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Coverage Analysis in Geometry-Based Drone-mmWave Hybrid Networks
Abstract
The demand for high-speed wireless communication has led to the exploration of millimeter-wave (mmWave) technologies, which offer large bandwidths and significant potential for 5G and beyond networks. However, mmWave signals are prone to challenges such as high path loss, susceptibility to blockage, and limited coverage in dense environments. Drone-assisted networks, leveraging unmanned aerial vehicles (UAVs), have emerged as a solution to extend network coverage, particularly in areas with inadequate terrestrial infrastructure. By integrating UAVs with mmWave communication, the network can offer dynamic and flexible coverage, enhancing performance in urban environments. This study focuses on evaluating the variation in signal-to-noise ratio (SNR) and rate coverage in a drone-assisted, mmWave hybrid cellular network. A geometry-based channel model is employed to model the propagation of mmWave signals in the presence of obstacles and dynamic environments, taking into account line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The primary objective is to assess how different SNR and rate thresholds impact network performance across various user locations and UAV configurations. Through extensive simulations, the study investigates how varying threshold values for SNR and data rate affect coverage and performance. The findings highlight the results show that with 50% of UAVs operating in mmWave, 93% of users meets the SNR coverage requirement, while 78% exceed the 1Mbps rate threshold, and 87% meet the 750Kbps rate requirement. These insights are crucial for the design and optimization of next-generation wireless communication systems that leverage drone-assisted mmWave networks.
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