We introduce a new metric in stochastic geometry analysis called the statistics of successive successful transmissions. In this talk, we will discuss this new metric from the perspective of automotive radar networks, where the avatar of this metric will be called the 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. To optimize the target tracking probability in high interference scenarios, we introduce and 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.
Gourab Ghatak is an Assistant Professor at the Department of Electrical Engineering at IIT Delhi, where he works on stochastic modeling of wireless networks. His primary research areas are stochastic geometry and online learning algorithms. In both these directions, he focuses on developing new models for investigating PHY and MAC layer wireless communication protocols. Currently, he is investigating the interplay of these two directions to study the spatiotemporal statistics of the performance of learning algorithms deployed in wireless networks.