UAV-Aided Backscatter Communications: Performance Analysis and Trajectory Optimization

In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

I. Introduction

Recently, machine-type communication (MTC) has been considered as an enabling technology to provide enormous connections for various devices, which is applied in domains spanning e-health, smart environment, and public security [1]. Since there have been a surging amount of data and information from devices, MTC plays a significant role in a number of applications. Due to the fact that most MTC devices have sparse activity and low signaling overhead, random access can be employed for MTC to provide an efficient way to support massive MTC devices with minimized network overload [2]. Many recent works conduct discussion on approaches such as contention-based random access procedures and overload control schemes in respect of the issues in above.