Optimal Operating Mode Selection of Ambient Backscatter Communication Systems

Ambient backscatter (AB) communication has emerged as an energy-preserving technology in Internet of Things (IoT). Specifically, an AB transmitter communicates with its receiver by backscattering ambient radio frequency (RF) signals instead of active information transmissions. When not operating in the communication mode, the AB transmitter can instead harvest the RF energy to replenish its battery, e.g., for supplying sensing energy consumptions. In this paper, we consider the optimal operating mode selection problem of the AB system that the transmitter decides either backscatter information or harvest energy based on the time-varying ambient RF signal strength. In particular, we aim to maximize the long-term average communication data rate given that an average energy-harvesting budget constraint is met. We formulate the optimization as a 0-1 knapsack problem and propose a dynamic programming (DP) based solution algorithm and a reduced-complexity greedy algorithm. Furthermore, we simulate under various setups, and show that the two proposed algorithms can achieve significant performance improvement over other representative benchmark methods.