Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Abstract

Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous techniques have been proposed to construct radio maps from spatially distributed measurements, the locations of such measurements are assumed predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.

Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

“…A user terminal in cellular networks may function as a sensing device. In fact, the RME formulation can be extended to accommodate the decision on where to acquire the next measurements given the past measurements and their locations [19]. This spectrum surveying task will be discussed in Sec.…”

“…In this context, kriging is probably the simplest and most popular approach [1], [9], [19]. There are several ways to arrive at the kriging estimator.…”

“…Thus, a more sensible approach would be to collect measurements at a small set of highly informative locations and apply an estimator such as those described so far to construct the entire map. To this end, the work in [19] proposes radio map estimators that not only provide the estimated map but also an uncertainty map that indicates how informative a measurement would be at each location given the measurements collected so far. Using such an uncertainty map, a route planning algorithm determines a trajectory through areas of high uncertainty.…”

“…Such data sets would also open the door to devising improved uncertainty metrics. Remarkably, these can be used for improving spectrum surveying techniques [19].…”

Deep Learning for Reduced Sampling Spatial 3-D REM Reconstruction

“…1) Ray Tracing (RT) methods -regarded as the most accurate modeling tool for this purpose as it estimates the strength of multiple signal beams reflected from the objects between the transmitter (TX) and the RX, and accounts for the materials they are made of [7], [8], [12]. Nevertheless, its speed is prohibitively slow for real-time applications, which is why this tool has often been used for RF data generation.…”