| Speaker : | Gourab Ghatak |
| Department of Electrical Engineering at IIT Delhi | |
| Date: | 11/12/2024 |
| Time: | 2:00 pm - 3:00 pm |
| Location: | Amphi 6 |
Abstract
Note: Part I of this talk was presented at LINCS in 2023 (www.lincs.fr/events/seminar-talk-by-gourab-ghatak/).
In this talk, first, we revisit the newly introduced model -the binomial line Cox processes (BLCP) and discuss its validity from the perspective of real world street data. Then, we employ the BLCP and the Poisson line Cox process (PLCP) to emulate a large scale automotive radar network. We will briefly discuss the classical stochastic geometry metrics of detection probability and the meta-distribution.
Then, we introduce a novel metric for stochastic geometry based analysis of automotive radar networks called target tracking probability. Unlike the well-investigated detection probability (often termed as the success or coverage probability in stochastic geometry), the tracking probability characterizes the event of successive successful target detection with a sequence of radar pulses. From a theoretical standpoint, this work adds to the rich repertoire of statistical metrics in stochastic geometry. To optimize the target tracking probability in high interference scenarios, we study a block medium access control (MAC) protocol for the automotive radars and recommend the optimal MAC parameter for a given vehicle and street density. Importantly, we show that the optimal MAC parameter that maximizes the detection probability may not be the one that maximizes the tracking probability. Our research reveals how the tracking event can be naturally mapped to the quality of service (QoS) requirements of latency and reliability for different vehicular technology use-cases. This can enable use-case specific adaptive selection of radar parameters for optimal target tracking.