Low Power Pilot Aided Sub-sample Based Channel Estimation for Mmwave Cellular Systems

The fast temporal changes of a millimeter wave channel necessitate frequent estimation of the channel. Power reduction techniques for the channel estimation process for ultra-wideband 5G systems are highly desirable. High speed analog-to-digital converters for the wideband data conversion and high speed baseband processing of the Nyquist rate digital samples are the main contributors to high power consumption. This work utilizes the subsequence properties of Zadoff Chu sequences and presents a training based channel estimation algorithm that can operate at a fraction of the symbol rate and thus save power. The algorithm also provides a framework for tradeoff between channel estimation performance and computational complexity. This can allow a receiver to go into power saving mode during high signal to noise ratio channel estimation. Our analysis and simulation results show that our sub-Nyquist based approach achieves maximum likelihood performance at full rate sequence for a single path channel model.