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

5G millimeter wave (mmWave) signals can enable accurate positioning in
vehicular networks when the base station and vehicles are equipped with large
antenna arrays. However, radio-based positioning suffers from multipath signals
generated by different types of objects in the physical environment. Multipath
can be turned into a benefit, by building up a radio map (comprising the number
of objects, object type, and object state) and using this map to exploit all
available signal paths for positioning. We propose a new method for cooperative
vehicle positioning and mapping of the radio environment, comprising a
multiple-model probability hypothesis density filter and a map fusion routine,
which is able to consider different types of objects and different fields of
views. Simulation results demonstrate the performance of the proposed method.

本文提出了一种新的协作车辆定位和无线电环境建图的方法，其中包括多模型概率假设密度滤波器和地图融合例程，能够考虑不同类型的物体和不同的视野，模拟结果证明了所提出方法的性能。

5G 毫米波联合定位和地图构建：基于多模型 PHD 滤波器和地图融合

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD  Filter and Map Fusion

In this paper, we study the path planning for a cellular-connected unmanned
aerial vehicle (UAV) to minimize its flying distance from given initial to
final locations, while ensuring a target link quality in terms of the
large-scale channel gain with each of its associated ground base stations
(GBSs) during the flight. To this end, we propose the use of radio map that
provides the information on the large-scale channel gains between each GBS and
uniformly sampled locations on a three-dimensional (3D) grid over the region of
interest, which are assumed to be time-invariant due to the generally static
and large-size obstacles therein (e.g., buildings). Based on the given radio
maps of the GBSs, we first obtain the optimal UAV path by solving an equivalent
shortest path problem (SPP) in graph theory. To reduce the computation
complexity of the optimal solution, we further propose a grid quantization
method whereby the grid points in each GBS's radio map are more coarsely
sampled by exploiting the spatial channel correlation over neighboring grids.
Then, we solve the approximate SPP over the reduced-size radio map (graph) more
efficiently. Numerical results show that the proposed solutions can effectively
minimize the flying distance of the UAV subject to its communication quality
constraint. Moreover, a flexible trade-off between performance and complexity
can be achieved by adjusting the quantization ratio for the radio map.

本文针对基于蜂窝网络的无人机路径规划问题，提出了使用无线电信号映射技术和图论算法进行路径规划的方法，旨在最小化从起始点到终点的飞行距离，并同时保证在飞行过程中与地面基站之间的通信质量达到预期要求。该方法在降低计算复杂度的同时，能够有效地实现路径规划任务，且可以通过调整无线电信号映射的量化比率来权衡算法的性能和复杂度。