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Found 6 papers, 0 papers with code
Model-based Deep Learning for Beam Prediction based on a Channel Chart
In non-standalone or cell-free systems, chart locations computed at a given base station can be transmitted to several other base stations (possibly operating at different frequency bands) for them to predict which beams to use.
Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially.
Model-based learning for location-to-channel mapping
In order to overcome this limitation, this paper presents a frugal, model-based network that separates the low frequency from the high frequency components of the target mapping function.
Channel charting based beamforming
Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference.
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods.
Deep learning for location based beamforming with NLOS channels
Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders.
