Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking

Autor:	Pratik, Kumar, Amjad, Rana Ali, Behboodi, Arash, Soriaga, Joseph B., Welling, Max
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Information Theory Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/GLOBECOM46510.2021.9685798
Popis:	We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive power of neural networks. Instead of keeping a bank of Kalman filters and choosing one based on approximating the actual dynamics, HKF adapts itself to each dynamics based on the observed sequence. Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information. At high Doppler values, it achieves around 2dB gain over genie Kalman filter. The HKF generalizes well to unseen Doppler, SNR values and pilot patterns unlike LSTM, which suffers from severe performance degradation. Comment: Accepted at IEEE Globecom 2021. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.12561 Zobrazit plný text záznamu View this record from Arxiv