相关论文: Improving Channel Charting using a Split Triplet L…
Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting…
The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system. Since, in static environments, CSI is a function of the transmitter…
Channel Charting is a dimensionality reduction technique that learns to reconstruct a low-dimensional, physically interpretable map of the radio environment by taking advantage of similarity relationships found in high-dimensional channel…
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. In this paper, a model-based deep learning approach to this problem is…
Channel charting (CC) applies dimensionality reduction to channel state information (CSI) data at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. The self-supervised nature of CC…
Distributed massive MIMO is considered a key advancement for improving the performance of next-generation wireless telecommunication systems. However, its efficacy in scenarios involving user mobility is limited due to channel aging. To…
Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel…
Channel charting is an emerging self-supervised method that maps channel state information (CSI) to a low-dimensional latent space, which represents pseudo-positions of user equipments (UEs). While this latent space preserves local…
Channel charting is an emerging self-supervised method that maps channel-state information (CSI) to a low-dimensional latent space (the channel chart) that represents pseudo-positions of user equipments (UEs). While channel charts preserve…
Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space:…
Channel Charting is a dimensionality reduction technique that reconstructs a map of the radio environment from similarity relationships found in channel state information. Distances in the channel chart are often computed based on some…
Channel charting has emerged as a powerful tool for user equipment localization and wireless environment sensing. Its efficacy lies in mapping high-dimensional channel data into low-dimensional features that preserve the relative…
Channel charting creates a low-dimensional representation of the radio environment in a self-supervised manner using manifold learning. Preserving relative spatial distances in the latent space, channel charting is well suited to support…
The sensing and positioning capabilities foreseen in 6G have great potential for technology advancements in various domains, such as future smart cities and industrial use cases. Channel charting has emerged as a promising technology in…
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. It has many potential applications, ranging from…
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that…
Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity relationships in high-dimensional channel state information.…
Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction…
Modern techniques in the Internet of Things or autonomous driving require more accuracy positioning ever. Classic location techniques mainly adapt to outdoor scenarios, while they do not meet the requirement of indoor cases with multiple…
We employ triplet loss as a feature embedding regularizer to boost classification performance. Standard architectures, like ResNet and Inception, are extended to support both losses with minimal hyper-parameter tuning. This promotes…