Multiple Adaptive Frequency Filtering for OFDM Channel Estimation

Transmission of pilot symbols in orthogonal frequency division multiplexing (OFDM) systems allows for an efficient least square (LS) channel estimate on pilots and its time/frequency interpolation over data carrier. In order to improve channel estimate, the correlation among pilot estimated and time-interpolated estimates can be exploited. However, while most approaches consider a single smoothing filter over time-interpolated estimates, we propose the use of multiple adaptive filters that take into account the different correlation among pilot estimate and interpolated estimates. In fact, time-interpolated estimates have different reliability than pilot estimates and thus must be weighted differently in the adaptive filtering. We consider both least mean square (LMS) and Kalman filter approach for the filter coefficient adaptation and aim at low-complexity solutions with respect to alternative optimal techniques. Moreover, as a simpler approach we consider also the use of a single adaptive filter updated taking into account the reliability of the various estimates. Performance results are reported with reference to the DV-B-T standard, showing the merits of the various techniques both in terms of mean square error of the channel estimate and complexity of the adaptive techniques.