The field of wireless communication networks has witnessed a dramatic change over the last decade due to sophisticated technologies deployed to satisfy various demands peculiar to different data-intensive wireless applications. Consequently, this has led to the aggressive use of the available propagation channels to fulfill the minimum quality of service (QoS) requirement. A major barometer used to gauge the performance of a wireless communication system is the spectral efficiency (SE) of its communication channels. A key technology used to improve SE substantially is the multiple input multiple output (MIMO) technique. This article presents a detailed survey of MIMO channel models in wireless communication systems. First, we present the general MIMO channel model and identified three major MIMO channel models, viz., the physical, analytical, and standardized models. The physical models describe the MIMO channel using physical parameters. The analytical models show the statistical features of the MIMO channel with respect to the measured data. The standardized models provide a unified framework for modern radio propagation architecture, advanced signal processing, and cutting-edge multiple access techniques. Additionally, we examined the strengths and limitations of the existing channel models and discussed model design, development, parameterization, implementation, and validation. Finally, we present the recent 3GPP-based 3D channel model, the transitioning from 2D to 3D channel modeling, discuss open issues, and highlight vital lessons learned for future research exploration in MIMO communication systems.

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Standard Propagation Channel Models for MIMO Communication Systems

The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math></inline-formula> to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength. 

Predictive Estimation of Optimal Signal Strength From Drones Over IoT Frameworks in Smart Cities

In recent times, the rapid growth in mobile subscriptions and the associated demand for high data rates fuels the need for a robust wireless network design to meet the required capacity and coverage. Deploying massive numbers of cellular base stations (BSs) over a geographic area to fulfill high-capacity demands and broad network coverage is quite challenging due to inter-cell interference and significant rate variations. Cell-free massive MIMO (CF-mMIMO), a key enabler for 5G and 6G wireless networks, has been identified as an innovative technology to address this problem. In CF-mMIMO, many irregularly scattered single access points (APs) are linked to a central processing unit (CPU) via a backhaul network that coherently serves a limited number of mobile stations (MSs) to achieve high energy efficiency (EE) and spectral gains. This paper presents key areas of applications of CF-mMIMO in the ubiquitous 5G, and the envisioned 6G wireless networks. First, a foundational background on massive MIMO solutions-cellular massive MIMO, network MIMO, and CF-mMIMO is presented, focusing on the application areas and associated challenges. Additionally, CF-mMIMO architectures, design considerations, and system modeling are discussed extensively. Furthermore, the key areas of application of CF-mMIMO such as simultaneous wireless information and power transfer (SWIPT), channel hardening, hardware efficiency, power control, non-orthogonal multiple access (NOMA), spectral efficiency (SE), and EE are discussed exhaustively. Finally, the research directions, open issues, and lessons learned to stimulate cutting-edge research in this emerging domain of wireless communications are highlighted.

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Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems.

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Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

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Development of a Multilayer Perception Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band.

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Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate

Epileptic patients suffer from an epileptic brain seizure caused by the temporary and unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals are manually studied by medical practitioners as it records the electrical activities from the brain. This technique consumes a lot of time, and the outputs are unreliable. In a bid to address this problem, a new structure for detecting an epileptic seizure is proposed in this study. The EEG signals obtained from the University of Bonn, Germany, and real-time medical records from the Senthil Multispecialty Hospital, India, were used. These signals were disintegrated into six frequency subbands that employed discrete wavelet transform (DWT) and extracted twelve statistical functions. In particular, seven best features were identified and further fed into k-Nearest Neighbor (kNN), naïve Bayes, Support Vector Machine (SVM), and Decision Tree classifiers for two-type and three-type classifications. Six statistical parameters were employed to measure the performance of these classifications. It has been found that different combinations of features and classifiers produce different results. Overall, the study is a first attempt to find the best combination feature set and classifier for 16 different 2-class and 3-class classification challenges of the Bonn and Senthil real-time clinical dataset.

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On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

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Stephen Ojo

Papers

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Development of a Multilayer Perception Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models