3,962 research outputs found
A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction
Spectrum data, which are usually characterized by many dimensions, such as location,
frequency, time, and signal strength, present formidable challenges in terms of acquisition, processing, and visualization. In practice, a portion of spectrum data entries may be unavailable due to the interference during the acquisition process or compression during the sensing process. Nevertheless, the completion work in multi-dimensional spectrum data has drawn few attention to the researchers working in the eld. In this paper, we rst put forward the concept of spectrum tensor to depict the multi-dimensional spectrum data.
Then, we develop a joint tensor completion and prediction scheme, which combines an improved tensor completion algorithm with prediction models to retrieve the incomplete measurements. Moreover, we build an experimental platform using Universal Software Radio Peripheral to collect real-world spectrum tensor data. Experimental results demonstrate that the effectiveness of the proposed joint tensor processing scheme is superior than relying on the completion or prediction scheme only
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
Sixth-generation (6G) mobile communication networks are expected to have
dense infrastructures, large-dimensional channels, cost-effective hardware,
diversified positioning methods, and enhanced intelligence. Such trends bring
both new challenges and opportunities for the practical design of 6G. On one
hand, acquiring channel state information (CSI) in real time for all wireless
links becomes quite challenging in 6G. On the other hand, there would be
numerous data sources in 6G containing high-quality location-tagged channel
data, making it possible to better learn the local wireless environment. By
exploiting such new opportunities and for tackling the CSI acquisition
challenge, there is a promising paradigm shift from the conventional
environment-unaware communications to the new environment-aware communications
based on the novel approach of channel knowledge map (CKM). This article aims
to provide a comprehensive tutorial overview on environment-aware
communications enabled by CKM to fully harness its benefits for 6G. First, the
basic concept of CKM is presented, and a comparison of CKM with various
existing channel inference techniques is discussed. Next, the main techniques
for CKM construction are discussed, including both the model-free and
model-assisted approaches. Furthermore, a general framework is presented for
the utilization of CKM to achieve environment-aware communications, followed by
some typical CKM-aided communication scenarios. Finally, important open
problems in CKM research are highlighted and potential solutions are discussed
to inspire future work
Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels
In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
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