Modeling of Dense CSMA Networks Using Random Sequential Adsorption Process

We model a dense wireless local area network (WLAN) where the access points (APs) employ carrier sense multiple (CSMA)-type medium control (MAC) protocol. In our model, spatial locations of set active APs are modeled using random sequential adsorption (RSA) process, which is more accurate in terms density active compared to Matérn hard-core point process type-II (MHPP-II) commonly used for modeling CSMA networks. Leveraging theory RSA from statistical physics literature, we provide an approximate but analytical result probability $(\tt{MAP})$ typical AP network. Further, present numerical approach determine pair correlation function notation="LaTeX">$(\tt{PCF})$ , useful estimation interference statistics. Using notation="LaTeX">$\tt{PCF}$ result, derive signal-to-interference-plus-noise ratio notation="LaTeX">$(\tt {SINR})$ coverage link validate accuracy theoretical results through extensive Monte Carlo simulations.