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Deep Learning-based Pilot Adaptation and Channel Estimation in OFDM Systems

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Abstract

Clear communication over wireless channels demands overcoming their disruptive effects. Doubly-selective fading channels, with rapidly changing parameters, pose a particular challenge for accurate channel estimation. Traditional models often falter, lacking solutions or becoming overly complex. This is where deep learning takes center stage. In OFDM systems, pilots assist in estimating the channel response, but they also come at the cost of reduced data throughput. Adaptive adjustment of pilot patterns based on the channel state offers a promising solution. This paper introduces a deep learning-based framework that leverages adaptive pilots for fast-varying channels. We employ two deep neural networks. First, the pilot adaptation network dynamically selects the pilot pattern, reacting to the channel coherence bandwidth. Second, the channel estimation network extracts features from the channel frequency response using a 1D convolutional neural network. It then harnesses the power of long short-term memory layers to learn the channel behavior and estimate the response across all pilots and data subcarriers. Training and testing datasets are generated using WINNER II. The entire communication link, equipped with our proposed method, undergoes rigorous simulations, evaluated by both bit error rate and pilot overhead. The simulation results illustrate that the proposed scheme outperforms the previous methods, because it yields the same or better errors with less pilot overhead. This translates to a substantial data rate boost, paving the way for faster wireless communication.

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Data Availability

The data that support the findings of this study are not openly available and are available from the corresponding author upon reasonable request.

Code Availability

The channel dataset is generated by WINNER II. The deep neural networks are trained in Python using Keras and exported to MATLAB, where the overall link is simulated. All codes are available from the corresponding author upon request.

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Authors and Affiliations

  1. Department of Information and Communication Technology (ICT), IRIB University, Tehran, Iran

    Mohammadamin Shahmohammadi, Mohammadali Sebghati & Hassan Zareian

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  1. Mohammadamin Shahmohammadi
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  2. Mohammadali Sebghati
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  3. Hassan Zareian
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Correspondence to Mohammadali Sebghati.

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Shahmohammadi, M., Sebghati, M. & Zareian, H. Deep Learning-based Pilot Adaptation and Channel Estimation in OFDM Systems. Wireless Pers Commun 134, 915–933 (2024). https://doi.org/10.1007/s11277-024-10937-3

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