Enhancing Adaptive Spectrum Access: An Intelligent Reflecting Surface Assisted CRN for Future Wireless Communication
Effective spectrum management remains a critical challenge in modern wireless communication due to the growing demand for high-speed and reliable connectivity. Cognitive radio (CR) has emerged as a transformative solution, enabling more efficient spectrum utilization through adaptive spectrum access...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11087197/ |
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| Summary: | Effective spectrum management remains a critical challenge in modern wireless communication due to the growing demand for high-speed and reliable connectivity. Cognitive radio (CR) has emerged as a transformative solution, enabling more efficient spectrum utilization through adaptive spectrum access mode (ASAM). In ASAM, the secondary transmitter (ST) selects either underlay or overlay transmission based on the sensing methodology. In the underlay mode, ST operates alongside the primary transmitter (PT) at low power levels to prevent interference with the primary receiver (PR), ensuring that transmission remains within the interference threshold. However, the dynamic and unpredictable nature of wireless environment makes it challenging to maintain transmit power within this limit. To address this, we propose an intelligent reflecting surface (IRS)-assisted enhanced ASAM (EASAM) CR network (CRN). Additionally, we optimize the IRS phase shifts and ST’s transmit power using the Grey Wolf Optimization (GWO) algorithm. This approach dynamically adjusts the ST’s transmit power within interference constraints while fine-tuning IRS phase shifts to enhance spectral efficiency. Extensive simulations demonstrate that the proposed EASAM with optimized IRS significantly outperforms the EASAM without IRS, achieving nearly a four times higher throughput. Furthermore, IRS optimization using the GWO yields a 7.1% increase in throughput compared to other meta-heuristic-based IRS optimization methods, and a 47.9% improvement over the unoptimized IRS. These findings highlight the potential of integrating GWO-based IRS approach in CRN to enhance performance of adaptive spectrum access. |
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| ISSN: | 2169-3536 |