The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges

Abstract

Next‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the higher throughput and very low latency. In this context, the usage of unmanned aerial vehicle (UAV) is becoming a feasible option for deploying 5G services on demand. At the same time, the immense bandwidth potential of mmWave has strengthened its performance in radio communication. In this article, we provide a consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges. The review focuses on UAV relay architectures, identifies the relevant problems and limitations in the deployment of UAVs using mmWave in both access and backhaul links simultaneously. There is a critical analysis of the optimum placement of the UAVs as a relay with a focus on the mmWave band. The distinctive rich characteristics of the mmWave propagation and scattering are presented. We also synthesis mmWave path loss models. Then, the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of UAV‐based cellular communication networks are discussed. In the end, security and privacy issues in UAV‐based cellular network are spotlighted. It is believed that the literature discussed, and the findings reached in this article are of significant importance to researchers, application engineers and decision‐makers in the designing and deployment of UAV‐supported 5G network.

The Impact of Leadership Styles on Employees Productivity in Organizations: A Comparative Study Among Leadership Styles

“…Many leadership models clarify the interaction between leadership types and circumstances [52]. They are named contingency methods which comprise Hersey's and Blanchard's scenario theory, Fiedler's paradigm of leadership and his supporters, Evans and House's theory of the road and the idea of alternate leadership [53].…”

A Comparative Analysis of Active Learning for Biomedical Text Mining

“…In non-contextual embedding, BERT has been usually superior and better than ELMo. Similarly, innovative wireless connectivity techniques could be applied to the remote execution of these techniques [59][60][61][62] Si et al [63], trained the BERT on clinical notes corpora, using complex task-specific models to improve both traditional embedding and ELMo embedding i2b2 2010 and 2012 BioNER. Similarly, in another study, a new domain-specific language model, BioBERT [64], trained a BERT model on biomedical documents from PMC abstracts and articles from PubMed that resulted in improved BioNER results.…”

“…The results of the supervised ML-based ap- 59 proached increases as the set of labelled data is used for training. Using crowdsourcing 60 for labelling clinical data is not useful in the general domain; manual labelling is an ex-61 pensive and labour-intensive task. 62 AL [15] and semi-supervised learning [16] are viable options in contrast to standard 63 supervised ML methods and can reduce labelling costs.…”

QoS-Aware Multilayer UAV Deployment to Provide VoWiFi Service over 5G Networks

“…Provisioning a communication service to ground users typically requires the deployment of a UAV-to-ground access network and, if not in place, a UAV-to-UAV and/or UAV-to-infrastructure network or backhaul [6]. Most research papers tend to focus on only one of these networks and its associated communication technology (e.g., WiMAX [7], cellular 3/4G [8], or 5G [9] for the backhaul and cellular for the access network). Clearly, ground users' devices must share the radio technology in the access network, which could be a problem in practice often overlooked (e.g., scarcity of 5G users or the involvement of a telco operator).…”