Related papers: Uncertainty-Aware Dimensionality Reduction for Cha…
Wireless communication networks rely heavily on channel state information (CSI) to make informed decision for signal processing and network operations. However, the traditional CSI acquisition methods is facing many difficulties:…
Channel state information (CSI) is critical for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Pilot-based channel estimation methods suffer from high pilot overhead and low channel acquisition…
Distance metric learning can be viewed as one of the fundamental interests in pattern recognition and machine learning, which plays a pivotal role in the performance of many learning methods. One of the effective methods in learning such a…
Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming…
Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both…
Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting channel charts usually accurately represent local neighborhood…
Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UMMIMO) systems, due to the high-dimensional channel matrices,…
For massive multiple-input multiple-output (MIMO) systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse…
This work addresses channel estimation in multiple antenna multicell interference-limited networks. Channel state information (CSI) acquisition is vital for interference mitigation. Wireless networks often suffer from multicell…
The monitoring and management of high-volume feature-rich traffic in large networks offers significant challenges in storage, transmission and computational costs. The predominant approach to reducing these costs is based on performing a…
Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$-normalization in…
In low altitude UAV communications, accurate channel estimation remains challenging due to the dynamic nature of air to ground links, exacerbated by high node mobility and the use of large scale antenna arrays, which introduce hybrid near…
Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (training) and target (test) data distributions. In particular,…
In interference channels, channel state information (CSI) can be exploited to reduce the interference signal dimensions and thus achieve the optimal capacity scaling, i.e. degrees of freedom, promised by the interference alignment…
With the development of the sixth-generation (6G) communication system, Channel State Information (CSI) plays a crucial role in improving network performance. Traditional Channel Charting (CC) methods map high-dimensional CSI data to…
In this paper, we propose a learning-based low-overhead channel estimation method for coordinated beamforming in ultra-dense networks. We first show through simulation that the channel state information (CSI) of geographically separated…
Dimensionality reduction is an effective method for learning high-dimensional data, which can provide better understanding of decision boundaries in human-readable low-dimensional subspace. Linear methods, such as principal component…
A transmitter without channel state information (CSI) wishes to send a delay-limited Gaussian source over a slowly fading channel. The source is coded in superimposed layers, with each layer successively refining the description in the…
Channel simulation involves generating a sample $Y$ from the conditional distribution $P_{Y|X}$, where $X$ is a remote realization sampled from $P_X$. This paper introduces a novel approach to approximate Gaussian channel simulation using…
The goal of a denoising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors…