Joint Communication and Sensing in OTFS-based UAV Networks

We consider the problem of accurately localizing $N$ unmanned aerial vehicles (UAV) in 3D space where the UAVs are part of a swarm and communicate with each other through orthogonal time-frequency space (OTFS) modulated signals. The OTFS communication system operates in the delay-Doppler domain and can simultaneously provide range and velocity information about the scatterers in the channels at no additional cost. Each receiving UAV estimates the multipath wireless channel on each link formed by the line-of-sight (LoS) transmission and by the single reflections from the remaining $N-2$ UAVs. The estimated channel delay profiles are communicated to an edge server to estimate the location and velocity of the UAVs from the relative echo delay (RED) measurements between the LoS and the non-LoS paths. To accurately obtain such estimations, we propose a solution called Turbo Iterative Positioning (TIP), initialized by a belief-propagation approach. Enabling a full cold start (no prior knowledge of initial positions), the belief propagation first provides a map associating each echo to a reflecting UAV. The localization of the $N$ UAVs is then derived by iteratively alternating a gradient descent optimization and a refinement of the association maps between UAVs and echos. Given that the OTFS receivers also acquire the Doppler shifts of each path, the UAV's velocities can be sensed jointly with communication. Our numerical results, obtained also using real-world traces, show how the multipath links are beneficial to achieving very accurate position and velocity for all UAVs, even with a limited delay-Doppler resolution. The robustness of our scheme is proven by its performance approaching the Cramer-Rao bound.