Assessing Data Rate Improvements through Traffic Offloading to mmWave-UAVs in Drone-Assisted Hybrid Cellular Networks

The surge in mobile data traffic and the rising demand for high-speed wireless connectivity have spurred interest in millimeter-wave (mmWave) technologies as a key enabler for next-generation cellular networks. However, mmWave communication faces challenges such as high signal attenuation, limited coverage, and susceptibility to blockage, particularly in dense urban environments. To address these challenges, drone-assisted networks utilizing Unmanned aerial vehicles (UAVs) have become a compelling and innovative solution. By deploying UAVs equipped with mmWave communication capabilities, network operators can improve coverage, enhance signal strength, and provide dynamic, on-demand capacity. This study assesses the data rate improvements achieved by users through traffic offloading to the mmWave-UAV tier in a drone-assisted, hybrid cellular network. The network combines mmWave and traditional sub-6 GHz technologies, allowing for efficient traffic management and high-speed connectivity. A geometry-based channel model is employed to accurately capture the physical environment, considering factors such as line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, as well as the mobility of both users and UAVs. The performance is evaluated across various scenarios, with a focus on the data rate enhancements achieved through offloading traffic to the UAVs, and the resulting impact on overall network efficiency. The results demonstrate that to ensure that 80% of users meet the data rate requirements, 14 out of the 20 UAVs need to be mmWave frequency when supporting up to 300 users. These results indicate significant potential for UAV-assisted networks to become key enablers in the quest for enhanced communication capabilities in increasingly dense urban settings.