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Ambient backscatter communication (AmBC) has emerged as a promising paradigm for enabling sustainable low-power operation of Internet of Things devices. This is due to its ability to enable sensing and communication through backscattering ambient wireless signals (e.g., WiFi and TV sig-nals). But a great impediment to AmBC-enabled networks is the difficulty in decoding the backscat-ter signals because the ambient signals are usually modulated and meant for other legacy receivers rather than AmBC devices. Drawing from the ability of machine learning (ML) to enhance the performance of wireless communication systems, some ML-aided techniques have been developed to assist signal detection in AmBC. Hence, this article aims to provide a comprehensive overview of the subject by describing the operation of the AmBC network, highlighting the major challenges to signal detection in AmBC, discussing and com-paring the performance of some existing ML-assisted solutions to AmBC signal detection, and highlighting some future research that could be carried out on the subject. [ABSTRACT FROM AUTHOR]

This paper considers an analytical approach to evaluate the outage behavior of the device-to-device (D2D) communication, which is underlaid with cellular networks, as an enabling technology for Internet-of-Things (IoTs). In such an architecture, a group of IoT devices (IoTDs) communicate with an IoT gateway by reusing the resources of cellular users (CUEs) to enhance the spectral efficiency of the fifth-generation networks. Two interference management schemes are widely used in the literature for the sharing of D2D spectrum, namely the fixed-power margin (FPM) and the cooperative pairing (CooP) schemes. We investigate and compare the performance of the two schemes from the perspective of outage probability (OP). While satisfying the minimal performance of the system, the OP of an arbitrary pair (i.e., one IoTD and one CUE) under both schemes are derived in closed form in terms of hyper-geometric functions via the Mellin transform technique. Moreover, for the CooP scheme, an iterative alternating Dinkelbach algorithm is proposed as an outage-optimal power allocation scheme. Analytical and simulation results reveal that the CooP scheme is the outage optimum for the high SNR regime while the FPM scheme is the optimal one for the low SNR regime. Simulation results also show that the suitable power margin of the FPM scheme lies in between 2 and 3 dB. Under these two interference management schemes, the accuracy of the analytical results is verified through numerical simulation and it turns out that these are well matched. [ABSTRACT FROM AUTHOR]