Latency-aware blockage prediction in vision-aided federated wireless networks - Khan, Ahsan Raza; Ahmad, Iftikhar; Mohjazi, Lina; Hussain, Sajjad; Rais, Rao Naveed Bin; Imran, Muhammad Ali; Zoha, Ahmed (Frontiers in Communications and Networks, 2023)
Introduction: The future wireless landscape is evolving rapidly to meet ever-increasing data requirements, which can be enabled using higher-frequency spectrums like millimetre waves (mmWaves) and terahertz (THz). However, mmWave and THztechnologies rely on line-of-sight (LOS) communication, making them sensitive to sudden environmental changes and higher mobility of users, especially in urban areas. Methods: Therefore, beam blockage prediction is a critical challenge for sixth-generation (6G) wireless networks. One possible solution is to anticipate the potential change in the wireless network surroundings using multi-sensor data (wireless, vision, lidar, and GPS) with advanced deep learning (DL) and computer vision (CV) techniques. Despite numerous advantages, the fusion of deep learning,computer vision, and multi-modal data in centralised training introduces many challenges, including higher communication costs for raw data transfer, inefficient bandwidth usage and unacceptable latency. This work proposes latency-aware vision-aided federated wireless networks (VFWN) for beam blockage prediction using bimodal vision and wireless sensing data. The proposed framework usesdistributed learning on the edge nodes (EN) for data processing and model training. Results and Discussion: This involves federated learning for global model aggregation that minimizes latency and data communication cost as compared to centralised learning while achieving comparable predictive accuracy. For instance, the VFWN achieves a predictive accuracy of 98.5%, which is comparable to centralised learning with overall predictive accuracy 99%, considering that no data sharing is done. Furthermore, the proposed framework significantly reduces the communication cost by 81.31% and latency by 6.77% using real-time on device processing and inference.