Abstract
The success of Internet-of-Things (IoT) relies on enabling the reliable exchange of data at low-rate and low-power among billions of battery-operated energy-constrained IoT devices. Ambient backscattering, with its technological capability of simultaneous information and energy transfer is quickly emerging as an appealing solution for this communication paradigm. In this paper, we investigate the detection of binary data transmitted using ambient backscatter at a receiver tracking the channel state information (CSI) of a flat-fading Rayleigh channel, and characterize the corresponding performance in terms of the bit-error probability. A binary hypothesis testing problem is formulated for the received signal and the performance of the receiver under mean threshold (MT) detection technique is analyzed. Two main contributions of the analysis that distinguish this work from the prior art are the characterization of the average signal energy in terms of the exact conditional density functions, and the characterization of average bit error rate (BER) expression for this setup. The key challenge lies in the handling of correlation between channel gains of two hypotheses for the derivation of joint probability distribution of magnitudes of channel gains that is needed for the BER analysis.
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