Radio Map Based Path Planning for Cellular-Connected UAV

Abstract

In this paper, we study the three-dimensional (3D) 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 expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the locationdependent and spatially varying channel and interference over the air, we propose a new radio map based path planning for the UAV. Specifically, we first utilize the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid over the region of interest, which are due to fixed and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant during the UAV's flight. Based on the channel gain maps of the GBSs as well as their loading factors, we construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we then derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. To reduce the computational complexity, we further propose a grid quantization method whereby the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids in practice. Then, we solve an approximate SPP over the reduced-size SINR map (graph) more efficiently.Numerical results show that the proposed solutions can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network.

Frequency Selection for Platoon Communications in Secondary Spectrum Using Radio Environment Maps

“…Instead, a reasonable comparison between radio channels, irrespective of the particular system properties, e.g., utilized coder or modulation, can be done on the basis of capacity being the upper bound estimate of the real system throughput. Such an approach is legitimated by the literature, e.g., to compare various frequency bands, in the context of Dynamic Spectrum Access [35], or for the purpose of communications with unmanned aerial vehicles [29]. However, in Sec.…”

“…We represent the problem of minimization of channel switches as a graph, where nodes refer to locations and edges represent available radio channels. A similar approach was proposed for the planning of UAV path [29]. However, in our case due to the nature of the platoon's route, the computational complexity is much lower.…”

“…Moreover, it can be obtained relatively easily, and irrespective of the transmission scheme, and medium access algorithms used. State-of-the-art solutions, e.g., [29], rely on creating location-dependent Signal-to-Interference-and-Noise-Ratio (SINR) maps. This approach makes those maps valid only for devices of equal transmission power and does not reflect the influence of narrowband interference on the signal.…”

QoS Aware Robot Trajectory Optimization with IRS-Assisted Millimeter-Wave Communications

“…Graph-based methods and radio maps are also used in [31] to optimize UAVs flying time distance while ensuring a target QoS requirement.…”

Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory

“…Recent studies have addressed this issue by adjusting the UAV altitude [19], ground BS height and antenna tilting angle [26], while exploiting multi-antenna UAVs [27], transmit power control [28], and optimal subcarrier assignment [29]. For given channel conditions, the UAV-to-ground connectivity should be ensured by limiting the range of UAV operations [18], [30]. Alternatively, one can partly offload the controlling operations to the UAVs so that they can locally carry out decisions even when the connectivity is temporarily lost [11].…”