Spatial Transformers for Radio Map Estimation

Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations to a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a great number of parameters. The first contribution of this paper addresses these limitations by means of an attention-based estimator named Spatial TransfOrmer for Radio Map estimation (STORM). This scheme not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, where the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real datasets.

Radio Map Estimation in the Real-World: Empirical Validation and Analysis

Radio maps quantify received signal strength or other magnitudes of the radio frequency environment at every point of a geographical region. These maps play a vital role in a large number of applications such as wireless network planning, spectrum management, and optimization of communication systems. However, empirical validation of the large number of existing radio map estimators is highly limited. To fill this gap, a large data set of measurements has been collected with an autonomous unmanned aerial vehicle (UAV) and a representative subset of these estimators were evaluated on this data. The performance-complexity trade-off and the impact of fast fading are extensively investigated. Although sophisticated estimators based on deep neural networks (DNNs) exhibit the best performance, they are seen to require large volumes of training data to offer a substantial advantage relative to more traditional schemes. A novel algorithm that blends both kinds of estimators is seen to enjoy the benefits of both, thereby suggesting the potential of exploring this research direction further.

Aerial Base Station Placement: A Gentle Introduction

The deployment of Aerial Base Stations (ABSs) mounted on board Unmanned Aerial Vehicles (UAVs) is emerging as a promising technology to provide connectivity in areas where terrestrial infrastructure is insufficient or absent. This may occur for example in remote areas, large events, emergency situations, or areas affected by a natural disaster such as a wildfire or a tsunami. To successfully materialize this goal, it is required that ABSs are placed at locations in 3D space that ensure a high Quality of Service (QoS) to the ground terminals. This paper provides a tutorial introduction to this ABS placement problem where the fundamental challenges and trade-offs are first investigated by means of a toy application example. Next, the different approaches in the literature to address the aforementioned challenges in both 2D or 3D space will be introduced and a discussion on adaptive placement will be provided. The paper is concluded by discussing future research directions.