An Agile Adaptation Method for Multi-mode Vehicle Communication Networks

This paper focuses on discovering the impact of communication mode allocation on communication efficiency in the vehicle communication networks. To be specific, Markov decision process and reinforcement learning are applied to establish an agile adaptation mechanism for multi-mode communication devices according to the driving scenarios and business requirements. Then, Q-learning is used to train the agile adaptation reinforcement learning model and output the trained model. By learning the best actions to take in different states to maximize the cumulative reward, and avoiding the problem of poor adaptation effect caused by inaccurate delay measurement in unstable communication scenarios. The experiments show that the proposed scheme can quickly adapt to dynamic vehicle networking environment, while achieving high concurrency and communication efficiency.

An NLoS-based Enhanced Sensing Method for MmWave Communication System

The millimeter-wave (mmWave)-based Wi-Fi sensing technology has recently attracted extensive attention since it provides a possibility to realize higher sensing accuracy. However, current works mainly concentrate on sensing scenarios where the line-of-sight (LoS) path exists, which significantly limits their applications. To address the problem, we propose an enhanced mmWave sensing algorithm in the 3D non-line-of-sight environment (mm3NLoS), aiming to sense the direction and distance of the target when the LoS path is weak or blocked. Specifically, we first adopt the directional beam to estimate the azimuth/elevation angle of arrival (AoA) and angle of departure (AoD) of the reflection path. Then, the distance of the related path is measured by the fine timing measurement protocol. Finally, we transform the AoA and AoD of the multiple non-line-of-sight (NLoS) paths into the direction vector and then obtain the information of targets based on the geometric relationship. The simulation results demonstrate that mm3NLoS can achieve a centimeter-level error with a 2m spacing. Compared to the prior work, it can significantly reduce the performance degradation under the NLoS condition.