Raju Shrestha

• Shrestha, Raju; Ha, Tien Ngoc; Pham, Viet Quoc & Romero, Daniel (2023). Radio Map Estimation in the Real-World: Empirical Validation and Analysis, IEEE Conference on Antenna Measurements & Applications (CAMA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-2304-7. s. 169–174. doi: 10.1109/CAMA57522.2023.10352759. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Shrestha, Raju; Romero, Daniel & Prabhakar Chepuri, Sundeep (2022). Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 22(1), s. 627–641. doi: 10.1109/TWC.2022.3197087. Vis sammendrag

  Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous existing techniques construct radio maps from spatially distributed measurements, the locations of such measurements are 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.

• Romero, Daniel; Shrestha, Raju; Teganya, Yves & Prabhakar Chepuri, Sundeep (2020). Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs, 2020 IEEE International Workshop on Machine Learning for Signal Processing. IEEE conference proceedings. ISSN 978-1-7281-6662-9. doi: 10.1109/MLSP49062.2020.9231595.
• Romero, Daniel; Shrestha, Raju; Teganya, Yves & Prabhakar Chepuri, Sundeep (2020). Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs. Machine Learning for Signal Processing. ISSN 1551-2541. s. 1–6. doi: 10.1109/MLSP49062.2020.9231595. Vis sammendrag

  Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning algorithm relies on a map uncertainty metric to collect measurements at those locations where they are more informative. An online Bayesian learning algorithm is developed to update the map estimate and uncertainty metric every time a new measurement is collected, which enables real-time operation.

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