Enhancing reliability in federated mmWave networks: A practical and scalable solution using radar-aided dynamic blockage recognition

Alquraan, M., Zoha, A. , Centeno, A. , Salameh, H. B., Muhaidat, S., Imran, M. A. and Mohjazi, L. (2024) Enhancing reliability in federated mmWave networks: A practical and scalable solution using radar-aided dynamic blockage recognition. IEEE Transactions on Mobile Computing, (doi: 10.1109/TMC.2024.3373529) (Early Online Publication)

Abstract

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Centeno, Dr Anthony and Zoha, Dr Ahmed and Imran, Professor Muhammad and Alquraan, Mohammad Mahmoud Younes and Mohjazi, Dr Lina
• Authors: Alquraan, M., Zoha, A., Centeno, A., Salameh, H. B., Muhaidat, S., Imran, M. A., and Mohjazi, L.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Transactions on Mobile Computing
• Publisher: IEEE
• ISSN: 1536-1233
• ISSN (Online): 1558-0660
• Published Online: 05 March 2024
• Copyright Holders: Copyright: © 2024 IEEE
• First Published: First published in IEEE Transactions on Mobile Computing 2024
• Publisher Policy: Reproduced in accordance with the publisher copyright policy