In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.
Keywords
GRU, SNR, MCS Selection, Deep Learning, Tactical Communication.
J. K. Park and E. Y. Park, “Implementation of a Remote Healthcare Monitoring System Using Li-Fi,” The Journal of Innovation Industry Technology, vol. 1, no. 2, pp. 15–23, Sep. 2023, doi: 10.60032/jiit.2023.1.2.15.
Fa-tang Chen and Gen-lin Tao, “A novel MCS selection criterion for supporting AMC in LTE system,” 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Oct. 2010, doi: 10.1109/iccasm.2010.5620840.
Chen Yu, Wen Xiangming, Lin Xinqi, and Zheng Wei, “Research on the modulation and coding scheme in LTE TDD wireless network,” 2009 International Conference on Industrial Mechatronics and Automation, May 2009, doi: 10.1109/icima.2009.5156664.
Y. Ji, F. Chen, and L. Liu, “MCS Selection for Performance Improvement in Downlink TD-LTE System,” 2012 Second International Conference on Business Computing and Global Informatization, Oct. 2012, doi: 10.1109/bcgin.2012.184.
A. J. Goldsmith and S.-G. Chua, “Adaptive coded modulation for fading channels,” IEEE Transactions on Communications, vol. 46, no. 5, pp. 595–602, May 1998, doi: 10.1109/26.668727.
R. Fantacci, D. Marabissi, D. Tarchi, and I. Habib, “Adaptive modulation and coding techniques for OFDMA systems,” IEEE Transactions on Wireless Communications, vol. 8, no. 9, pp. 4876–4883, Sep. 2009, doi: 10.1109/twc.2009.090253.
Harivikram T S, Harikumar R, Babu C G and Murugamanickam P, “Adaptive Modulation and Coding Rate for OFDM Systems,” International Journal of Emerging Technology and Advanced Engineering, Vol.3, No.2, pp.250-255, Feb 2013.
D. Kim, B. Jung, H. Lee, D. Sung, and H. Yoon, “Optimal modulation and coding scheme selection in cellular networks with hybrid-ARQ error control,” IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 5195–5201, Dec. 2008, doi: 10.1109/t-wc.2008.070950.
J. Fan, Q. Yin, G. Y. Li, B. Peng, and X. Zhu, “MCS Selection for Throughput Improvement in Downlink LTE Systems,” 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), Jul. 2011, doi: 10.1109/icccn.2011.6005743.
N.-N. Dao, M. Park, J. Kim, and S. Cho, “Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform,” PLOS ONE, vol. 12, no. 8, p. e0182527, Aug. 2017, doi: 10.1371/journal.pone.0182527.
J.-E. Oh, A.-M. Jo, J. Joung, and E.-R. Jeong, “CNN-Based Transmit Antenna and MCS Selection in a Mobile Communication System Using Multiple Antennas,” The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1194–1201, Sep. 2023, doi: 10.7840/kics.2023.48.9.1194.
J. Wang, X. Li, J. Li, Q. Sun, and H. Wang, “NGCU: A New RNN Model for Time-Series Data Prediction,” Big Data Research, vol. 27, p. 100296, Feb. 2022, doi: 10.1016/j.bdr.2021.100296.
Z. Z., P. A. E. A., and H. M. H., “Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 870–878, Apr. 2021, doi: 10.11591/eei.v10i2.2036.
Chung J, Gulcehre C, Cho K and Bengio Y, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv preprint arXiv:1412.3555. 2014 Dec 11.
K. Naveen Durai, R. Subha, and A. Haldorai, “Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing,” Intelligent Automation & Soft Computing, vol. 34, no. 1, pp. 467–483, 2022, doi: 10.32604/iasc.2022.026020.
A. Shewalkar, D. Nyavanandi, and S. A. Ludwig, “Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU,” Journal of Artificial Intelligence and Soft Computing Research, vol. 9, no. 4, pp. 235–245, Aug. 2019, doi: 10.2478/jaiscr-2019-0006.
Acknowledgements
Author(s) thanks to Hanbat National University for research lab and equipment support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Eui-Rim Jeong
Eui-Rim Jeong
Department of Artificial Intelligence Software, Hanbat National University, Daejeon, Republic of Korea.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Seok-Jin Hong, Woong-Jong Yun and Eui-Rim Jeong, “GRU Based MCS Selection in Tactical Vehicle Communication”, Journal of Machine and Computing, pp. 594-602, July 2024. doi: 10.53759/7669/jmc202404057.
