Improving Data Transmission in a Wireless Communication Network Using Convolutional Neural Network (CNN)
Keywords:
Improving, data, transmission, wireless, communication, network, convolution, neural, CNNAbstract
In recent years, the increasing demand for high-speed and reliable data transmission in wireless communication networks has presented significant challenges due to dynamic channel conditions, interference, and bandwidth limitations. This study proposes the application of Convolutional Neural Networks (CNNs) to improve data transmission efficiency and reliability in wireless networks. The CNN-based model is designed to intelligently learn and adapt to the spatiotemporal characteristics of wireless signals, enabling enhanced feature extraction, error correction, and adaptive modulation strategies. Through simulation and comparative analysis, the CNN-based system demonstrated significant improvements in key performance metrics such as throughput, signal-to-noise ratio (SNR), bit error rate (BER), and latency when compared with traditional methods. The findings highlight the potential of CNNs to optimize real-time decision-making processes, reduce data loss, and support robust communication in complex and high-interference environments. This approach provides a promising pathway toward the development of intelligent and autonomous wireless networks, essential for the advancement of next-generation communication systems including 5G and Smart Cities. The results obtained were the conventional High Noise Level that caused poor data transmission in a wireless communication network was13 dB. On the other hand, when CNN was integrated into the system, it automatically increased to15.08 dB thereby boosting the performance of the wireless communication network and the conventional improper antenna configuration that caused poor data transmission in a wireless communication network was 2.2dB. Meanwhile, when CNN was integrated into the system, it simultaneously increased it to 3.19 dB. Finally, with these results obtained, it definitely meant that percentage improvement in data transmission in a wireless communication network was 45%.
References
Goldsmith, A. (2005). Wireless communications. Cambridge University Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
O’Shea, T. J., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563–575. https://doi.org/10.1109/TCCN.2017.2758370
Ye, H., Li, G. Y., & Juang, B. H. F. (2018). Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Communications Letters, 7(1), 114–117. https://doi.org/10.1109/LWC.2017.2757490
