Millimeter Wave Beamforming Based on WiFi Fingerprinting in Indoor Environment

TL;DR

This paper introduces a WiFi fingerprinting-based millimeter wave beamforming method that significantly reduces setup time while maintaining performance, facilitating efficient beam alignment in indoor 5G environments.

Contribution

It proposes a novel statistical learning approach using WiFi fingerprints to quickly estimate the best mm-wave beam, reducing setup time compared to traditional exhaustive search methods.

Findings

Achieves beamforming with minimal setup time

Maintains performance comparable to exhaustive search

Enables efficient beam coordination in mobile environments

Abstract

Millimeter Wave (mm-w), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cellular networks. Beamforming (BF) is tremendously used with mm-w transmissions to enhance the link quality and overcome the channel impairments. The current mm-w BF mechanism, proposed by the IEEE 802.11ad standard, is mainly based on exhaustive searching the best transmit (TX) and receive (RX) antenna beams. This BF mechanism requires a very high setup time, which makes it difficult to coordinate a multiple number of mm-w Access Points (APs) in mobile channel conditions as a 5G requirement. In this paper, we propose a mm-w BF mechanism, which enables a mm-w AP to estimate the best beam to communicate with a User Equipment (UE) using statistical learning. In this scheme, the fingerprints of the UE WiFi signal and mm-w best beam identification (ID) are collected in an offline phase on a grid of arbitrary learning points (LPs) in target environments. Therefore, by just comparing the current UE WiFi signal with the pre-stored UE WiFi fingerprints, the mm-w AP can immediately estimate the best beam to communicate with the UE at its current position. The proposed mm-w BF can estimate the best beam, using a very small setup time, with a comparable performance to the exhaustive search BF.