Performance Optimization for Next Generation UAV Networks with Millimeter-Wave Simultaneously in Access and Backhaul

Khan, Shah Khalid; Farasat, Madiha; Naseem, Usman; Ali, Farasat

doi:10.17485/ijst/2019/v12i39/146317

Cited by 14 publications

(14 citation statements)

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“…Substituting (2) and 3in (6), The UE's received power through UAV is (if antenna gain for the Rx and Tx is same for both side of the UAV):…”

Section: Relay Path (Gnb-uav-ue)mentioning

confidence: 99%

“…With regard to its great potential in wireless communication networks, the application of Unmanned aerial vehicle's (UAVs) in cellular networks is also a salient topic of study in industry and the academia. [2][3][4][5] Urbanization and innovation in UAV technology and applications have been successful in unpacking various new means of use, in particular, its unparalleled scalability and resilience in on-the-go and pseudo-permanent cellular communication systems. Its applications are further ramped up with the innovations in the daily usage scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In both of these domains, there are various aspects which require further study and analysis; these include UAV's channel modeling, cellular network planning for UAV-based networks, energy efficiency, radio resource management, and UAV's trajectory optimization. [9][10][11][12][13] The mmWave band is capable of overcoming the scarcity of bandwidth in future wireless communication systems. 14,15 The scope and performance of mmWave are assessed in many scenarios as a backhaul or access link only; still, its utilization in backhaul or access communication links simultaneously is an area that needs further study, exploration and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…14,15 The scope and performance of mmWave are assessed in many scenarios as a backhaul or access link only; still, its utilization in backhaul or access communication links simultaneously is an area that needs further study, exploration and analysis. 2,10,16,17 In addition, other challenges that haven't yet been explored include a realistic assessment of situations focusing on satisfying consumer expectations for fast data speeds and keeping mobile industry standards as well.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical framework for 5G‐UAV relay

Khan

2021

Trans Emerging Tel Tech

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Unmanned aerial vehicle's (UAVs) small size, long-lasting battery, and flexible trajectory have produced widespread applications in radio communication systems, especially its usage as a cellular tower (gNB) for 5G services. At the same time, the mmWave's enormous available bandwidth makes it the primary contender for next-generation communication networks. This article aims to synthesize and assess the usage of mmWave concurrently in backhaul and access links for amplify-and-forward (AF) relay installed on the UAV. A mathematical framework is developed for the received power of the user equipment (UE) obtained in relay path (gNB-UAV-UE) and direct path (gNB-UE) via; (i) free space, and (ii) log-distance path loss model. The International Telecommunication Union recommended city model is used to assess the likelihood for LoS and NLoS paths in four urban environments; (i) high-rise urban, (ii) dense urban, (iii) urban, and (iv) suburban. Furthermore, various parameters, that is, UAV's position and amplification factor are stipulated to optimize the efficiency of a UAV-based AF relay for enhanced users coverage.

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“…Substituting (2) and 3in (6), The UE's received power through UAV is (if antenna gain for the Rx and Tx is same for both side of the UAV):…”

Section: Relay Path (Gnb-uav-ue)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical framework for 5G‐UAV relay

Khan

2021

Trans Emerging Tel Tech

Self Cite

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“…As the gradient-based iteration should maintain the connection between nodes until combination, it may result in enormous intercommunication costs. Also, the traded message duration may turn out to be incredibly long in a nonlinear situation [33][34][35][36][37]. These problems make wireless sensor network applications inappropriate.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms

Regin¹,

Rajest

Singh

2018

ICST Transactions on Scalable Information Systems

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This paper is about Fault detection over a wireless sensor network in a fully distributed manner. First, we proposed the Convex hull algorithm to calculate a set of extreme points with the neighbouring nodes and the duration of the message remains restricted as the number of nodes increases. Second, we proposed a Naïve Bayes classifier and convolution neural network (CNN) to improve the convergence performance and find the node faults. Finally, we analyze convex hull, Naïve bayes and CNN algorithms using real-world datasets to identify and organize the faults. Simulation and experimental outcomes retain feasibility and efficiency and show that the CNN algorithm has better-identified faults than the convex hull algorithm based on performance metrics.

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

Khan

Naseem

Siraj

et al. 2021

Trans Emerging Tel Tech

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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.

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Performance Optimization for Next Generation UAV Networks with Millimeter-Wave Simultaneously in Access and Backhaul

Cited by 14 publications

References 0 publications

Mathematical framework for 5G‐UAV relay

Mathematical framework for 5G‐UAV relay

Fault Detection in Wireless Sensor Network Based on Deep Learning Algorithms

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

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