International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Edge-Enhanced 6G Intelligence: A Deep Learning Framework for Real-Time Beam Prediction and Blockage Detection Anant Manish Singh1, Krishna Jitendra Jaiswal2, Arya Brijesh Tiwari3, Shifa Siraj Khan4, Sanika Satish Lad5 1,2,3,5 Department of Computer Engineering
Thakur College of Engineering and Technology (TCET), Mumbai, Maharashtra, India 4Department of Information Technology Thakur College of Engineering and Technology (TCET), Mumbai, Maharashtra, India --------------------------------------------------------------------------***--------------------------------------------------------------------------Abstract The integration of edge computing, machine learning (ML) and sixth-generation (6G) wireless communication promises transformative improvements in latency, reliability and throughput. This work introduces a novel deep learning framework that leverages multi-modal edge data and sub-6 GHz channel information to predict optimal millimeter-wave (mmWave) beams and detect link blockages in real time. Using the publicly available DeepSense 6G dataset comprising synchronized camera, LiDAR, radar, GPS and mmWave beam training data we construct a dual-input neural model that processes visual/depth features alongside low-frequency channel state information to infer the top-1 and top-3 mmWave beams and blockage status. Experimental evaluation across urban vehicular scenarios demonstrates a top-1 beam prediction accuracy of 88.7% and blockage detection accuracy of 94.2%, reducing conventional beam training overhead by over 90% and enabling sub-10 ms inference latency at the edge. Comparative analysis against state-of-the-art methods shows a 6.5% accuracy improvement and 20% lower inference delay. Statistical significance is validated via McNemar’s test (p < 0.01). Our results confirm the feasibility of edge-based, ML-driven beam management and blockage prediction, addressing key 6G challenges of mobility, densification and ultra-reliability. The proposed framework paves the way for industry-relevant deployments in autonomous vehicles, augmented reality and IoT-enabled smart cities.
Keywords 6G, edge computing, deep learning, mmWave beam prediction, blockage detection, DeepSense 6G dataset, latency reduction, mobility management
1. Introduction 1.1 Motivation The forthcoming 6G era demands ultra-low latency (<1 ms), ultra-high reliability (>99.999%) and massive connectivity (10⁷ devices/km²), outstripping 5G capabilities[1]. Beamforming at mmWave frequencies is essential for maintaining high data rates but incurs substantial training overhead and sensitivity to blockages [2][3]. 1.2 Edge Intelligence in 6G Edge computing relocates computation closer to end devices, mitigating latency and offloading the core network [4]. Integrating ML models at edge nodes enables real-time inference for beam selection and blockage prediction without cloud dependency[5]. 1.3 Research Gap Existing beam prediction methods often rely solely on sub-6 GHz channel extraction or synthetic datasets, neglecting multimodal sensing and practical vehicular scenarios[6][7]. Blockage detection remains underexplored in real-world, multi-candidate environments.
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