Wireless communication systems have evolved over the ages. However, there are some undesirable effects of a wireless communication channel on the signals transmitted through it which are caused by the physical properties of the channel. These in turn always result in attenuation, distortion, delays, and phase shift of the signals arriving at the receiver end of the communication system. Consequently, there is a need to provide perfect and up-to-date estimate of the channel, in order to compensate for these effects, and for accurate signal demodulation, equalization, decoding, and a host of other baseband processing applications at the receiver end of the systems. In this article, a review of the various channel estimation techniques is provided. This review will be of assistance in reaching a compromised decision regarding channel ?estimation techniques to be employed in any wireless communication systems being developed.

Distracted driving is any activity that deviates an individual’s attention from driving. Some of these activities include talking to people in the vehicle, using hand-held devices such as mobile phones or tablets, eating or drinking, and adjusting the stereo or navigation systems while driving. To counter the effects caused by distracted driving, many countries around the world have imposed rules and charged fines on distracted drivers in order to ensure safe driving. Owing to the technological advancement in recent times, modern-day technologies such as computer vision, image processing, and machine learning techniques can further support the efforts of governments to prevent accidents caused by distracted driving. In this paper, an efficient distracted driver detection scheme (DDDS) has been proposed using two robust deep learning architectures, mainly visual geometric groups (VGG-16) and residual networks (ResNet-50). The proposed DDDS scheme contains the pre-processing module, image augmentation techniques, and two classification modules based on deep learning architectures. Both the architectures are implemented, and the results have been compared in terms of performance indices, namely accuracy, and logarithmic loss. The two-dimensional (2D) dashboard images derived from the State-Farm dataset are pre-processed and are used for training, testing, and validation of the proposed architectures. Accuracy of 86.1% and 87.92% are achieved with VGG-16 and ResNet-50 models, respectively, and it is observed that the DDDS scheme is found highly efficient for c4, c5, and c7 categories of the State-Farm dataset. The results obtained with the proposed DDDS methodology are compared with existing literature and found to be satisfactory. The algorithms developed and discussed for the proposed DDDS can be instrumental in reducing the fatalities and injuries caused due to distracted driving.

The need to cope with the continuously growing number of connected users and the increased demand for mobile broadband services in the Internet of Things has led to the notion of introducing the fog computing paradigm in fifth generation (5G) mobile networks in the form of fog radio access network (F-RAN). The F-RAN approach emphasises bringing the computation capability to the edge of the network so as to reduce network bottlenecks and improve latency. However, despite the potential, the management of computational resources remains a challenge in F-RAN architectures. Thus, this paper aims to overcome the shortcomings of conventional approaches to computational resource allocation in F-RANs. Reinforcement learning (RL) is presented as a method for dynamic and autonomous resource allocation, and an algorithm is proposed based on Q-learning. RL has several benefits in resource allocation problems and simulations carried out show that it outperforms reactive methods. Furthermore, the results show that the proposed algorithm improves latency and thus has the potential to have a major impact in 5G applications, particularly the Internet of Things.

Wireless communication in high obscurity environments is not effective especially for millimeter-wave (mmWave) frequencies due to propagation challenges. It is therefore, necessary to deploy multiple reconfigurable intelligent surfaces (multi-RISs) and massive multiple-input multiple-output (massive-MIMO) systems to circumvent the propagation challenges in such environments for effective communication. However, deploying massive-MIMO multi-RIS system increases the dimensionality of the channel, and using conventional channel estimation (CE) methods for estimating this multi-RIS-assisted channel is infeasible. In literature, machine learning (ML)-based CE methods have been proven to yield more accurate CE results for massive-MIMO RIS-assisted systems. Thus, in this paper, the ML-based CE method called the denoising convolutional neural network-gated recurrent unit (DnCNN-GRU) scheme is proposed for estimating the uplink cascaded time-varying massive-MIMO multi-RIS-assisted channel. The proposed scheme was then benchmarked with other CE methods to prove its superiority. It achieved a performance closer to the lower bound oracle least square (LS) estimate with about 1 dB gap in terms of the normalised mean squared error (NMSE).

This work investigates cooperative spectrum sensing in multi-channel cognitive radio networks (CRNs) with energy harvesting. Our goal is two-fold: firstly, to determine the optimal sensing parameters for effective management of the limited energy budget in order to maximize the achievable throughput, and secondly to exploit the benefits of a practical CRN towards improving the performance of the energy constrained CRN. Two different scenarios are considered. In the first, the Secondary User (SU) is assigned a single radio frequency (RF) harvesting source, while in the second, the SU is assigned multiple RF harvesting sources and can opportunistically harvest from any of the sources. For these scenarios, the problem is formulated as a stochastic optimal control system with infinite and continuous state and action spaces. This is known to be computationally intractable and becomes even more complicated in a two dimensional problem such as considered. In order to reduce the computational complexity, a myopic optimization approach is taken, and the problem is formulated into a mixed integer nonlinear problem (MINLP) to determine the channel assignment, the sensing duration, the distribution of the sensing duration associated with the assigned channels and the detection threshold under the constraint of energy causality and Primary User (PU) protection. A near-optimal solution is obtained to the MINLP based on the alternating convex optimization technique. Simulation results obtained show that the considered work can improve the amount of energy harvested and, by extension, the active probability of the SUs by exploiting the multi-channel benefits of practical CRN for an enhanced throughput.

In this paper overview of recent selected works that deal with novel directions in which Non orthogonal multiple access (NOMA) research is progressing is presented. These include the cell-free NOMA, deep learning extensions and optimizations of NOMA, energy optimization and task offloading with mobile-edge computing, NOMA and physical layer security, as well as virtualization, centralized-RAN aspects. All these are hot issues towards deployments of NOMA in the designs of beyond 5G and 6th generation (6G) wireless communication networks. Even though 3rd Generation Partnership Project (3GPP) has not yet made the decision regarding which NOMA techniques should be adopted, it seems like researchers already indicate clearly that NOMA has important place in the future network deployments based on ultra-density, novel 5G use-cases (massive machine type communications, ultra-reliable low latency communications). This paper highlights the most promising directions for NOMA research. The paper is summarized with necessary steps that are required to get NOMA into practical usage.