Capacity Analysis of Ambient Backscatter System with Bernoulli Distributed Excitation

Abstract

In recent years, building Internet of Things (IoT) systems through backscatter communication techniques has gained rapid popularity. Backscatter communication relying on passive reflections of the existing RF signals enables low-power and low-complexity communication, which exactly meets the requirements of many emerging IoT applications. However, the performance of the backscatter communication systems severely degrades due to the fact that the real-life radio-frequency excitation signals such as WiFi transmissions have dynamic property. To examine how the dynamic property affects the performance of backscatter systems, it is of great significance to theoretically analyze the capacity and data rate. We can then use these analyses to optimize the backscatter systems. In this paper, we investigate the capacity and the achievable data rate of the ambient backscatter with the dynamic excitation. In particular, we model the dynamic source as a Bernoulli distribution and derive the corresponding channel capacity. We then use the maximum a posteriori criterion to build the optimal signal detection algorithm. The numerical results verify our theoretical analysis and prove that the effect of dynamic property is significant. Moreover, we find that the random excitation has much more influence on the system performance than other impact factors, like the signal-to-noise ratio.