Quantum Machine Learning for Robust Channel Estimation in Cyclic Prefix-Free OFDM Systems with Impulsive Noise

Abstract

Channel estimation in OFDM systems requires minimal complexity with one-tap equalizers and it is generally performed based on pilot symbols (PS) using least squares (LS). However, in a practical environment where impulsive noise may be present, this method may not be effective; furthermore, the minimal complexity required depends on cyclic prefixes (CP), which must be sufficiently large to cover the channel impulse response. In contrast, the use of PS and CP decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we propose the use of quantum machine learning algorithms for channel estimation in OFDM systems with a reduced number of PS, without PS, and without CP. Our approach involves adapting classical machine learning models, specifically support vector regression and K-means clustering, to their quantum counterparts by integrating quantum kernels and encoding strategies suitable for channel modeling. The performance of the resulting quantum models is evaluated in comparison to LS and classical learning-based estimators. The viability of our approach is substantiated by computational simulation results obtained in frequency-selective channel models with the presence of non-Gaussian impulsive noise interfering with the symbols.