| Abstract |
In recent years, the demand for high-speed wireless communication has experienced exponential growth due to increasing reliance on mobile devices and the proliferation of high-data-rate applications. To meet this demand, the millimeter wave (mmWave) and terahertz (THz) frequency bands have emerged as a promising solutions, offering significant data transmission capacity and bandwidth. Relying on line-of-sight (LOS) connections to ensure adequate reception power, They play a crucial role in fulfilling the data rate requirements for 5G and future 6G networks. Nevertheless, this necessitates the deployment of large antenna arrays using narrow beams at the transmission as well as the reception antennas to secure reliable received signal strength. Therefore, we are facing two types of problems that must be overcome: On one hand, selecting optimal beams for these large antenna arrays usually requires extensive training, challenging mmWave/THz communication systems to promote high consuming 5G/6G applications, such as connected and intelligent vehicle, video streaming, augmented and virtual reality, etc. Thus, there is a significant need for finding new ways to overcome this beam training effort and realize highly mobile mmWave/THz communication systems. On the other hand, deploying mmWave communication systems presents another challenge which is he susceptibility of mmWave signals to occlusion. Obstacles, such as moving objects like cars, trucks, buses, or even human bodies, can have a significant impact on the performance of mmWave communication systems. These impacts can result in a notable degradation of connection performance, increased disturbances, and reduced available data transmission capacity. This situation can lead to decreased download speeds, increased latency, and an unsatisfactory user experience. This phenomenon poses a significant obstacle to achieving reliable and uninterrupted communication in mmWave networks. In this book chapter, we propose addressing these two issues by utilizing computer vision and artificial intelligence (AI) tools within a multi-source data fusion framework. This framework integrates data collected from various sensors, including cameras, LiDAR, and GPS, to advance smart city transformation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
