Supervised learning and frequency domain averaging‐based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

Abstract

AbstractFilterbank multicarrier with offset quadrature amplitude modulation (FBMC‐OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC‐OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per‐subcarrier filtering using a frequency‐time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC‐OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k‐nearest neighbor‐based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean‐squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE‐MSE and improved bit error rates compared with the popular preamble‐based CE schemes for FBMC‐OQAM, without a priori knowledge of channel's frequency selectivity.

Keywords

• channel estimation
• fbmc‐oqam
• frequency domain averaging
• k‐nearest neighbors
• supervised machine learning