Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoT’s stringent ultra-low latency, realtime processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

In disaster situations, collapse of local communication infrastructure is a major issue due to destruction of buildings, antennas, power sources, and to name a few. Drones, as flying base stations, are a promising solution to restore essential communication services in emergency situations. The contribution of this article is twofold: First, an efficient computer vision technique is proposed to identify areas with high density of low mobility or stationary users. This is done using a multistep process, which includes image acquisition, classification, and crowd density estimation. Next, an accurate mathematical model is presented for joint optimization of drone base stations placement and user assignment. The goal here is to maximize the number of serviced users with minimum number of drones, while satisfying practical network constraints. An optimal solution to such a biobjective optimization problem has complexity exponential to the network size. Furthermore, a low complexity heuristic is proposed to solve the optimization problem. Complexity analysis of the proposed solution is then carried out. Simulation results for a number of practical network scenarios demonstrate that the proposed solution achieves a performance comparable to the optimal solution.

In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of internet of things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power Mobile Edge Cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and Mobile Edge Cloud (MEC) to address low power devices’ battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes.

Non-orthogonal multiple access (NOMA) continues to receive enormous attention as a potential technique for improving the spectral efficiency (SE) of wireless networks. Although for several years most research efforts on the performance of NOMA systems focused on the ergodic sum-rate and outage probability, recent works have shifted towards error rate analysis of various NOMA configurations and designs. While the influx of publications on this topic is rich in lessons and innovations, the sheer volume of it makes it easy to get caught up in the details, so much so that one often loses sight of the overall picture. This paper serves as a survey on NOMA error rate analysis, painted in the large with bold and immediately recognizable strokes of insights to facilitate for the reader to understand and follow the up-to-date progress in this area. In addition to summarizing the principles of NOMA error rate analysis, this work aims to minimize redundancy and overlaps, identify research gaps, and outline future research directions.