Deep Learning-Aided Phase Noise Mitigation for Backhaul Communication: A Model-Driven Approach

This paper introduces a novel Model-Driven Deep Learning Assisted (MDLA) approach for phase noise (PN) estimation in wideband wireless backhaul scenarios. MDLA estimation combines a model-driven foundation for problem formulation with the computational power of Convolutional Neural Networks (CNN) to tackle the derived problem effectively. Exploiting inherent inter-correlations among PN-affected signals, MDLA method incorporates preceding and subsequent received signals in the PN estimation process. Moreover, a dedicated neural network is crafted to capitalize on input dependencies. This hybrid methodology demonstrates superior performance across diverse wideband scenarios, including constellation sizes, pilot overhead ratios, PN levels, and signal-to-noise ratios. The proposed MDLA scheme exhibits robustness in the face of reduced pilot overhead and achieves nearly optimal results in mitigating PN effects. In a 2 GHz bandwidth system with strong PN, MDLA remains within 0.2 dB and 0.5 dB of the PN -free system in 64-QAM and 256-QAM scenarios, respectively, at a coded bit-error-rate of $10^{-6}$.