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1. Coupled Block-term Tensor Decomposition Based Blind Spectrum Cartography

   https://doi.org/10.1109/IEEECONF44664.2019.9048667  

   Zhang, Guoyong; Fu, Xiao; Wang, Jun; Hong, Mingyi (November 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers)

   Spectrum cartography aims at estimating the pattern of wideband signal power propagation over a region of interest (i.e. the radio map)—from limited samples taken sparsely over the region. Classical cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio map---but fine-grained emitter-level RF information is of great interest. In addition, most existing cartography methods are based on random geographical sampling that is considered difficult to implement in some cases, due to legal/privacy/security issues. The theoretical aspects (e.g., identifiability of the radio map) of many existing methods are also unclear. In this work, we propose a radio map disaggregation method that is based on coupled block-term tensor decomposition. Our method guarantees identifiability of the individual wideband radio map of each emitter in the geographical region of interest (thereby that of the aggregate radio map as well), under some realistic conditions. The identifiability result holds under a large variety of geographical sampling patterns, including many pragmatic systematic sampling strategies. We also propose an effective optimization algorithm to carry out the formulated coupled tensor decomposition problem. 

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2. Deep Generative Model Learning For Blind Spectrum Cartography with NMF-Based Radio Map Disaggregation

   https://doi.org/10.1109/ICASSP39728.2021.9413382  

   Shrestha, Sagar; Fu, Xiao; Hong, Mingyi (June 2021, IEEE ICASSP 2021)

   null (Ed.)

   Spectrum cartography (SC) aims at estimating the multi-aspect (e.g., space, frequency, and time) interference level caused by multiple emitters from limited measurements. Early SC approaches rely on model assumptions about the radio map, e.g., sparsity and smoothness, which may be grossly violated under critical scenarios, e.g., in the presence of severe shadowing. More recent data-driven methods train deep generative networks to distill parsimonious representations of complex scenarios, in order to enhance performance of SC. The challenge is that the state space of this learning problem is extremely large—induced by different combinations of key problem constituents, e.g., the number of emitters, the emitters’ carrier frequencies, and the emitter locations. Learning over such a huge space can be costly in terms of sample complexity and training time; it also frequently leads to generalization problems. Our method integrates the favorable traits of model and data-driven approaches, which substantially ‘shrinks’ the state space. Specifically, the proposed learning paradigm only needs to learn a generative model for the radio map of a single emitter (as opposed to numerous combinations of multiple emitters), leveraging a nonnegative matrix factorization (NMF)-based emitter disaggregation process. Numerical evidence shows that the proposed method outperforms state-of-the-art purely model-driven and purely data-driven approaches 

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3. NUWBS: Non-Uniform Wavelet Bandpass Sampling for Compressive RF Feature Acquisition

   Pelissier, Michael; Studer, Chrsitoph (June 2017, Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop)

   Feature extraction from wideband radio-frequency (RF) signals, such as spectral activity, interferer energy and type, or direction- of-arrival, finds use in a growing number of applications. Compressive sensing (CS)-based analog-to-information (A2I) converters enable the design of inexpensive and energy-efficient wideband RF sensing solutions for such applications. However, most A2I architectures suffer from a variety of real-world impairments. We propose a novel A2I architecture, referred to as non-uniform wavelet bandpass sampling (NUWBS). Our architecture extracts a carefully-tuned subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of most existing A2I converters. We use simulations to show that NUWBS approaches the performance limits of l1-norm-based sparse signal recovery. 

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4. RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring

   Xie, Zongxing; Nederlander, Ava; Park, Isac; Ye, Fan (January 2023, ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies)

   Continuous monitoring of respiration provides invaluable insights about health status management (e.g., the progression or recovery of diseases). Recent advancements in radio frequency (RF) technologies show promise for continuous respiration monitoring by virtue of their non-invasive nature, and preferred over wearable solutions that require frequent charging and continuous wearing. However, RF signals are susceptible to large body movements, which are inevitable in real life, challenging the robustness of respiration monitoring. While many existing methods have been proposed to achieve robust RF-based respiration monitoring, their reliance on supervised data limits their potential for broad applicability. In this context, we propose, RF-Q, an unsupervised/self-supervised model to achieve signal quality assessment and quality-aware estimation for robust RF-based respiration monitoring. RF-Q uses the recon- struction error of an autoencoder (AE) neural network to quantify the quality of respiratory information in RF signals without the need for data labeling. With the combination of the quantified sig- nal quality and reconstructed signal in a weighted fusion, we are able to achieve improved robustness of RF respiration monitor- ing. We demonstrate that, instead of applying sophisticated models devised with respective expertise using a considerable amount of labeled data, by just quantifying the signal quality in an unsupervised manner we can significantly boost the average end-to-end (e2e) respiratory rate estimation accuracy of a baseline by an improvement ratio of 2.75, higher than the gain of 1.94 achieved by a supervised baseline method that excludes distorted data. 

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5. Trust in 5G Open RANs through Machine Learning: RF Fingerprinting on the POWDER PAWR Platform

   Reus-Muns, Guillem; Jaisinghani, Dheryta; Sankhe, Kunal Sankhe; Chowdhury, Kaushik (December 2020, IEEE Global Communications Conference)

   5G and open radio access networks (Open RANs) will result in vendor-neutral hardware deployment that will require additional diligence towards managing security risks. This new paradigm will allow the same network infrastructure to support virtual network slices for transmit different waveforms, such as 5G New Radio, LTE, WiFi, at different times. In this multi- vendor, multi-protocol/waveform setting, we propose an additional physical layer authentication method that detects a specific emitter through a technique called as RF fingerprinting. Our deep learning approach uses convolutional neural networks augmented with triplet loss, where examples of similar/dissimilar signal samples are shown to the classifier over the training duration. We demonstrate the feasibility of RF fingerprinting base stations over the large-scale over-the-air experimental POWDER platform in Salt Lake City, Utah, USA. Using real world datasets, we show how our approach overcomes the challenges posed by changing channel conditions and protocol choices with 99.86% detection accuracy for different training and testing days. 

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