Optimized Dynamic Spectrum Sharing for Cognitive Radio with Full-Duplex Primary Users, Enhancing 5G Networks' Spectral Efficiency by Mitigating Self-Interference

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Manisha Rajput, P. Malathi

10.14445/23488549/IJECE-V12I9P120

How to Cite?

Manisha Rajput, P. Malathi, "Optimized Dynamic Spectrum Sharing for Cognitive Radio with Full-Duplex Primary Users, Enhancing 5G Networks' Spectral Efficiency by Mitigating Self-Interference," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 9, pp. 235-252, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I9P120

Abstract:

In this research, a customized and effective Dynamic Spectrum Sharing (DSS) approach is proposed for use in Cognitive Radio (CR) systems to handle challenges brought by In-Band Full-Duplex (IBFD) Primary User (PU) networks in 5G environments. Since more data is being used, the existing wireless networks are under more stress, so using the spectrum efficiently is very important. Most traditional methods are unable to handle changing conditions and interference situations, especially with IBFD systems, because of their interference issues. This new framework reduces self-interference, enhancing spectrum access opportunities for both PUs and SUs. With this approach, the parameters for sharing the radio spectrum are adjusted in real time by the control function, which improves decisions and system reliability. The method designs its system so that primary users apply proper Gaussian signalling, whereas secondary users make use of improper Gaussian signals to send communications. This design prevents disruption between PUs and SUs, and the bandwidth is used well with little or no interference. Because cognitive radios have to be careful not to disturb the PUs, the right signalling approach provides more protection against interference, making the system both stronger and more efficient in terms of spectrum usage. A new game model is introduced, and it relies on enthalpy-based sigmoid functions to dynamically change and monitor the DSS parameters automatically. You can use these algorithms in real-time, and they need less computing power and cost less, which is needed in 5G networks. Also, as a new development, an Improved Multi-Objective Grasshopper optimisation algorithm (I-MOGOA) is provided, which surpasses the Genetic Algorithm (GA) and Simplified Particle Genetic Algorithm (SPGA) in delivering enhanced spectral efficiency and energy conservation. It is notable that I-MOGOA manages to keep several performance metrics under control, such as Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), throughput, transmission power and Signal-To-Self-Interference-Plus-Noise Ratio (SSINR). Analytics have proven that this method substantially impacts all of the performance indicators. Both the system flowchart and simulation studies indicate that higher SUs can lead to a drop in spectral efficiency caused by more interference and fewer resources, highlighting the essential balance in the allocated spectrum. Because of the sigmoid-based control, the system continues to adjust resource access in real time, leading to fair and stable sharing between the primary and secondary users. It has been found in this research that the SNR decreases while the spectral efficiency increases in 5G cognitive radio systems. While a high SNR gives better signal quality, it can make the network use more energy and simultaneously reduce the number of bits sent through the same channel. Therefore, careful handling of this tradeoff is needed. The suggested method can be used to find the right SNR level to achieve the best tradeoff between using less power and getting higher throughput. The study shows that bad SNR management causes higher BER, lower throughput, and unnecessary energy use. The new system is superior to regular IBFD and interference reduction methods through experiments that achieve strong BER, reliable signals, and safe transmission. It proves that grouping, advanced signal modelling, optimization algorithms, and real-time interference management improve performance. The fact that the new DSS framework is more effective than traditional methods means it helps 5G networks today and sets the stage for progress in cognitive radio technology later. The research introduces a DSS model that can adjust and grow, setting a good start for the development of wireless systems. It fixes major shortcomings in the current CR by using good methods to share the radio spectrum, not getting stuck in densely used networks or persistent interference. In brief, the suggested enthalpy-based sigmoid DSS method ensures high performance, energy saving and resistance to interference in 5G-based cognitive radio systems. Integrating dynamic control, signal optimization, and evolutionary algorithms plays a key role in this research, which delivers an important solution for next-generation wireless communications. It improves the efficiency and dependability of the system and prepares for the use of new wireless services in the future.

Keywords:

Band Full Duplex, Cognitive Radio, Gaussian Signalling, Spectrum Sharing, 5G Cognitive Radio.

References:

[1] Siyu Lin et al., “Advanced Dynamic Channel Access Strategy in Spectrum Sharing 5G Systems,” IEEE Wireless Communications, vol. 24, no. 5, pp. 74-80, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Masaki Kitsunezuka et al., “Learning-Based, Distributed Spectrum Observation System for Dynamic Spectrum Sharing in the 5G Era and Beyond,” IEICE Transactions on Communications, vol. E102.B, no. 8, pp. 1526-1533, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] W.S.H.M.W. Ahmad et al., “5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks,” IEEE Access, vol. 8, pp. 14460-14488, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Maziar Nekovee, “Opportunities and Enabling Technologies for 5G and Beyond-5G Spectrum Sharing,” Handbook of Cognitive Radio, pp. 1-15, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jeongho Jeon et al., “Coordinated Dynamic Spectrum Sharing for 5G and Beyond Cellular Networks,” IEEE Access, vol. 7, pp. 111592-111604, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Rajbir Kaur, Avtar Singh Buttar, and Jayant Anand, “Spectrum Sharing Schemes in Cognitive Radio Network: A Survey,” 2018 Second International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, pp. 1279-1284, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wei Liang, Soon Xin Ng, and Lajos Hanzo, “Cooperative Overlay Spectrum Access in Cognitive Radio Networks,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1924-1944, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] A. Sharmila, and P. Dananjayan, “Spectrum Sharing Techniques in Cognitive Radio Networks – A Survey,” 2019 IEEE International Conference on System, Computation, Automation and Networking, Pondicherry, India, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Wensheng Zhang et al., “Enhanced 5G Cognitive Radio Networks Based on Spectrum Sharing and Spectrum Aggregation,” IEEE Transactions on Communications, vol. 66, no. 12, pp. 6304-6316, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Muhammad Amjad et al., “Full-Duplex Communication in Cognitive Radio Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2158-2191, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Lin Zhang et al., “A Survey of Advanced Techniques for Spectrum Sharing in 5G Networks,” IEEE Wireless Communications, vol. 24, no. 5, pp. 44-51, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Shree Krishna Sharma et al., “Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges,” IEEE Communications Surveys & Tutorials, vol. 20, no. 1, pp. 674-707, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Soo-Min Kim et al., “Opportunism in Spectrum Sharing for Beyond 5G With Sub-6 GHz: A Concept and Its Application to Duplexing,” IEEE Access, vol. 8, pp. 148877-148891, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Dongming Li, Julian Cheng, and Victor C.M. Leung, “Adaptive Spectrum Sharing for Half-Duplex and Full-Duplex Cognitive Radios: From the Energy Efficiency Perspective,” IEEE Transactions on Communications, vol. 66, no. 11, pp. 5067-5080, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ramez Askar et al., “Interference Handling Challenges toward Full Duplex Evolution in 5G and Beyond Cellular Networks,” IEEE Wireless Communications, vol. 28, no. 1, pp. 51-59, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ciprian Zamfirescu et al., “Network Slice Allocation for 5G V2X Networks: A Case Study from Framework to Implementation and Performance Assessment,” Vehicular Communications, vol. 45, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Haolin Li et al., “Self-Interference Cancellation Enabling High-Throughput Short-Reach Wireless Full-Duplex Communication,” IEEE Transactions on Wireless Communications, vol. 17, no. 10, pp. 6475-6486, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jingyi J. Sun, Matthew P. Chang, and Paul R. Prucnal, “Demonstration of Over-the-Air RF Self-Interference Cancellation Using an Optical System,” IEEE Photonics Technology Letters, vol. 29, no. 4, pp. 397-400, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Kenneth E. Kolodziej, Bradley T. Perry, and Jeffrey S. Herd, “In-Band Full-Duplex Technology: Techniques and Systems Survey,” IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 7, pp. 3025-3041, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Kazuki Komatsu, Yuichi Miyaji, and Hideyuki Uehara, “Iterative Nonlinear Self-Interference Cancellation for In-Band Full-Duplex Wireless Communications under Mixer Imbalance and Amplifier Nonlinearity,” IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. 4424-4438, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Bojiang Ma, Hamed Shah-Mansouri, and Vincent W.S. Wong, “Full-Duplex Relaying for D2D Communication in Millimeter Wave-Based 5G Networks,” IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4417-4431, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Mohammad Amzad Hossain, Michael Schukat, and Enda Barrett, “Enhancing the Spectrum Utilization in Cellular Mobile Networks by Using Cognitive Radio Technology,” 2019 30th Irish Signals and Systems Conference, Maynooth, Ireland, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Xin Liu et al., “A Novel Multichannel Internet of Things Based on Dynamic Spectrum Sharing in 5G Communication,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 5962-5970, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Osama Al-Saadeh, and Ki Won Sung, “A Performance Comparison of In-Band Full Duplex and Dynamic TDD for 5G Indoor Wireless Networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2017, pp. 1-14, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ahmed Alshaflut, and Vijey Thayananthan, “Traffic Predicting Model for Dynamic Spectrum Sharing Over 5G Networks,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 6, pp. 369-374, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Mohamed Gaafar et al., “Underlay Spectrum Sharing Techniques With In-Band Full-Duplex Systems Using Improper Gaussian Signaling,” IEEE Transactions on Wireless Communications, vol. 16, no. 1, pp. 235-249, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Harri Holma, and Antti Toskala, WCDMA for UMTS: HSPA Evolution and LTE, Wiley, pp. 1-640, 2010.
[Google Scholar] [Publisher Link]
[28] Seyedeh Zahra Mirjalili et al., “Grasshopper Optimization Algorithm for Multi-Objective Optimization Problems,” Applied Intelligence, vol. 48, pp. 805-820, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Shahrzad Saremi, Seyedali Mirjalili, and Andrew Lewis, “Grasshopper Optimisation Algorithm: Theory and Application,” Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Alexios Balatsoukas-Stimming, “Joint Detection and Self-Interference Cancellation in Full-Duplex Systems Using Machine Learning,” 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, pp. 989-992, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Kenneth E. Kolodziej, Aidan U. Cookson, and Bradley T. Perry, “RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems,” IEEE Open Journal of the Communications Society, vol. 2, pp. 1158-1170, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Syama Sasikumar, and J. Jayakumari, “A Novel Method for the Optimization of Spectral -Energy Efficiency Tradeoff in 5G Heterogeneous Cognitive Radio Network,” Computer Networks, vol. 180, 2020.
[CrossRef] [Google Scholar] [Publisher Link]