Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing
As the popularity of smartphones, wearable devices, intelligent vehicles, and countless other devices continues to rise, the surging demand for mobile data traffic has resulted in an increasingly crowded electromagnetic spectrum. Spectrum sharing serves as a solution to optimize the utilization of w...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4290 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144550262145024 |
|---|---|
| author | Yang Yu Xiaoqing Tang Guihui Xie |
| author_facet | Yang Yu Xiaoqing Tang Guihui Xie |
| author_sort | Yang Yu |
| collection | DOAJ |
| description | As the popularity of smartphones, wearable devices, intelligent vehicles, and countless other devices continues to rise, the surging demand for mobile data traffic has resulted in an increasingly crowded electromagnetic spectrum. Spectrum sharing serves as a solution to optimize the utilization of wireless communication channels, allowing various types of users to share the same frequency band securely. This paper investigates spectrum allocation and power control problems in overlay spectrum sharing, with a focus on promoting green communication. Maximizing weighted sum energy efficiency (WSEE) requires solving complex multiple-ratio fractional programming (FP) problems. In contrast, weighted sum power (WSP) minimization offers a more straightforward approach. Moreover, because WSP is directly related to users’ power consumption, we can dynamically adjust their weights to balance their residual energy. We prioritize WSP minimization over the more common WSEE maximization. This choice not only simplifies computation but also maintains users’ quality of service (QoS) requirements. The joint optimization for multiple primary users (PUs) and secondary users (SUs) can be decomposed into two components: a weighted bipartite matching problem and a series of convex resource allocation problems. Utilizing Newton’s method, our system-level simulation results show that the proposed scheme achieves optimal performance with minimal computational time. We explore strategies to accelerate the proposed scheme by refining the selection of initial values for Newton’s method. |
| format | Article |
| id | doaj-art-e3489cb7d9d04370bc2a14e6a4a750d2 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e3489cb7d9d04370bc2a14e6a4a750d22025-08-20T02:28:19ZengMDPI AGApplied Sciences2076-34172025-04-01158429010.3390/app15084290Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum SharingYang Yu0Xiaoqing Tang1Guihui Xie2Electronic Information School, Hubei Three Gorges Polytechnic, Yichang 443199, ChinaSchool of Artificial Intelligence, Hubei University, Wuhan 430062, ChinaSchool of Automation, China University of Geosciences (Wuhan), Wuhan 430074, ChinaAs the popularity of smartphones, wearable devices, intelligent vehicles, and countless other devices continues to rise, the surging demand for mobile data traffic has resulted in an increasingly crowded electromagnetic spectrum. Spectrum sharing serves as a solution to optimize the utilization of wireless communication channels, allowing various types of users to share the same frequency band securely. This paper investigates spectrum allocation and power control problems in overlay spectrum sharing, with a focus on promoting green communication. Maximizing weighted sum energy efficiency (WSEE) requires solving complex multiple-ratio fractional programming (FP) problems. In contrast, weighted sum power (WSP) minimization offers a more straightforward approach. Moreover, because WSP is directly related to users’ power consumption, we can dynamically adjust their weights to balance their residual energy. We prioritize WSP minimization over the more common WSEE maximization. This choice not only simplifies computation but also maintains users’ quality of service (QoS) requirements. The joint optimization for multiple primary users (PUs) and secondary users (SUs) can be decomposed into two components: a weighted bipartite matching problem and a series of convex resource allocation problems. Utilizing Newton’s method, our system-level simulation results show that the proposed scheme achieves optimal performance with minimal computational time. We explore strategies to accelerate the proposed scheme by refining the selection of initial values for Newton’s method.https://www.mdpi.com/2076-3417/15/8/4290green communicationNewton’s methodspectrum allocationspectrum sharingpower controlweighted sum power |
| spellingShingle | Yang Yu Xiaoqing Tang Guihui Xie Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing Applied Sciences green communication Newton’s method spectrum allocation spectrum sharing power control weighted sum power |
| title | Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing |
| title_full | Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing |
| title_fullStr | Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing |
| title_full_unstemmed | Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing |
| title_short | Spectrum Allocation and Power Control Based on Newton’s Method for Weighted Sum Power Minimization in Overlay Spectrum Sharing |
| title_sort | spectrum allocation and power control based on newton s method for weighted sum power minimization in overlay spectrum sharing |
| topic | green communication Newton’s method spectrum allocation spectrum sharing power control weighted sum power |
| url | https://www.mdpi.com/2076-3417/15/8/4290 |
| work_keys_str_mv | AT yangyu spectrumallocationandpowercontrolbasedonnewtonsmethodforweightedsumpowerminimizationinoverlayspectrumsharing AT xiaoqingtang spectrumallocationandpowercontrolbasedonnewtonsmethodforweightedsumpowerminimizationinoverlayspectrumsharing AT guihuixie spectrumallocationandpowercontrolbasedonnewtonsmethodforweightedsumpowerminimizationinoverlayspectrumsharing |