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Found 6 papers, 0 papers with code
On the Accuracy of Hotelling-Type Asymmetric Tensor Deflation: A Random Tensor Analysis
This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions.
Unsourced Random Access With Tensor-Based and Coherent Modulations
Unsourced random access (URA) is a particular form of grant-free uncoordinated random access wherein the users' identities are not associated to specific waveforms at the physical layer.
On the Accuracy of Hotelling-Type Tensor Deflation: A Random Tensor Analysis
Leveraging on recent advances in random tensor theory, we consider in this paper a rank-$r$ asymmetric spiked tensor model of the form $\sum_{i=1}^r \beta_i A_i + W$ where $\beta_i\geq 0$ and the $A_i$'s are rank-one tensors such that $\langle A_i, A_j \rangle\in [0, 1]$ for $i\neq j$, based on which we provide an asymptotic study of Hotelling-type tensor deflation in the large dimensional regime.
Triplet-Based Wireless Channel Charting: Architecture and Experiments
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 fundamental parameters governing its distribution.
DNN-based Localization from Channel Estimates: Feature Design and Experimental Results
We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems.
CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach
We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system.
