Joint channel and phase noise estimation for mmWave OFDM systems using adaptive Kalman filtering

Abstract

This manuscript presents a joint channel and phase-noise estimation framework for millimeter-wave (mmWave) OFDM systems based on an adaptive extended Kalman filter (EKF). The proposed method formulates a unified state-space model that simultaneously tracks time-varying multipath channels and oscillator phase noise, a critical impairment in 5G NR frequency range 2 (FR2) and emerging 6G transceivers. An innovation-based covariance adaptation mechanism is introduced to enhance estimation accuracy while maintaining computational efficiency. Simulation results demonstrate that the proposed approach achieves up to a 5 dB SNR gain at a target BER of 10⁻³, maintains NMSE below 10⁻³ for linewidths up to 200 kHz, and reduces ICI power by over 20 dB compared to conventional estimators. Moreover, the adaptive EKF delivers near-UKF accuracy with approximately 40% to 60% lower runtime, demonstrating its scalability for real-time mmWave implementations. These findings establish the proposed algorithm as a practical and robust solution for phase-noise–limited OFDM receivers in next- generation wireless systems.