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Found 19 papers, 1 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.
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies.
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 Deep Learning for High-Dimensional Periodic Structures
This work presents a deep learning surrogate model for the fast simulation of high-dimensional frequency selective surfaces.
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.
Experimentally realized physical-model-based wave control in metasurface-programmable complex media
Strikingly, when only phaseless calibration data is available, our model can nonetheless retrieve the precise phase relations of the scattering matrix as well as their dependencies on the metasurface configuration.
Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication
We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments.
Model-Driven End-to-End Learning for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G.
Channel charting based beamforming
Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference.
Efficient Deep Unfolding for SISO-OFDM Channel Estimation
Its architecture, based on a sparse recovery algorithm, allows SISO-OFDM channel estimation even if the system's parameters are not perfectly known.
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.
Efficient channel charting via phase-insensitive distance computation
Channel charting is an unsupervised learning task whose objective is to encode channels so that the obtained representation reflects the relative spatial locations of the corresponding users.
Similarity-based prediction for channel mapping and user positioning
In a wireless network, gathering information at the base station about mobile users based only on uplink channel measurements is an interesting challenge.
mpNet: variable depth unfolded neural network for massive MIMO channel estimation
Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas.
Online unsupervised deep unfolding for MIMO channel estimation
Channel estimation is a difficult problem in MIMO systems.
Channel estimation: unified view of optimal performance and pilot sequences
In this setting, the problem of designing optimal pilot sequences of smallest possible size is studied for any parametric channel model.
Flexible Multi-layer Sparse Approximations of Matrices and Applications
The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors.
Learning computationally efficient dictionaries and their implementation as fast transforms
The resulting dictionary is in general a dense matrix, and its manipulation can be computationally costly both at the learning stage and later in the usage of this dictionary, for tasks such as sparse coding.
