Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network.
Saved in:
| Title: | Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network. |
|---|---|
| Authors: | Poonia, Renuka1 (AUTHOR) pooniarenuka77@gmail.com, Dalal, Priyanka1 (AUTHOR), Singh, Vijay Pal1 (AUTHOR) |
| Source: | International Journal of Communication Systems. 1/25/2025, Vol. 38 Issue 2, p1-15. 15p. |
| Subjects: | Radio frequency allocation, Power resources management, Radio networks, Resource allocation, Cognitive radio, Optimization algorithms, Data transmission systems |
| Abstract: | Device‐to‐device (D2D) transmission is essential for enhancing the functionality of fifth‐generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal‐to‐noise ratio–channel usability (SNR‐CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy‐induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state‐of‐the‐art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Device‐to‐device (D2D) transmission is essential for enhancing the functionality of fifth‐generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal‐to‐noise ratio–channel usability (SNR‐CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy‐induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state‐of‐the‐art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 10745351 |
| DOI: | 10.1002/dac.6078 |
