THESIS
2023
1 online resource (xvi, 104 pages) : illustrations (chiefly color)
Abstract
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...[
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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.
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