The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).

Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.

Filterbank multicarrier (FBMC) is an effective method for modulation over massive multiple-input multiple-output (MIMO) channels in the future fifth generation wireless networks. This multicarrier technique provides high spectral efficiency, and it is resilient against synchronization errors compared to the well-known orthogonal frequency division multiplexing technique. This paper introduces a new technique, that combines the Code-division multiple-access (CDMA) and cosine modulated filter banks, a specific class of FBMC, to massive MIMO communication systems. In the proposed technique, the data symbols are transmitted on different sub-bands and also, they are spread in each sub-band by CDMA code. The proposed technique reduces the interference between data symbols, which consequently improves bit-error-rate (BER) performance. Simulation results show that the overall BER performance of the proposed multicarrier transmission technique is significantly enhanced. The results obtained show that as the number of base station antennas increases, different channel distortions are averaged out, thus BER decreases (in average by 10^–2) for the same signal-to-noise ratio.

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.

Both light emitting diode (LED) characteristics for illumination and communication simultaneously have made visible light communication-orthogonal frequency division multiplexing (VLC-OFDM) a strong competitive to radio frequency (RF). In this juncture, to improve signal to noise ratio (SNR) and coverage contour, the wavelet-OFDM is suggested for indoor VLC systems. In this paper, a wavelet VLC-OFDM is proposed for imaging multiple-input multiple-output (MIMO) systems. The proposed wavelet-OFDM is exploited for a hybrid space-frequency domain pre-equalization technique instead of the traditional fast Fourier transform (FFT)-OFDM technique. The Meyer filter is selected and employed in the proposed technique. A comparable achievement is elaborated for several numbers of channels to achieve the enhanced performance in terms of bit rate and coverage contour. In addition, a useful comparison is executed between our wavelet VLC-OFDM and the traditional FFT-OFDM for a hybrid space-frequency domain pre-equalization technique. The simulation results emphasize the superiority point of wavelet VLC-OFDM MIMO system by improving the coverage contour by ~20% over the traditional OFDM at a 10⁻³ bit error rate (BER) target. Hence, the proposed technique can be potentially executed in indoor VLC-MIMO systems.