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@article{180320,
        author = {Jyotsna Sagar and Dr.Rajesh Rai},
        title = {Data Driven Channel Estimation in Massive Multiple input multiple output system using Deep learning algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {6119-6125},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180320},
        abstract = {Channel estimation in massive multiple-input multiple-output (MIMO) systems is a challenging task. Existing deep learning approaches, which aim to learn the mapping from input signals to target channels, often struggle to accurately estimate channel state information (CSI). In this paper, we approach the problem by treating the quantized received measurements as a low-resolution image and apply deep learning-based image super-resolution techniques to reconstruct the channel. Specifically, we leverage a state-of-the-art convolutional neural network (CNN) framework for channel estimation. This framework processes the quantized received measurements while preserving abundant low-frequency information through skip connections. To address the gradient dispersion problem in the estimator, we introduce dense connections within the residual blocks to maximize information flow between layers. Additionally, the local channel features extracted from different residual blocks are retained through multi-path feature fusion. Simulation results demonstrate that the proposed scheme outperforms both traditional methods and existing deep learning approaches, particularly in low signal-to-noise ratio (SNR) scenarios.},
        keywords = {Massive MIMO, Channel Estimation, Deep Learning, CNN},
        month = {July},
        }