On the Vision-Beam Aided Tracking for Wireless 5G-Beyond Networks Using Long Short-Term Memory with Soft Attention Mechanism

Abstract

The growth of 5G technology and the continuous success of deep learning for various computer vision tasks in healthcare, self-driving cars, visual recognition, and many other areas, brought new challenges in the field of wireless communication. Moreover, 5G-Beyond networks primarily rely on how to maintain line-of-sight (LOS) links between base stations and mobile users. As such, one of the main challenges in 5G-Beyond networks is how to proactively maintain the hand-over mechanism for mobile users before blockages prevent mobile users from communicating, so as to avoid the latency of searching the best beamforming for the best performance. Accordingly, vision-aided millimeter-wave (mmWave) beam and blockage prediction has opened the door for new research for proactive hand-off and resource allocation. The purpose of this paper is to study wireless beam tracking on mmWave bands using deep learning approach evaluated on the Vision-Wireless ViWi-BT dataset [1]. We present how to predict future beam sequences from previously observed beam sequences and images using a long short-term memory (LSTM) network as a base predictive method. As such, we utilize the soft attention mechanism to intelligently choose the most important features and thus we suggest replacing the softmax attention function with different periodic attention functions to eliminate the gradient vanishing problem.