gain. Another article [71] proposed a CNN-based transmit antenna selection for a nonorthogonal multiple-access MIMO system for 5G applications, where the proposed algorithm showed a performance 10,000 times faster than the exhaustive search and 2 times faster than the hyper region proposal network (HRPN) with an 89% validation accuracy. However, SVM, Naive bayes, and KNN showed increased performance in selecting transmit antennas in untrusted relay networks conserving standard channel-state information (CSI) secrecy with decreased computational complexity compared to the exhaustive search in MIMO systems (Figure 5) [72,73].…”