Channel charting based beamforming

Autor:	Magoarou, Luc Le, Yassine, Taha, Paquelet, Stephane, Crussière, Matthieu
Rok vydání:	2022
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels. Comment: Asilomar Conference on Signals, Systems, and Computers, Oct 2022, Pacific Grove, United States
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.12340 Zobrazit plný text záznamu View this record from Arxiv