Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices, which has emerged as a promising solution to achieve green communication for future Internet of Things (IoT). The key challenge in the development of AmBC is that the signals backscattered by the AmBC Tag are very weak, while the strong ambient RF signals are unknown and uncontrollable. In this thesis, we investigate utilizing multiple antennas to enhance the AmBC system performance.

We first propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader. Both coherent and non-coherent OSTBC are considered, and an accurate approximate linearized and normalized mu...[ Read more ]

Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices, which has emerged as a promising solution to achieve green communication for future Internet of Things (IoT). The key challenge in the development of AmBC is that the signals backscattered by the AmBC Tag are very weak, while the strong ambient RF signals are unknown and uncontrollable. In this thesis, we investigate utilizing multiple antennas to enhance the AmBC system performance.

We first propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader. Both coherent and non-coherent OSTBC are considered, and an accurate approximate linearized and normalized multiple-input multiple-output (MIMO) channel model has been derived for the AmBC system. Simulation results show that enhanced bit error rate (BER) performance can be achieved, demonstrating the benefit of using multiple antennas at the Tag and the Reader.

Next, we fully investigate the potential of utilizing MIMO for AmBC including power, multiplexing, and diversity gains. These results can be utilized to motivate the development of MIMO AmBC by providing performance bounds on the MIMO AmBC gains. Capacity analysis, capacity maximization, and beamforming optimization are presented based on the reformulated MIMO AmBC channel model. Utilizing comparisons between AmBC and conventional MIMO, we highlight the unique characteristics of MIMO AmBC.

To overcome the drawbacks of averaging detector used in the above two works that the data rate is low and there is an error floor, we propose a new detection strategy based on the complex ratio between signals received from a multi-antenna Reader. An accurate linear channel model approximation is proposed, which allows the derivation of a minimum distance detector and closed-form expressions for BER. A low-complexity and accurate channel state information (CSI) estimation method is also provided. Coding and interleaving are also included to further enhance the BER performance. The results are general, allowing any number of Reader antennas to be utilized in the approach. Numerical results demonstrate that the proposed approach performs better than approaches based on energy detection and the original ratio detectors.

Finally, we propose to improve the achievable rate for RF-powered cognitive radio networks (CRNs) with AmBC by implementing multiple antennas on the secondary transmitter (ST). A low-complexity and time-efficient block coordinate descent (BCD)-assisted exhaustive search algorithm is proposed to find the optimal time sharing and antenna selection scheme to maximize the overall system throughput.

These techniques when taken together provide great enhancement of AmBC in detection, throughput, and channel estimation. Simulation results are utilized to verify all the research results.