Spectrum Cartography via Coupled Block-Term Tensor Decomposition

Abstract

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 work 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 \textit{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.

Accepted version; IEEE Transactions on Signal Processing (27-Apr-2020)

References in corpus (13)

• Tensor Decomposition for Signal Processing and Machine Learning
• Robust Low-rank Tensor Recovery: Models and Algorithms
• Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
• Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
• Group-Lasso on Splines for Spectrum Cartography
• Learning Power Spectrum Maps from Quantized Power Measurements
• A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion
• Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
• Joint Tensor Factorization and Outlying Slab Suppression with Applications
• Tensor Completion from Regular Sub-Nyquist Samples
• Compressed Wideband Spectrum Sensing: Concept, Challenges and Enablers
• Limited Feedback Channel Estimation in Massive MIMO with Non-uniform Directional Dictionaries
• Primary User Localization and Online Radio Cartography via Structured Tensor Decomposition

Cited by in corpus (11)

• Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
• Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
• Propagation Map Reconstruction via Interpolation Assisted Matrix Completion
• Block-Term Tensor Decomposition: Model Selection and Computation
• Grassmannian Optimization for Online Tensor Completion and Tracking with the t-SVD
• RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks
• Computing Large-Scale Matrix and Tensor Decomposition with Structured Factors: A Unified Nonconvex Optimization Perspective
• CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks
• Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
• 3D Spectrum Awareness for Radio Dynamic Zones Using Kriging and Matrix Completion
• Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm