The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers’ structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks (HetNET). We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

Autonomous security unmanned aerial vehicles (UAVs) have recently gained popularity as an effective solution for accomplishing target/intrusion detection and tracking tasks with little or no human intervention. In this context, we aim at developing an autonomous UAV system for detecting a dynamic and uncertain intrusion within an area, in which the intruder/target moves from one location to another within the area according to an unknown random distribution. The problem of finding an uncertain target while considering the energy causality constraint of the UAV’s battery and the uncertainty of the target movement is mathematically formulated as a search benefit maximization problem, which cannot be directly optimized due to the uncertain unknown target movement. Thus, we reformulate the optimization problem as a Markov-decision process that can be solved using reinforcement learning (RL) techniques. Then, we implement an RL-based algorithm to solve the reformulated benefit maximization problem by enabling the UAV to autonomously learn the dynamics of the intruder/target. Specifically, different design variants of the RL-based algorithm are implemented that differ in the used temporal difference methods (i.e., Q-learning or state-action-reward-state-action), and in the exploration algorithms (convergence-based or $\epsilon$-greedy). Simulation results show RL algorithms’ superiority and effectiveness over existing random and circular target detection algorithms.

The Internet of Underwater Things (IoUT) has lately gained popularity as a means of facilitating a wide range of underwater applications. In the IoUT, underwater communication is best accomplished by the usage of acoustic waves, whereas the terrestrial communication between the surface sinks and the on-shore control stations is typically achieved using radio waves. As a result, the greatest portion of an IoUT is enabled by the underwater acoustic sensor network (UASN), where the specific issues provided by the use of acoustic waves, the underwater node mobility, and the localization difficulties have yet to be addressed. In this paper, we discuss the challenges faced by the IoUT in terms of the currently proposed routing protocols and propose a Directional Selective Power Routing Protocol (DSPR) to cope with most of these challenges. The proposed protocol (i.e., DSPR) uses the angle of arrival and the sender depth information to find the best direction to the surface sink. In addition, the DSPR uses selective power control to enhance the delivery ratio and ensure connectivity while reducing energy consumption. To testify the performance of the proposed protocol, intensive simulation experiments have been conducted. The simulation results show that the proposed DSPR protocol outperforms two variations of the fixed directional routing (DR) protocol and the variable power depth-based routing (VDBR) protocol in terms of energy consumption and delivery ratio. For instance, the proposed DSPR protocol achieves at least 8 times enhancement in energy consumption compared with VDBR. In addition, DSPR saves around 30% of energy consumption over the DR protocols when the network is mobile. Moreover, the DSPR protocol acquires a delivery ratio above 90% for static/dynamic scenarios in both sparse and dense networks.

This paper analyzes the performance of decode-and-forward (DF)-based multihop relaying communication systems over $\alpha$ – $\eta$ – $\mu$ fading channels. Particularly, we provide expressions for the probability density function, cumulative distribution function, and moment generating function of the end-to-end signal-to-noise ratio for the DF-based multihop systems. Accordingly, closed-form expressions are derived for the end-to-end outage probability (OP), Ergodic capacity, and average symbol error rate (ASER) are provided. The provided expressions are very important in predicting the channel/link quality in the network such that practical multihop communication protocols (channel assignment, multihop path selection, power control, etc.) can be effectively designed. The derived expressions are numerically validated against well-known fading channel models (Rayleigh and $\eta$ – $\mu$ ) by setting the appropriate $\alpha$ , $\eta$ , and $\mu$ values. We evaluate the effects of the fading parameters and number of hops on the system performance. Results indicate that a DF-based multihop technique can significantly enhance the end-to-end ASER and OP performance.

In this paper, we propose a highly-reliable transmission strategy based on exploiting channel diversity for Internet-of-Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link-reliability, data rate and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is sub-optimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.