Performance Analysis of Ambient IoT with Sampling Frequency Offset

In 3GPP Release 18, the ambient Internet of Things (A-IoT) has been introduced to provide ultra-low complexity and ultra-low power consumption, facilitating extensive connectivity in Internet of Things applications. Considering cost constraints, the performance of oscillators in A-IoT communication systems is limited. Oscillators cause frequency deviation that increases over time and leads to a significant challenge in the decoding design of the A-IoT system. In this paper, we investigate an approach to mitigate the effects of sampling frequency offset (SFO) on the performance of A-IoT systems. The method involves Manchester decoding and is supported by both link analysis of A-IoT signals alone and the evaluation of their coexistence with fifth-generation (5G) New Radio (NR) signals. The results illustrate that even though frequency offsets up to 20%, this Manchester decoding approach is also used to mitigate the impact of frequency deviation. The study presented in this paper demonstrates that the Manchester decoding method can withstand sampling frequency offsets caused by misalignment between the transmitter’s Digital-to-Analog Converter and the receiver’s Analog-to-Digital Converter.