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.

本文提出了基于联合块 - 项张量分解的射频地图恢复和分解方法，保证了在实际条件下每个发射器的单个射频地图的可辨识性，并提出了有效的优化算法来解决射频地图的分解问题。

通过耦合的块张量分解进行频谱制图

Spectrum Cartography via Coupled Block-Term Tensor Decomposition

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs
becomes computational expensive due to the large number of model parameters.
This hinders RNNs from solving many important computer vision tasks, such as
Action Recognition in Videos and Image Captioning. To overcome this problem, we
propose a compact and flexible structure, namely Block-Term tensor
decomposition, which greatly reduces the parameters of RNNs and improves their
training efficiency. Compared with alternative low-rank approximations, such as
tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only
more concise (when using the same rank), but also able to attain a better
approximation to the original RNNs with much fewer parameters. On three
challenging tasks, including Action Recognition in Videos, Image Captioning and
Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of
both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes
17,388 times fewer parameters than the standard LSTM to achieve an accuracy
improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.

通过使用 Block-Term 张量分解方法，可以降低 RNNs 的参数数量从而提高其训练效率。