Binomial Line Cox Processes – Modeling Streets Across a City
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Abstract
The current analysis of wireless networks whose transceivers are confined to streets is largely based on Poissonian models, such as Poisson line processes and Poisson line Cox processes. We demonstrate important scenarios where a model with a finite and deterministic number of streets, termed the binomial line process (BLP), is more accurate. We characterize the statistical properties of the BLP and the corresponding binomial line Cox process (BLCP) and apply them to analyze the performance of a network whose access points (APs) are deployed along the streets of a city. To obtain a fine-grained insight into the network performance, we derive the meta-distribution of the signal-to-interference and noise ratio (SINR). Accordingly, we investigate the mean local delay in transmission and the density of successful transmissions. These metrics, respectively, characterize the latency and coverage performance of the network and are key performance indicators of next-generation wireless systems.
Gourab primarily works in two directions of research: stochastic geometry and bandit algorithms. Currently, he is investigating the interplay of these two directions to study the spatiotemporal statistics of learning algorithms deployed in wireless networks. His main application areas are MAC and PHY layer algorithms for 5G and 6G communications. During his PhD, he developed stochastic geometry tools to analyze initial access procedures, adaptive beamforming, user association, and traffic distribution in dual-band networks. He is currently an Assistant Professor in the Department of Electrical Engineering at IIT Delhi.