KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Complex Field Network Coding with MPSK Modulation for High Throughput in UAV Networks

  • Mingfei Zhao (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Rui Xue (College of Information and Communication Engineering, Harbin Engineering University)
  • Received : 2022.08.17
  • Accepted : 2024.07.16
  • Published : 2024.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Employing multiple drones as a swarm to complete missions can sharply improve the working efficiency and expand the scope of investigation. Remote UAV swarms utilize satellites as relays to forward investigation information. The increasing amount of data demands higher transmission rate and complex field network coding (CFNC) is deemed as an effective solution for data return. CFNC applied to UAV swarms enhances transmission efficiency by occupying only two time slots, which is less than other network coding schemes. However, conventional CFNC applied to UAVs is combined with constant coding and modulation scheme and results in a waste of spectrum resource when the channel conditions are better. In order to avoid the waste of power resources of the relay satellite and further improve spectral efficiency, a CFNC transmission scheme with MPSK modulation is proposed in this paper. For the proposed scheme, the satellite relay no longer directly forwards information, but transmits information after processing according to the current channel state. The proposed transmission scheme not only maintains throughput advantage of CFNC, but also enhances spectral efficiency, which obtains higher throughput performance. The symbol error probability (SEP) and throughput results corroborated by Monte Carlo simulation show that the proposed transmission scheme improves spectral efficiency in multiples compared to the conventional CFNC schemes. In addition, the proposed transmission scheme enhances the throughput performance for different topology structures while keeping SEP below a certain value.

Keywords

Acknowledgement

This paper was supported in part by the National Natural Science Foundation of China (No. 61873070), the Heilongjiang Provincial Natural Science Foundation of China (No. LH2020F018), the Fundamental Research Funds for the Central Universities (No. 3072022QBZ0803).

References

  1. A. Sharma, P. Vanjani, N. Paliwal et al., "Communication and networking technologies for UAVs: A survey," Journal of Network and Computer Applications, vol.168, Oct. 2020.
  2. H. Huang, A. V. Savkin and W. Ni, "Online UAV Trajectory Planning for Covert Video Surveillance of Mobile Targets," IEEE Transactions on Automation Science and Engineering, vol.19, no.2, pp.735-746, Apr. 2022.
  3. Y. Zhou, B. Rao and W. Wang, "UAV Swarm Intelligence: Recent Advances and Future Trends," IEEE Access, vol.8, pp.183856-183878, Oct. 2020.
  4. Z. Mou, Y. Zhang, F. Gao et al., "Deep Reinforcement Learning Based Three-Dimensional Area Coverage With UAV Swarm," IEEE Journal on Selected Areas in Communications, vol.39, no.10, pp.3160-3176, Oct. 2021.
  5. J. Hu, H. Wu, R. Zhan et al., "Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior," Journal of Systems Engineering and Electronics, vol.32, no.6, pp.1463-1476, Dec. 2021.
  6. Y. Lin, Y. Tu and Z. Dou, "An Improved Neural Network Pruning Technology for Automatic Modulation Classification in Edge Devices," IEEE Transactions on Vehicular Technology, vol.69, no.5, pp.5703-5706, May 2020.
  7. R. Xue, M. Zhao, H. Tang, "Information Transmission Schemes Based on Adaptive Coded Modulation for UAV Surveillance Systems With Satellite Relays," IEEE Access, vol.8, pp.191355-191364, Sep. 2020.
  8. H. Touati, A. Chriki, H. Snoussi et al., "Cognitive Radio and Dynamic TDMA for efficient UAVs swarm communications," Computer Networks, vol.196, Sep. 2021.
  9. R. Xue, L. Han and H. Chai, "Complex field network coding for multi-source multi-relay single-destination UAV cooperative surveillance networks," Sensors, vol.20, no.6, Mar. 2020.
  10. A. Andrawes, R. Nordin, and M. Ismail, "Survey on Techniques and Applications of Cooperative Diversity with Adaptive Modulation in Wireless Networks," in Proc. of the 10th Jordan International Electrical and Electronics Engineering Conference, 2017.
  11. T. Wang and G. B. Giannakis, "Complex Field Network Coding for Multiuser Cooperative Communications," IEEE Journal on Selected Areas in Communications, vol.26, no.3, pp.561-571, Apr. 2008.
  12. T. Zhu, C. Li, Y. Tang et al., "On latency reductions in vehicle-to-vehicle networks by random linear network coding," China Communications, vol.18, no.6, pp.24-38, Jun. 2021.
  13. T. Ferrett and M. C. Valenti, "Noncoherent LDPC-Coded Physical-Layer Network Coding Using Multitone FSK," IEEE Transactions on Communications, vol.66, no.6, pp.2384-2395, Jun. 2018.
  14. J. Li, J. Yuan, R. Malaney et al., "Full-Diversity Binary Frame-Wise Network Coding for Multiple-Source Multiple-Relay Networks Over Slow-Fading Channels," IEEE Transactions on Vehicular Technology, vol.61, no.3, pp.1346-1360, Mar. 2012.
  15. M. Wang, Y. Lin, Q. Tian and G. Si, "Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges," IEEE Transactions on Reliability, vol.70, no,2, pp.790-807, Jun. 2021.
  16. K. Eritmen and M. Keskinoz, "Improving the Performance of Wireless Sensor Networks Through Optimized Complex Field Network Coding," IEEE Sensors Journal, vol.15, no.5, pp.2934-2946, May. 2015.
  17. H. Pan, F. Zheng, and M. Fitch, "Wireless Backhaul Networks With Precoding Complex Field Network Coding," IEEE Communications Letters, vol.19, no.3, pp.447-450, Mar. 2015.
  18. K. Eritmen and M. Keskinoz, "Symbol-error rate optimized complex field network coding for wireless communications," Wireless Networks, vol.21, no.8, pp.2467-2481, Mar. 2015.
  19. Y. Dong, X. Jiang, H. Zhou, Y. Lin and Q. Shi, "SR2CNN: Zero-Shot Learning for Signal Recognition," IEEE Transactions on Signal Processing, vol.69, pp.2316-2329, Mar. 2021.
  20. M. Biscarini, K. De Sanctis, S. Di Fabio et al., "Dynamical Link Budget in Satellite Communications at Ka-Band: Testing Radiometeorological Forecasts With Hayabusa2 Deep-Space Mission Support Data," IEEE Transactions on Wireless Communications, vol.21, no.6, pp.3935-3950, Jun. 2022.
  21. V. S. Kumar and D. G. Kurup, "A New Broadband Magic Tee Design for Ka-Band Satellite Communications," IEEE Microwave and Wireless Components Letters, vol.29, no.2, pp.92-94, Feb. 2019.
  22. P. M. Kalaivaanan, A. Sali, R. S. A. R. Abdullah et al., "Evaluation of Ka-Band Rain Attenuation for Satellite Communication in Tropical Regions Through a Measurement of Multiple Antenna Sizes," IEEE Access, vol.8, pp.18007-18018, Jan. 2020.
  23. C. Hou, G. Liu, Q. Tian, Z. Zhou, L. Hua, and Y. Lin, "Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain," IEEE Internet of Things Journal, vol.9, no.19, pp.19438-19449, Oct. 2022.
  24. R. Xue, Y. Cao, and T. Wang, "Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications," Information, vol.8, no.3, Jun. 2017.