Machine-Learning-Based Throughput Estimation Using Images for mmWave Communications

The human blockage problem is an open issue in next- generation wireless access networks using millimeter-wave (mmWave) communications. A proactive base station (BS) handover system leveraging RGB and depth (RGB-D) cameras is proposed for solving the human blockage problem. RGB-D cameras observe mmWave communication ranges, and BS handover is conducted proactively before a human blockage causes serious performance degradation. However, this system must rely on a scheme that provides a guideline for selecting a BS to which the transfer can be done. In this paper, we propose a mmWave throughput estimation scheme using an online machine learning algorithm and depth images obtained by the RGB-D camera. The algorithm learns the relationship between depth images and measured throughputs, and estimates throughput from depth images. The scheme enables the handover system to estimate throughput quickly and adaptively to the wireless environment without transmitting any control frames. We conducted a proof-of-concept experiment by using a testbed consisting of IEEE 802.11ad mmWave wireless local area network devices and an RGB-D camera. The experiment confirmed that the proposed scheme estimates throughput from a depth image with an RMS error of 114-178 Mbit/s in real time.