Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

Abstract

Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this challenge, this paper leverages mmWave and LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they occur. This allows the network to make proactive decisions for hand-off/beam switching, enhancing the network reliability and latency. More specifically, this paper addresses the following key questions: (i) Can we predict a line-of-sight link blockage, before it happens, using in-band mmWave/THz signal and LiDAR sensing data? (ii) Can we also predict when this blockage will occur? (iii) Can we predict the blockage duration? And (iv) can we predict the direction of the moving blockage? For that, we develop machine learning solutions that learn special patterns of the received signal and sensory data, which we call pre-blockage signatures, to infer future blockages. To evaluate the proposed approaches, we build a large-scale real-world dataset that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising algorithm that applies some pre-processing to the LiDAR data. Based on the real-world dataset, the developed approaches are shown to achieve above 95% accuracy in predicting blockages occurring within 100 ms and more than 80% prediction accuracy for blockages occurring within one second. Given this future blockage prediction capability, the paper also shows that the developed solutions can achieve an order of magnitude saving in network latency, which further highlights the potential of the developed blockage prediction solutions for wireless networks.

Spectral-Based Proactive Blockage Detection for Sub-THz Communications

“…However, this approach requires dynamic cooperation between UEs in real-time, which might not be feasible in practice. Finally, the authors in [10], [11] observed the so-called "pre-blockage signatures" that occurred in mmWave/sub-THz channel before blockage events, which we also observed in our experiments. Subsequently, by operating in the time domain they utilized a recurrent neural networks for the proactive detection of blockage events.…”

“…They last for approximately 100-200 ms which is sufficiently long not only to detect the blockage before it occurs but also to take actions to avoid it, e.g., reroute the active session to the back-up link by utilizing the multiconnectivity functionality [5], [12]. We also note that similar behavior was reported in [10], [11] for mmWave band.…”

“…Specifically, for small values of the threshold multiplier k the proposed approach allows to detect blockage very early as E[D] > 150 ms. However, it is sensitive to variations in the computed STFT values, leading to a false alarm rate higher than 15 events/s for Multimodal CNN, 0.8s [11] Multimodal FC, 0.8s [11] (a) Blockage detection probability all the considered values of the EWMA smoothing threshold. When k increases, the mean time to blockage begins to decrease, which is also associated with a rapid decrease in the false alarm rate.…”

“…To this end, Fig. 3(a) illustrates a comparison with the three ML methods presented in [10], [11]. They do not depend on the threshold multiplier k and therefore remain constant.…”

Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition

“…This study is primarily concerned with predicting the timing of LoS link blockage, a task that can be accomplished by knowing the range and height of an object. While comprehensive object dimensions, such as length and width, are of lesser importance for this purpose, they will become crucial for estimating the duration of link blockages [27]. Investigating these full object dimensions represents a potential area of research expansion in the future, building upon this work.…”