Smart Refletion and Passive Communication: Synergizing Backscatter Communication and 6G Technologies for Future Wireless Networks

Abstract

Backscatter Communication (BackCom) has been envisioned as a key enabler for ubiquitous connectivity in the Internet of Things (IoT). It achieves this by passively modulating and reusing existing radio-frequency signals. To become a pivotal technology within the IoT framework, BackCom faces various challenges including limited coverage, inflexibility in deployment, low data rates etc. This thesis is dedicated to tackling the inherent issues within BackCom. It does so by delving into a series of system configurations that expand the boundaries in terms of increased range and improved bit error rate (BER). Furthermore, it future-proofs BackCom by investigating how it can synergize with other emerging technologies like intelligent reflecting surfaces (IRS), wireless power transfer (WPT) and machine learning. In chapter 2, we consider WPT from an energy transmitter (ET) employing retrodirective WPT using a large phased antenna array to an energy receiver (ER) capable of ambient backscatter. The advantage of retrodirective WPT is that no explicit channel estimation is needed at the ET and the use of ambient backscattering eliminates the need for active transmission at the ER. We propose a training sequence design, i.e., pattern of varying the reflection coefficient at the ER, to eliminate the direct-link interference from the ambient source. We show that when the ambient symbol duration is known, the ambient interference is fully cancelled by the proposed design. We analytically model the system and find the average harvested power at the ER considering Nakagami-m fading channels and non-linear energy harvesting model. Our results clearly show that the proposed solution is robust to a small timing offset mismatch at the correlator. When interference from undesired neighboring sources in the ambient environment is not significant, the ER can successfully harvest tens to hundreds of micro-watts of power, which is an important improvement for low-power IoT devices. In chapter 3, we address a key issue in ambient BackCom viz. the weakness of backscatter signal in the presence of strong direct-link interference from the original RF source. This limits the bit error rate (BER) and hence the transmission rate and range of ambient BackCom systems. Meanwhile, the IRS offers new degrees of freedom in enhancing a variety of systems by transforming their propagation media and signals. In this work, we devise a novel scheme to improve the detection performance of an ambient BackCom system using an IRS located in its proximity. The IRS augments the backscatter signal quality at the receiver by adjusting its phase shifts to balance signal strengths, ultimately improving the performance of energy detection at the receiver. Our results clearly show that an IRS of reasonable size can considerably improve the BER performance of ambient backscatter, which is an important improvement for low power IoT systems. In chapter 4, we address the issue of limited range in monostatic BackCom systems by considering such a system assisted by an IRS and controlled seamlessly by data driven deep learning (DL) based approach. We propose a deep residual convolutional neural network (DRCNN) named BackIRS-Net that exploits the unique coupling between the IRS phase shifts and the beamforming at the reader, to jointly optimize these quantities in order to maximize the effective signal to noise ratio (SNR) of the backscatter signal received at the reader. We show that the performance of a trained BackIRS-Net is close to the conventional optimization based approach while requiring much less computational complexity and time, which indicates the utility of this scheme for real-time deployment. Our results show that an IRS of moderate size can significantly improve backscatter SNR, resulting in range extension by a factor of 4 for monostatic BackCom, which is an important improvement in the context of BackCom based IoT systems.