Quantum Support Vector Regressor for Robust Channel Estimation in OFDM Systems

Channel estimation in OFDM systems is generally performed based on pilot symbols using least squares (LS). However, in a practical environment where impulse noise may be present, this method may not be effective. In this work we propose the use of quantum kernel in support vector machine (SVM) algorithm for robust channel estimation in OFDM systems and compare its performance with the LS and the classic SVR. 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 in the pilot symbols.