Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning
This work addresses positioning challenges in urban millimeter wave environments, offering a potential solution for new systems, though it appears incremental as it builds on existing deep learning and beamforming techniques.
The paper tackles the problem of accurate positioning in millimeter wave communications by converting received radiation into device position using deep learning and a beamforming codebook, achieving average estimation errors below 10 meters in realistic outdoor scenarios with mostly non-line-of-sight conditions.
With millimeter wave wireless communications, the resulting radiation reflects on most visible objects, creating rich multipath environments, namely in urban scenarios. The radiation captured by a listening device is thus shaped by the obstacles encountered, which carry latent information regarding their relative positions. In this paper, a system to convert the received millimeter wave radiation into the device's position is proposed, making use of the aforementioned hidden information. Using deep learning techniques and a pre-established codebook of beamforming patterns transmitted by a base station, the simulations show that average estimation errors below 10 meters are achievable in realistic outdoors scenarios that contain mostly non-line-of-sight positions, paving the way for new positioning systems.