Analytical Modeling of Location and Contention Randomness for Node-Assisted WiFi Backscatter Communication

Node-assisted WiFi backscatter (NWB) communication is a promising technology that allows backscatter tags to communicate over long distances and achieve high throughput by using WiFi nodes as relays and enabling concurrent transmissions. However, NWB lacks an accurate theoretical model to evaluate and optimize its network performance, which is challenging to develop due to the location and contention randomness of both WiFi nodes and backscatter tags. Existing backscatter models that only account for one type of randomness are not suitable for NWB. To address this issue, we propose a novel stochastic geometry-based model that captures location and contention randomness as well as the involved dependency and interference (named LoCoR). We use the Matérn hard-core point process and Matérn cluster process to model the repulsive and clustering attributes of the locations of WiFi nodes and backscatter tags, respectively. We also introduce a unified time unit to analyze the randomness and dependency of WiFi and backscatter contentions. Our model factors in various design parameters (e.g., the density and transmission power of tags) and can be used to evaluate their impacts on system throughput. We conduct extensive simulations to validate the accuracy of our model. With our accurate model, one can easily configure the optimal design parameters to maximize system throughput.