Practical Framework for Beam Feature-based Physical Layer Identification in 802.11 ad/ay Networks

The millimeter wave (mmWave) technologies can significantly increase the throughput and user capacity in the future wireless networks. In term of device authentication, due to the usage of highly directional communication link, new physical layer identification (PLI) mechanism based on the spatial-temporal beam features becomes available. However, it is not known how to implement the new PLI mechanism using commodity devices in multiple client scenario in wireless networks. To this end, this paper presents a practical operational framework for the new beam feature-based PLI that is compatible with 802.11ad/ay standards. The low cost of these commodity devices leads to much wider beams, multiple main lobes, and high side lobes which in turn results in frequent sector level sweep (SLS) even for a minimal level of the transmitter-receiver misalignment. The high mobility sensitivity also triggers SLS. The key idea is to utilize the mobility of the mmWave device to collect enough measurements, the beam pattern feature values, from different observation angles where the beam features are extracted. This mobility effect takes advantage of the rich spatial-temporal information of the feature to prevent the system from spoofing. We also propose a novel feature database refinement algorithm to strengthen the database against false accept/reject rates and increase the identification accuracy. The algorithm filters the noisy data collected in the presence of multiple-clients. The proposed operational framework is implemented in commodity 802.11ad/ay devices. We show that the proposed scheme can reach near 100% accuracy even with a minimal feature vector database in real-time scenarios.