CNN-Based Adaptive Pilot Position Selection for OFDM in Time-Varying Channel Conditions

Shikha Choudhary; OM Dasgupta; Muktimoy Neog; Nikita Yadav; Kabita Chaudhury

doi:10.55524/ijircst.2026.14.5.1

Abstract

Accurate channel estimation plays a major role in the performance of orthogonal frequency division multiplexing (OFDM). The channel estimation performance depends on how effectively the channel state information can be extracted using known pilot symbols. The placement of these pilot symbols influences both channel estimation and subsequent detection performance, and the conventional fixed comb- or block-type pilot arrangements may not always provide the best results in time-varying channel conditions. Therefore, this work presents a Convolutional Neural Network (CNN)-based pilot position selection approach. In the proposed work, we generate a set of various time-varying channel realizations under six different 3GPP channel conditions and utilize the generated dataset to train the CNN. We evaluate these channel realizations using different pilot patterns, enabling the trained CNN to select pilot positions effectively in time-varying channel conditions. To evaluate the effectiveness of the proposed learning-based pilot position selection, we compare the results with Particle Swarm Optimization (PSO)-based pilot position selection and fixed diamond pilot arrangement methods. The simulation results indicate that both the CNN- and PSO-based pilot arrangements achieve better Bit Error Rate (BER) and Normalized Mean Squared Error (NMSE) than the diamond pilot arrangement, with the CNN achieving slightly better performance than the PSO-optimized one. Therefore, a trained CNN is a promising approach for adaptive pilot position selection without using an iterative approach under time-varying channel conditions.

Keywords

CNN, MIMO, OFDM, PSO, Time-Frequency Grid

References

[1] Y. Li, "Pilot-Symbol-Aided Channel Estimation for OFDM in Wireless Systems," IEEE Transactions on Vehicular Technology, vol. 49, no. 4, pp. 1207–1215, Jul. 2000. Available: https://doi.org/10.1109/25.875230

[2] O. Edfors, M. Sandell, J.-J. van de Beek, S. K. Wilson, P. O. Börjesson, "OFDM Channel Estimation by Singular Value Decomposition," IEEE Transactions on Communications, vol. 46, pp. 931–939, Aug. 1998. Available: https://doi.org/10.1109/26.701321

[3] S. Coleri, M. Ergen, A. Puri, A. Bahai, "Channel Estimation Techniques Based on Pilot Arrangement in OFDM Systems," IEEE Transactions on Broadcasting, vol. 48, pp. 223–229, Oct. 2002. Available: https://doi.org/10.1109/TBC.2002.804034

[4] H. Minn, V. K. Bhargava, "An Investigation into Time-Domain Approach for OFDM Channel Estimation," IEEE Transactions on Broadcasting, vol. 46, no. 4, pp. 240–248, Dec. 2000. Available: https://doi.org/10.1109/11.898744

[5] N. Ta?p?nar, ?. ?im?ir, "Bio-Inspired Pilot Design Approach based on Genetic Algorithm for OFDM-IDMA Scheme," European Journal of Science and Technology, no. 19, pp. 466–474, Aug. 2020. Available: https://doi.org/10.31590/ejosat.732528

[6] I. Khan, M. Cheffena, "Optimal Pilot Pattern for Data-Aided Channel Estimation for MIMO-OFDM Wireless Systems," IET Communications, vol. 18, no. 19, pp. 1474–1484, Sept. 2024. Available: https://doi.org/10.1049/cmu2.12840

[7] S. Dhanasekaran, S. Ramalingam, P. Vivek Karthick, D. Silambarasan, "An Improved Pilot Pattern Design-Based Channel Estimation in Wireless Communication Using Distribution Ant Colony Optimization," Simulation Modelling Practice and Theory, vol. 129, p. 102820, 2023. Available: https://doi.org/10.1016/j.simpat.2023.102820

[8] ?. ?im?ir, N. Ta?p?nar, "Pilot Tones Design Using Grey Wolf Optimizer for OFDM–IDMA System," Physical Communication, vol. 25, pp. 259–267, 2017. Available: https://doi.org/10.1016/j.phycom.2017.08.010

[9] M. N. Seyman, N. Ta?p?nar, "Particle Swarm Optimization for Pilot Tones Design in MIMO-OFDM Systems," EURASIP Journal on Advances in Signal Processing, vol. 2011, no. 1, Dec. 2011. Available: https://doi.org/10.1186/1687-6180-2011-10

[10] N. Ta?p?nar, ?. ?im?ir, "Pilot Tones Design using Particle Swarm Optimization for OFDM–IDMA System," Neural Computing and Applications, vol. 31, pp. 5299–5308, Sept. 2019. Available: https://doi.org/10.1007/s00521-018-3366-8

[11] T. O'Shea, J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," IEEE Transactions on Cognitive Communications and Networking, vol. 3, pp. 563–575, Oct. 2017. Available: https://doi.org/10.1109/TCCN.2017.2758370

[12] H. Ye, G. Y. Li, B. H. Juang, "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems," IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, 2018. Available: https://doi.org/10.1109/LWC.2017.2757490

[13] H. Ye, L. Liang, G. Y. Li, B. H. Juang, "Deep Learning-Based End-to-End Wireless Communication Systems with Conditional GANs as Unknown Channels," IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3133–3143, May 2020. Available: https://doi.org/10.1109/TWC.2020.2970707

[14] X. Gao, S. Jin, C. Wen, G. Y. Li, "ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers," IEEE Communications Letters, vol. 22, no. 12, pp. 2627–2630, Dec. 2018. Available: https://doi.org/10.1109/LCOMM.2018.2877965

[15] B. Yue, S. Qiu, C. Yang, L. Peng, Y. Zhang, "Transformer-Empowered Receiver Design of OFDM Communication Systems," Computer Communications, vol. 228, p. 107960, 2024. Available: https://doi.org/10.1016/j.comcom.2024.107960

[16] A. Siriwanitpong, K. Sanada, H. Hatano, K. Mori, P. Boonsrimuang, "Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments," IEEE Access, vol. 13, pp. 13128–13142, 2025. Available: https://doi.org/10.1109/ACCESS.2025.3531009

[17] T. Mata, P. Boonsrimuang, "Optimized CNN-Based Channel Estimation for Zero-Padded Uplink OFDMA in 5G New Radio Over Fast-Fading Channels," Physical Communication, vol. 77, p. 103167, 2026. Available: https://doi.org/10.1016/j.phycom.2026.103167

[18] H. Ju, H. Zhang, L. Li, X. Li, B. Dong, "A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems," Signal Processing, vol. 223, p. 109554, 2024. Available: https://doi.org/10.1016/j.sigpro.2024.109554

[19] M. Soltani, V. Pourahmadi, H. Sheikhzadeh, "Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems," IEEE Wireless Communications Letters, vol. 9, no. 12, pp. 2173–2176, 2020. Available: https://doi.org/10.1109/LWC.2020.3016603

[20] M. B. Mashhadi, D. Gündüz, "Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems," IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6315–6328, Oct. 2021. Available: https://doi.org/10.1109/TWC.2021.3073309

[21] A. Ranjan, B. C. Sahana, "Deep Learning-Based Pilot Allocation for Optimized Channel Estimation and Pilot Contamination Reduction in Massive MIMO-OFDM Systems," Mathematical Modelling of Engineering Problems, vol. 12, no. 1, pp. 329–339, Jan. 2025. Available: https://doi.org/10.18280/mmep.120132

[22] Y. Hong, T. Thaj, E. Viterbo, Delay-Doppler Communications: Principles and Applications. London, U.K.: Academic Press, 2022. Available: https://doi.org/10.1016/C2020-0-01791-3

Cites this article as

S. Choudhary, O. Dasgupta, M. Neog, N. Yadav, K. Chaudhury, "CNN-Based Adaptive Pilot Position Selection for OFDM in Time-Varying Channel Conditions", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 14, no. 5, pp. 1-8, 2026. doi: https://doi.org/10.55524/ijircst.2026.14.5.1

Corresponding Author

Kabita Chaudhury

Assistant Professor, Department of Electronics and Telecommunication Engineering, Jorhat Institute of Science and Technology, Jorhat, India

Download Full Paper

Download PDF

No. of Downloads: 28 | No. of Views: 245

Design of a Robust Hybrid Fuzzy Method for Medical Image Fusion

Mahdi Koohi, Behzad Moshiri, Abbas Shakery .

July 2023 - Vol 11, Issue 4
Non-Vibrational Fault Analysis of Turbojet Engine Bearings by using Deep Neural Networks

Juvith Ghosh, Medha Mani.

July 2020 - Vol 8, Issue 4
Performance Analysis of 64-Channel DWDM System Using EDFA

Saurabh Vats, Vidur Kakar, A.Jabeena.

March 2015 - Vol 3, Issue 2

CNN-Based Adaptive Pilot Position Selection for OFDM in Time-Varying Channel Conditions

Citations

Total View 245

Total Download 28