Next-Level 5G Channel Prediction Using Residual Convolutional Neural Network (ResNet)
DOI:
https://doi.org/10.31272/jeasd.3652Keywords:
Channel Estimation, Convolutional Neural Network, Deep Learning, Fifth-Generation New Radio, Orthogonal Frequency Division Multiplexing, Residual Connection, ResnetAbstract
Accurate channel estimation is critical for achieving the high reliability and low latency required by 5G New Radio (5G NR) systems. Conventional estimators, including Least Squares (LS) and Practical Channel Estimation (PCE), rely on linear models that fail to capture nonlinear channel characteristics. Linear Minimum Mean Square Error (LMMSE) improves accuracy but requires prior channel statistics and a costly matrix inversion, limiting its real-time applicability. This paper proposes a lightweight ResNet-based channel estimator that treats the complex time-frequency channel matrix as a two-dimensional image. Convolutional layers exploit spatial correlations across subcarriers and Orthogonal Frequency Division Multiplexing (OFDM) symbol dimensions, while residual skip connections stabilize training and enable correction over a coarse LS estimate. The model is trained end-to-end using Demodulation Reference Signal (DMRS) pilot patterns in a fully 3GPP-compliant 5G NR environment based on TDL-A and TDL-C channel models (TR 38.901). Simulations over 0–30 dB SNR show consistent superiority over all benchmarks. At SNR = 10 dB, the proposed method achieves a Mean Squared Error (MSE) of 0.0177 — a 45% gain over PCE, ~90% over LS, and 29% over ChannelNet — with only ≈0.29 million parameters, single-stage training, and an inference time of 2–5 ms per OFDM frame, suitable for real-time 5G deployments.
References
M. Alsader, A. A. Barakabitze, and I.-H. Mkwawa, “QoE-Driven Adaptive Video Streaming: Architectures, Techniques, and Future Research Challenges Toward 6G Networks,” IEEE Access, vol. 13, pp. 157408–157441, 2025. doi: https://doi.org/10.1109/ACCESS.2025.3597058.
M. Aloqaily, O. Bouachir, and F. Karray, “Digital twin for healthcare immersive services: Fundamentals, architectures, and open issues,” in Digital Twin for Healthcare, Elsevier, 2023, pp. 39–71. doi: https://doi.org/10.1016/B978-0-32-399163-6.00008-1.
G. Noh et al., “DMRS design and evaluation for 3GPP 5G new radio in a high-speed train scenario,” in Proc. IEEE Global Communications Conference (GLOBECOM), Singapore, 2017. doi: https://doi.org/10.1109/GLOCOM.2017.8254568.
C. Skiribou, F. Elbahhar, and R. Elassali, “DMRS-based channel estimation for railway communications in tunnel environments,” Vehicular Communications, vol. 29, p. 100340, 2021. doi: https://doi.org/10.1016/j.vehcom.2021.100340.
R. Adeogun, “Toward Intelligent Fading Channel Prediction: A Comprehensive Survey,” IEEE Access, vol. 13, pp. 111260–111281, 2025. doi: https://doi.org/10.1109/ACCESS.2025.3583361.
G. Bacci, A. A. D’Amico, and L. Sanguinetti, “MMSE channel estimation in large-scale MIMO: Improved robustness with reduced complexity,” IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 18563–18575, 2024. doi: https://doi.org/10.1109/TWC.2024.3470124.
M. Soltani et al., “Deep learning-based channel estimation,” IEEE Communications Letters, vol. 23, no. 4, pp. 652–655, 2019. doi: https://doi.org/10.1109/LCOMM.2019.2898944.
D. Góez et al., “Computational Efficiency of Deep Learning-Based Super Resolution Methods for 5G-NR Channel Estimation,” in 2024 IEEE Latin-American Conference on Communications (LATINCOM), pp. 1–7, 2024. doi: https://doi.org/10.1109/LATINCOM62985.2024.10770678.
L. Li et al., “Deep residual learning meets OFDM channel estimation,” IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 615–618, 2020. doi: https://doi.org/10.1109/LWC.2019.2962796.
Y. Liang and Z. Zhu, “SE block-assisted ResNet for channel estimation in OFDM system,” in Proc. International Conference on Smart Grid Inspired Future Technologies, Springer, 2022, pp. 386–396. doi: https://doi.org/10.1007/978-3-031-31733-0_32.
H. Luan et al., “Channelformer: Attention-based neural solution for wireless channel estimation and effective online training,” IEEE Transactions on Wireless Communications, vol. 23, no. 4, pp. 3412–3424, 2024. doi: https://doi.org/10.1109/TWC.2023.3244484.
Y. Wang, J. Chang, Z. Lu, F. Yu, J. Wei, and Y. Xu, “Channel estimation of 5G OFDM system based on ConvLSTM network,” 2022 7th International Conference on Communication, Image and Signal Processing (CCISP), pp. 62–66, 2022. doi: https://doi.org/10.1109/ccisp55629.2022.9974588.
M. H. Essai Ali and I. B. M. Taha, “Channel state information estimation for 5G wireless communication systems: Recurrent neural networks approach,” PeerJ Computer Science, vol. 7, p. e682, 2021. doi: https://doi.org/10.7717/peerj-cs.682.
Y. Jin et al., “Channel estimation for mmWave massive MIMO with convolutional blind denoising network,” IEEE Communications Letters, vol. 24, no. 1, pp. 95–98, 2020. doi: https://doi.org/10.1109/LCOMM.2019.2952845.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778, 2016. doi: https://doi.org/10.1109/CVPR.2016.90.
E. Ahmed and G. Abdulkareem, “Rayleigh fading channel estimation based on generalized regression neural network,” Journal of Engineering and Sustainable Development, vol. 27, no. 3, pp. 363–374, 2023. doi: https://doi.org/10.31272/jeasd.27.3.6.
E. Endovitskiy, A. Kureev, and E. Khorov, “Reducing computational complexity for the 3GPP TR 38.901 MIMO channel model,” IEEE Wireless Communications Letters, vol. 11, no. 6, pp. 1133–1136, 2022. doi: https://doi.org/10.1109/LWC.2022.3158095.
H. A. Le et al., "Machine learning-based 5G-and-beyond channel estimation for MIMO-OFDM communication systems," Sensors, vol. 21, no. 14, p. 4861, 2021. doi: https://doi.org/10.3390/s21144861.
X. Zhu, Z. Sheng, Y. Fang, and D. Guo, “A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications,” EURASIP Journal on Wireless Communications and Networking, vol. 2020, no. 1, 2020. doi: https://doi.org/10.1186/s13638-020-01714-4.
P. Dong, H. Zhang, G. Y. Li, I. Gaspar, and N. Naderializadeh, “Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989–1000, 2019. doi: https://doi.org/10.1109/JSTSP.2019.2925975.
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