MobiLoc: Enhancing COTS mmWave Localization with Neural Network

Millimeter-wave (mmWave) communication technology with high throughput and high reliability attracts much attention in both academic and industrial fields. This technology plays a pivotal role in next-generation communication networks, offering promising solutions for high-speed data transfer. Localization of mobile mmWave communication devices is essential in this context, as it can effectively guide mmWave beam steering, thereby eliminating the need for cumbersome beam alignment processes. However, existing approaches for commercial mmWave communication devices suffer channel state fluctuations and can only work on static devices. To provide accurate localization for mobile mmWave devices, we propose MobiLoc , a neural network-based approach to enhance localization accuracy in mobile scenarios. Our method leverages Channel Frequency Response (CFR) and the angular spectrum for assistance to determine the positions. We first analyze the feasibility of classifying sensing data into different qualities. Then we implement a neural network architecture specifically designed to identify the sensing data with high quality. The effectiveness of our approach is demonstrated through comprehensive experiments conducted on commercial off-the-shelf (COTS) mmWave communication devices. Results show that MobiLoc can increase the localization accuracy significantly and reduce the median angle estimation error of mobile devices to 1.33ˆ with only single items of CFR measurements.