Imperceptible latency, uninterrupted communication, and the availability of inexhaustible bandwidth are conceptualized as essential milestones to revolutionize the modes by which societies generated, circulate, receive, and perceive information. The exponential increase in wireless data traffic has raised concerns to investigate suitable bands in the radio spectrum to satisfy the intensifying user's data rate requirements. Overall the wireless infrastructure needs development and exploitation to synchronize with the massive capacity and connectivity demands. The Terahertz (THz) frequency band (0.1-10 THz) is considered as a pivotal solution to fulfill the needs of applications and devices requiring the high speed transmission, and have received noticeable attention from the research community. Technologies in this spectrum are facing rapid development and hold high potentials in applications like ultra-fast short-range wireless communications, remote sensing, biological detection, and basic material research. The antenna is one of the critical components to support the THz systems and require a considerable attention in terms of precision. Compact high-gain antennas are desirable for low latency and high data rate THz wireless communication systems, specifically for applications having space limitation, for example, in the high speed interlink inside the high density wireless communication base station (BS). Nevertheless, there still exist many challenges, while designing the antenna for THz communications requiring innovative solutions. This paper serves an introductory guideline to address the challenges and opportunities, while designing a THz enabled antenna.

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Due to unrivaled effectiveness, non-orthogonal multiple access (NOMA) has risen as a promising multiple access scheme for the Internet of things (IoT). In this paper, we provide a new power allocation technique for improving the energy and spectral efficiency of NOMA-enabled IoT devices. The power allocation is performed without compromising the quality of service (QoS) requirements of the network. By considering the transmit power, QoS and successive interference cancellation (SIC) constraints, we use the sequential quadratic programming (SQP) technique to solve the non-convex problem. To assess the performance of our scheme, we compare the proposed SQP-based approach with the conventional KKT-based optimization method. We provide Monte Carlo simulation results to assess our proposed power allocation framework and illustrate the performance improvements against orthogonal multiple access (OMA) scheme. The results uncover that the proposed SQP-based power optimization design substantially improves the performance of the NOMA-enabled IoT network.

The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented.

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the evergrowing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMFaware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques.

This work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner.

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The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively.