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1. Spectrum Cartography via Coupled Block-Term Tensor Decomposition

   https://doi.org/10.1109/TSP.2020.2993530  

   Zhang, Guoyong; Fu, Xiao; Wang, Jun; Zhao, Xi-Le; Hong, Mingyi (April 2020, IEEE Transactions on Signal Processing)

   Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)—from limited samples taken sparsely over the region. Classic 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, many existing cartography methods explicitly or implicitly assume random spatial sampling schemes that may be difficult to implement, 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 joint radio map recovery and disaggregation method that is based on coupled block-term tensor decomposition. Our method guarantees identifiability of the individual radio map of each emitter (thereby that of the aggregate radio map as well), under realistic conditions. The identifiability result holds under a large variety of geographical sampling patterns, including a number of pragmatic systematic sampling strategies. We also propose effective optimization algorithms to carry out the formulated radio map disaggregation problems. Extensive simulations are employed to showcase the effectiveness of the proposed approach. 

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2. 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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3. 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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4. Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion

   https://doi.org/10.1109/TCSI.2017.2729779  

   Pelissier, Michael; Studer, Christoph (August 2017, IEEE Transactions on Circuits and Systems I: Regular Papers)

   Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of ℓ₁-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications. 

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5. Hyperspectral Super-Resolution Via Coupled Tensor Factorization: Identifiability and Algorithms

   https://doi.org/10.1109/ICASSP.2018.8462525  

   Kanatsoulis, Charilaos I.; Fu, Xiao; Sidiropoulos, Nicholas D.; Ma, Wing-Kin (April 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   This work focuses on the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image that admits high spatial and spectral resolutions. Existing algorithms are mostly based on joint low-rank factorization of the ma-tricized HSI and MSI. This framework is effective to some extent, but several challenges remain. First, it is unclear whether or not the super-resolution image is identifiable in theory under this framework, while identifiability usually plays an essential role in such estimation problems. Second, most algorithms assume that the degradation operators from the super-resolution image to the HSI and MSI are known or can be easily estimated - which is hardly true in practice. In this work, we propose a novel coupled tensor decomposition method that can effectively circumvent these issues. The proposed approach guarantees the identifiability of the super-resolution image under realistic conditions. The method can work even without knowing the spatial degradation operator, which could be hard to accurately estimate in practice. Simulations using AVIRIS Cuprite data are employed to demonstrate the effectiveness of the proposed approach. 

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