Frequency Synchronization for Low Resolution Millimeter-Wave.

Low resolution data converters can enable power efficient high bandwidth communication at millimeter-wave and terahertz frequencies. Synchronization of such systems is a critical step in accurate decoding, yet current approaches require long block lengths or fail to reach the Cramer Rao Bound (CRB).´ Prior solutions have traditionally been divided into two distinct focuses: algorithms and designed sequences for synchronization. In this paper, we develop a jointly optimized neural architecture for frequency synchronization from configurable sequences and estimators. Our proposed technique uses two neural networks to generate sequences and determine the carrier frequency offset of the sequence after propagating through a channel and applying one-bit quantization. Our simulations show that we can improve estimation performance at low signal to noise ratio (SNR) by up to 8dB at little cost compared to the same estimator without the sequence generator. Our proposed system is fast, efficient, and easily updated, allowing it to handle time-varying systems. In conclusion, we believe further investigation in jointly optimized pilot sequences and estimators will be fundamental to handling signal processing techniques with low resolution data converters.