Training sequence based frequency-domain channel estimation for indoor diffuse wireless optical communications

Abstract

Channel estimation is a key technology for wireless optical communication (WOC) systems. Based on the training sequence (TS), this article develops three frequency domain (FD) channel estimation approaches for diffuse wireless optical channels. Considering the propagation property of light and the characteristics of optical modulation, this article establishes a link model for the indoor WOC systems. Using the established link model, three FD channel estimation methods, such as the LS method, the minimum mean square error (MMSE) method and the ORL-MMSE method are proposed. The computational complexity analysis gives time complexities of the three channel estimation methods in FD. This article also evaluates the performance of the proposed three methods by computer simulation, measuring in terms of MSE and BER, respectively. Further more, based on the Cramer-Rao Bound theorem, optimal TSs are found and tabulated for different channel responses and TS lengths. By using the optimal TSs, channel estimation errors measured by BER performance are also investigated.

Blind channel estimation for indoor optical wireless communication systems

“…(1) Least Square Estimation-In the discrete-time channel model when the joint probability density function of the observable random variables is not given, then the estimation of 'h' can be approached by using the method of least squares [4,5]. The LS estimation method minimizes the least square errors to find an optimal estimator for the unknown parameter.…”

“…In [4] and [5] different time domain and frequency domain training sequence based channel estimation methods, such as Least Square method, MMSE method, and OLR-MMSE methods are proposed.…”

“…Therefore, the MMSE estimator is an estimator with minimal MMSE [4,5]. (3) OLR-MMSE Estimation-The MMSE channel estimation method described above is considerably complex as it requires a matrix inversion every time when input changes.…”

“…In Figure 1, ν(t) represents the noise. For most indoor optical wireless communication systems, the noise source ν(t) can be assumed to be white, signal independent with Gaussian distribution [5]. The received signal for the indoor OWC system is given by: …”

“…Channel identification using blind estimation is obtained by only channel output sequences alongwith certain statistical information on input sources [4]. Training sequence based channel estimation for indoor OWC was investigated by Wang et.al [4,5]. The blind channel estimation techniques increase the transmission capability because the elimination of the training sequence reduces the time required for transmission of training sequence.…”