Ubiquitous sensing anywhere and anytime is envisioned under the general umbrella of
Internet-of-Things (IoT). The objective of this dissertation is to contribute ultra-low-power
IoT technology, exploiting novel concepts in wireless communications and networking.
The first part of this work studies far field radio frequency (RF) energy harvesting,
taking into account non-linearity, sensitivity, and saturation effects of existing rectenna circuits.
The proposed methodology offers the statistics of the harvested power for any given rectenna model,
under mild assumptions. It is also demonstrated that currently-used linear RF harvesting models in the literature deviate from reality.
In the second part, scatter radio technology, i.e., communication via means of reflection, is studied in order to enable ultra-low-power
radio communication with single-transistor front-ends. The thesis proposes low-complexity detection schemes
as well as decoding techniques for short block-length channel codes,
tailored to coherent, as well as noncoherent reception of scatter radio. The goal was to target resource-constrained, i.e., hardware-``thin'',
scatter radio tags and simple, low-latency receivers. The developed detection and decoding algorithms are based on
composite hypothesis testing framework. Interestingly, it is demonstrated that the bit error rate (BER)
performance gap between coherent and noncoherent reception depends on the kind of
channel codes employed, the fading conditions, as well as the utilized coding interleaving depth.
The third part of this work proposes a multistatic scatter radio network architecture, based on orthogonal signaling, contrasted to existing
architectures for dyadic Nakagami fading. Orthogonal signaling allows for collision free multi-user access for low-bitrate tags.
It is shown that the proposed scatter radio architecture offers better diversity order, more reliable reception, as well as better
field coverage, while demonstrating smaller sensitivity to the topology of the scatter radio tags, compared to existing monostatic architecture.
Finally, the last part of the dissertation studies resource allocation in multi-cell backscatter sensor networks (BSNs). The average long-term
signal-to interference-plus-noise ratio (SINR) of linear detectors is explored for multi-cell BSNs, and subsequently harnessed to
allocate frequency sub-channels at tags. The proposed resource allocation algorithm is based on the Max-Sum inference algorithm and its convergence-complexity trade-off
is quantified.
Experimental studies in an outdoor scatter radio testbed corroborate the theoretical findings of this work.
Hopefully, this thesis will establish the viability of scatter radio for ultra-low-power communications, enabling critical current and future IoT
applications.
(EN)
