Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned
  Neural Models   The spectrum cartography (SC) technique constructs multi-domain (e.g.,
frequency, space, and time) radio frequency (RF) maps from limited
measurements, which can be viewed as an ill-posed tensor completion problem.
Model-based cartography techniques often rely on handcrafted priors (e.g.,
sparsity, smoothness and low-rank structures) for the completion task. Such
priors may be inadequate to capture the essence of complex wireless
environments -- especially when severe shadowing happens. To circumvent such
challenges, offline-trained deep neural models of radio maps were considered
for SC, as deep neural networks (DNNs) are able to "learn" intricate underlying
structures from data. However, such deep learning (DL)-based SC approaches
encounter serious challenges in both off-line model learning (training) and
completion (generalization), possibly because the latent state space for
generating the radio maps is prohibitively large. In this work, an emitter
radio map disaggregation-based approach is proposed, under which only
individual emitters' radio maps are modeled by DNNs. This way, the learning and
generalization challenges can both be substantially alleviated. Using the
learned DNNs, a fast nonnegative matrix factorization-based two-stage SC method
and a performance-enhanced iterative optimization algorithm are proposed.
Theoretical aspects -- such as recoverability of the radio tensor, sample
complexity, and noise robustness -- under the proposed framework are
characterized, and such theoretical properties have been elusive in the context
of DL-based radio tensor completion. Experiments using synthetic and real-data
from indoor and heavily shadowed environments are employed to showcase the
effectiveness of the proposed methods.

28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics