Abstract
Conference Title: 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC) Conference Start Date: 2017, Jan. 8 Conference End Date: 2017, Jan. 11 Conference Location: Las Vegas, NV, USA Although Wireless Gigabit (WiGig) access operating in the 60 GHz band plays a significant role towards multi-Gbps WLANs, its transmission suffers from harsh propagation loss and path blocking reducing its transmission range to be few meters around a WiGig access point/station (AP/STA). Consequently, directional transmissions using antenna beamforming is tremendously used in WiGig communication. In this paper, a novel approach of leveraging Wi-Fi channel fingerprints for localizing WiGig coverage area along with reducing its beamforming training (BT) complexity over conventional exhaustive search BT is proposed. The proposed approach is motivated by the fact that Wi-Fi fingerprints, WiGig coverage area and the best beam identifications (IDs) of a WiGig AP and STA are all location dependent. Hence, by linking Wi-Fi fingerprints with WiGig information, e.g., WiGig coverage and the best AP/STA beam IDs, using statistical learning, Wi-Fi fingerprints comparisons can be used to detect if a WiGig STA is within the coverage area of a WiGig AP or not and which AP/STA beam IDs are expected to maximize the link quality. Experimental work in real indoor environment is conducted to prove the effectiveness of the proposed approach compared to the conventional exhaustive search BT.