Authors
Omkar Milind Mujumdar and J. Nicholas Laneman (University of Notre Dame)
Abstract
Integrated sensing and communication (ISAC) is emerging as a key paradigm for the upcoming 6G wireless systems. ISAC systems seek to reduce resource usage by performing data communication and environmental sensing simultaneously, using the same radio hardware and spectrum. We study a cellular network scenario in which a base station receives signals from multiple transmitters while also tracking a nearby drone using reflections of those signals. The drone position and velocity evolve according to a nearly constant-velocity model, and these time-varying dynamics affect the received signals. We model this interaction using a state-dependent multiple-access channel framework, in which the drone motion is modeled as a time-varying channel state. To enable analytical study, we approximate the drone motion using a finite-state Markov chain and formulate the problem of characterizing achievable transmission rates subject to constraints on state-estimation accuracy.