Low Complexity 2D Adaptive Channel Estimation for OFDM Based Two-Way Relay Systems

Abstract

This paper introduces a low complexity adaptive channel estimation technique for OFDM based two-way relay systems. The adaptive filter used is known as Group Fast Array Multichannel 2D-Recursive Least Square (GFAM 2DRLS) filter. It has a computational complexity comparable to that of 2D-Normalized Least Mean Square (2D-NLMS) algorithm while maintaining the same convergence rate as the classic 2D Recursive Least Square (2D-RLS) algorithm. It considers the correlation of the channel frequency response in both time and frequency, while estimating the channel. In order to reduce the number of training data for time varying channel, the channel estimation is carried out based on the Decision Directed (DD) principle. It is assumed that the relay is capable of performing complex signal processing tasks. Hence the channel estimation is performed at the relay. Since the Channel State Information (CSI) is available at the relay, it could perform Multiple Input Multiple Output (MIMO) precoding of the transmitted data. Hence CSI is not required at the transmitting nodes. The convergence rate of GFAM 2D-RLS is compared with the existing 2D-NLMS algorithm and the computational complexity at each iteration is tabulated. Simulations are performed using MATLAB.