Green Learning-Based Beam Tracking for Robust and Efficient Communications in Harsh Channel Environments

Abstract

This work proposes novel beam tracking methods to significantly reduce training overhead and combat harsh channel environments that lead to tracking failures, including blockages, multipaths, user movements, and rotations. The proposed beam tracking system is designed using a new low-carbon-footprint machine learning technique called Green Learning (GL). We introduce a GL-based prediction model (GLPM) along with an anti-blockage method (ABM) and an anti-rotation method (ARM) to enhance robustness. In a 5G NR beam management scenario utilizing a near-realistic ray-tracing channel, the proposed integrated beam tracking scheme significantly reduces the number of Synchronization Signal Blocks (SSBs) by 46–57 and outperforms traditional beam sweeping methods, achieving 1.3 to 4 times higher data rates compared to sweeping with 64 SSBs. Additionally, the proposed approach delivers superior accuracy while maintaining lower computational complexity than deep learning-based methods. Moreover, when examining instantaneous tracking ability, the proposed beam tracking scheme can closely follow ideal cases and rescue frequent tracking failures in conventional beam sweeping caused by harsh channel environments. This rescue is more pronounced in low SNR regions, where conventional beam sweeping can frequently lose connection entirely, while the proposed scheme maintains satisfactory link quality thanks to its accurate beam tracking that provides sufficient received power. From the experiments, the GLPM accounts for 70% to 93% of the overall operations, resulting in low latency and computational complexity. The ABM and ARM do not need to operate frequently but effectively address the shortcomings of GLPM in specific environments, significantly enhancing overall performance. The superior tracking ability and reduction in training redundancy demonstrate that the proposed integrated beam tracking technique is a promising solution for future 5G/6G beam management.