VLSI Implemented Data-Aided ML Parameter Estimators of PSK Burst Modems

A high-performance universal modem ASIC that supports several modulation types and burst mode frame formats are under development. The ASIC is designed to work under stringent conditions such as large carrier frequency offset (up to 13% symbol rate) and low signal-to-noise ratio (SNR). Powerful and generic data-aided parameter estimators are necessary to accommodate many modes. In this paper, we present an approximated maximum likelihood (ML) carrier frequency offset estimator, ML joint carrier phase, and timing offsets estimator and their systolic VLSI implementations for PSK burst modems. The performances are close to the Cramer-Rao lower bounds at low SNRs. Compared with theoretical solutions the estimators proposed here are much simpler and easier to implement by the current VLSI technology.