Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Abstract

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users' location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

“…In [57], the authors proposed a proactive recognition of plugging scenarios and cranking Proactive Handover (PHO) scheme that integrates ML and Computer Vision (CV) for highfrequency vehicle IoT. The emulation consequences showed that the designed object detection and localization (ODL) algorithm achieved blockage prediction to optimize reliability.…”

“…Several typical approaches are presented in this section to comprehensively investigate reliability maximization in 5G and 6G communication systems. In particular, reliability optimization based on ML is defined as the main means of reliability optimization of 5G and 6G IoT networks, which is proposed in [54], [56], [57]. Other concepts, like reliability function [53], LiFi [55] and FastReRoute [58], are effective techniques for reliability optimization of 5G and 6G IoT networks.…”

“…The real natural environment is not tested. [57] Reduce packet loss. FastReRoute Obtain the IoT network reliability.…”

Semantic-Aware Federated Blockage Prediction (SFBP) in Vision-Aided Next-Generation Wireless Network

“…The results show that the proposed scheme significantly improved blockage detection, resulting in a higher handover (HO) success rate. Similarly, the challenges of frequent HO and beam blockage prediction in high-frequency wireless networks are addressed [18]. This study introduces a novel approach that leverages CV to predict blockage events by identifying obstacles, user locations, and speeds.…”

“…The increasing need for reliable connectivity has resulted in the commercial development of fifth-generation (5G) wireless networks [1]. The 5G technology is poised to bring about a major transformation in how smart devices interact with each other to support advanced services such as massive connectivity, ultra-reliable low-latency communication (URLLC), and enhanced mobile broadband (eMBB) [2], [3]. However, with technological advancements, current innovations such as selfdriving cars, augmented or virtual reality, industry 4.0, teleoperation and telepresence, and smart healthcare technologies demand higher bandwidth, low latency, and energy efficiency.…”