Joint Frame Detection and Channel Parameter Estimation for OOK Free-Space Optical Communications

We consider a free-space optical (FSO) communication link for packet-based transmissions over a turbulence fading channel. The modulation format is on-off keying (OOK) and a unique-word (UW) composed by a known synch pattern is periodically inserted in the data stream to identify the start of frame. Since an avalanche photo-diode (APD) is used for direct detection of the OOK symbols, the photocurrent signal provided by the APD is plagued by a mixture of thermal and shot noise with signal-dependent power. Our goal is the detection of the UW position in the received stream, along with the estimation of the unknown channel attenuation and noise variances. The aforementioned problem has recently been studied by ignoring any information conveyed by data symbols surrounding the UW. In this work, further investigation is conducted in order to determine the maximum likelihood (ML) solution that, in addition to the UW, exploits all the information-bearing symbols belonging to the observation window. The relevant Cramer-Rao bound (CRB) is also evaluated to establish the ultimate accuracy achievable in the estimation process. Since the true ML estimator leads to a computationally intractable multi-dimensional optimization problem, we develop suitable approximations that enable accurate frame synchronization with affordable complexity. We also present an iterative solution to refine the channel and noise power estimates provided by the UW detection procedure. Numerical simulations demonstrate the superiority of the proposed synchronization and estimation schemes with respect to existing alternatives.

Automated summary

This paper investigates a free-space optical communication link using on-off keying modulation over a turbulence fading channel. By considering the information conveyed by data symbols surrounding the known sync pattern, the paper develops suitable approximations to improve frame synchronization accuracy and presents an iterative solution to refine channel and noise power estimates.