NONCOHERENT MAXIMUM-LIKELIHOOD DETECTION FOR AMBIENT BACKSCATTERING COMMUNICATIONS OVER AMBIENT OFDM SIGNALS

Backscattering communications have been recently proposed as an effective enabling technology for massive Internet of Things (IoT) development. A novel application of backscattering, called ambient backscattering (AmBC), has been gaining much attention, wherein backscattering communications exploit existing RF signals without the need for a dedicated transmitter. In such a system, data demodulation process is strongly complicated by the random nature of the illuminating signal, as well as by the presence of the direct-link interference (DLI) from the legacy system. To overcome these shortcomings, one can resort to noncoherent detection strategies, aimed at reducing or even nullifying the amount of a priori information needed to reliably perform signal demodulation. This paper deals with the problem of noncoherent maximum-likelihood (ML) signal detection for backscatter communications over ambient OFDM. The performance of the proposed detector is corroborated through Monte Carlo simulations.