Analyzing Adaptive Beamforming Techniques for Reliable Navigation in Maritime Cognitive Radio Networks
DOI:
https://doi.org/10.31838/NJAP/08.01.07Keywords:
Adaptive Beamforming, Cognitive Radio, Maritime Communication, Navigation Reliability, Spectrum Management, Interference Mitigation, Wireless NetworksAbstract
Navigation and communication for operational safety and efficiency are vital functions within the fast-evolving and spectrum-limited maritime domains. CRNs, or cognitive radio networks, offer intelligent solutions for telecommunications and enable dynamic spectrum access by allowing sophisticated spectrum sensing. However, advanced maritime spectrum channels are subject to rapid changes and interference from coexisting systems which requires overcoming severe resilience issues. The purpose of this study is to evaluate the effectiveness of adaptive beamforming strategies for quality and reliability of signal in maritime cognitive radio networks. Specifically, we estimate the performance of the beamforming approaches—LMS, SMI, MVDR, and Minimum Variance Distortionless Response—given realistic mobility of marine vessels and channel fading conditions. Simulations show that with adaptive beamforming, a focus on direction of the received signals while minimizing interference leads to better SINR, spectral efficiency, and navigation precision. The results demonstrate the need for integration of adaptive beamforming into a maritime navigation system based on cognitive radio networks to secure reliable and efficient communication in maritime environments
References
1. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220. https://doi.org/10.1109/JSAC.2004.839380
2. Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Doctoral Dissertation, KTH Royal Institute of Technology.
3. Zain, Z. (2025). Exploring the field of mechatronics: Scope and future. Innovative Reviews in Engineering and Science, 2(1), 45–51. https://doi.org/10.31838/INES/02.01.05
4. Li, H., Zhu, H., Wang, L., & Zhang, Y. (2016). Maritime communications: A comprehensive survey. IEEE Access, 6, 6806–6830. https://doi.org/10.1109/ACCESS.2016.2633276
5. Van Veen, B.D., & Buckley, K.M. (1988). Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 5(2), 4–24. https://doi.org/10.1109/53.665
6. Godara, L.C. (1997). Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations. Proceedings of the IEEE, 85(8), 1195–1245. https://doi.org/10.1109/5.622504
7. Trees, H. L. V. (2002). Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons.
8. Kavitha, S., & Kaniezhil, R. (2023). Enhanced wireless network usage and loading on network delay using cognitive radio network. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(4), 10–22. https://doi.org/10.58346/JOWUA.2023.I4.002
9. Wang, Y., Chen, S., & Liu, Y. (2020). Adaptive beamforming in maritime environments with low SNR: A practical perspective. IEEE Systems Journal, 14(4), 5050–5058. https://doi.org/10.1109/JSYST.2020.2979232
10. Kavitha, M. (2024). Embedded system architectures for autonomous vehicle navigation and control. SCCTS Journal of Embedded Systems Design and Applications, 1(1), 31–36. https://doi.org/10.31838/ESA/01.01.06
11. Zhang, Y., & Xu, X. (2021). Performance evaluation of adaptive beamforming techniques for maritime 5G networks. International Journal of Antennas and Propagation, 2021, 1–9. https://doi.org/10.1155/2021/9971325
12. Nayak, A. (2024). A wireless sensor network-based framework for structural health monitoring: real-time damage detection and energy-efficient communication. Journal of Wireless Intelligence and Spectrum Engineering, 1(1), 1–9.
13. Liu, C., & Zhao, X. (2021). Adaptive antenna array design for maritime CRNs: Enhancing resilience to sea-state variations. IEEE Access, 9, 44332–44340. https://doi.org/10.1109/ACCESS.2021.3066843
14. Beken, K., Caddwine, H., Kech, R., & Mlein, M. (2023). Electromagnetic sounding in antennas using near-field measurement techniques. National Journal of Antennas and Propagation, 5(2), 29–35.
15. Balanis, C. A. (2016). Antenna theory: Analysis and design (4th ed.). John Wiley & Sons.
16. Cheng, X., Li, T., & Han, B. (2022). Reinforcement learning-based beam selection for maritime wireless communication. IEEE Transactions on Vehicular Technology, 71(2), 1721–1732. https://doi.org/10.1109/TVT.2021.3136329
17. Huang, R., Zhang, J., & Wei, L. (2020). MVDR-based beamforming for robust maritime communication in multipath environments. IEEE Communications Letters, 24(9), 1957–1960. https://doi.org/10.1109/LCOMM.2020.3003392
18. Prakash, J., & Meena, K. (2022). Simulation of selective radiation anti-reflection coating for improvement of emissivity. International Academic Journal of Innovative Research, 9(1), 42–51. https://doi.org/10.9756/IAJIR/V9I1/IAJIR0907
19. Saranya, U., & Madhubala, P. (2019). Enhancement of security and network lifetime using flexi-cast method. International Journal of Communication and Computer Technologies, 7(1), 23–26.
20. Akin, O., Gulmez, U.C., Sazak, O., Yagmur, O. U., & Angin, P. (2022). GreenSlice: An energy-efficient secure network slicing framework. Journal of Internet Services and Information Security, 12(1), 57–71. https://doi.org/10.22667/JISIS.2022.02.28.057
21. Fischer, S., & Martin, H. (2017). Limitations of AIS-based maritime navigation and risk management. Marine Policy, 83, 67–74. https://doi.org/10.1016/j.marpol.2017.06.019
22. Sathwik, B., Sudha, D., Devi, Y. A. S., & Bodapati, J.(2024). RoBA multiplier-driven FIR filter synthesis: uniting efficiency and speed for enhanced digital signal processing. Journal of VLSI Circuits and Systems, 6(2), 23–30. https://doi.org/10.31838/jvcs/06.02.03
23. Zhao, H., Sun, X., & Lu, C. (2019). Channel modeling and performance evaluation in maritime wireless communications. IEEE Access, 7, 102551–102563. https://doi.org/10.1109/ACCESS.2019.2930594
24. Zhou, X., Zhang, R., & Tse, D.N. (2019). Beamforming and interference cancellation for maritime cognitive radio networks. IEEE Transactions on Wireless Communications, 18(12), 5889–5901. https://doi.org/10.1109/TWC.2019.2938221
25. Qian, W., Sun, S., & Ai, B. (2020). Deep learning-based adaptive beamforming in time-varying CRNs. IEEE Transactions on Wireless Communications, 19(10), 6428–6442. https://doi.org/10.1109/TWC.2020.3006665
26. Yan, L., Chen, X., & Zhang, M. (2018). Maritime wireless communication: Channel modeling and performance analysis. IEEE Transactions on Vehicular Technology, 67(9), 8454–8466. https://doi.org/10.1109/TVT.2018.2845620
27. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130. https://doi.org/10.1109/SURV.2009.090109
28. Zhang, Q., & Li, X. (2019). Performance analysis of beamforming techniques in cognitive radio networks. International Journal of Wireless and Mobile Computing, 16(3), 225–234. https://doi.org/10.1504/IJWMC.2019.10025432
29. Xu, P., Huang, D., & Han, Z. (2022). Intelligent beamforming for maritime IoT networks using reinforcement learning. IEEE Internet of Things Journal, 9(5), 3921–3932. https://doi.org/10.1109/JIOT.2021.3110386
30. Al-Dulaimi, A., Al-Rubaye, S., Ni, Q., & So, D.K.C. (2018). 5G communications for maritime applications: An overview. IEEE Access, 6, 68070–68088. https://doi.org/10.1109/ACCESS.2018.2878528
31. Choi, Y., Park, J., & Kim, H. (2015). Adaptive beamforming for CRNs with interference avoidance. Wireless Personal Communications, 82(1), 337–352. https://doi.org/10.1007/s11277-014-2061-4
32. Shah, N., Gupta, R., & Lee, J. (2021). Hybrid maritime communication frameworks using satellite, cognitive radio, and adaptive beamforming. Journal of Marine Science and Engineering, 9(4), 465. https://doi.org/10.3390/jmse9040465
33. Vij, P., & Prashant, P.M. (2024). Predicting aquatic ecosystem health using machine learning algorithms. International Journal of Aquatic Research and Environmental Studies,4(S1), 39–44. https://doi.org/10.70102/IJARES/V4S1/7
34. Elbir, A.M., Mishra, A.K., & Chatzinotas, S. (2020). Deep learning for hybrid beamforming in millimeter-wave massive MIMO systems. IEEE Transactions on Vehicular Technology, 69(1), 552–563. https://doi.org/10.1109/TVT.2019.2953185
