Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks
Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the S...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/13/3800 |
| _version_ | 1797563081537093632 |
|---|---|
| author | Xiang Xiao Fanzi Zeng Zhenzhen Hu Lei Jiao |
| author_facet | Xiang Xiao Fanzi Zeng Zhenzhen Hu Lei Jiao |
| author_sort | Xiang Xiao |
| collection | DOAJ |
| description | Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows. |
| first_indexed | 2024-03-10T18:37:31Z |
| format | Article |
| id | doaj.art-70604c6c99964649bc00f8a9b9fe1f60 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:37:31Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-70604c6c99964649bc00f8a9b9fe1f602023-11-20T06:04:51ZengMDPI AGSensors1424-82202020-07-012013380010.3390/s20133800Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio NetworksXiang Xiao0Fanzi Zeng1Zhenzhen Hu2Lei Jiao3College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaDepartment of Information and Communication Technology, University of Agder, 4630 Agder, NorwayCognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.https://www.mdpi.com/1424-8220/20/13/3800cognitive radio networksflow-adaptive spectrum leasingchannel aggregating |
| spellingShingle | Xiang Xiao Fanzi Zeng Zhenzhen Hu Lei Jiao Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks Sensors cognitive radio networks flow-adaptive spectrum leasing channel aggregating |
| title | Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks |
| title_full | Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks |
| title_fullStr | Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks |
| title_full_unstemmed | Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks |
| title_short | Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks |
| title_sort | dynamic flow adaptive spectrum leasing with channel aggregation in cognitive radio networks |
| topic | cognitive radio networks flow-adaptive spectrum leasing channel aggregating |
| url | https://www.mdpi.com/1424-8220/20/13/3800 |
| work_keys_str_mv | AT xiangxiao dynamicflowadaptivespectrumleasingwithchannelaggregationincognitiveradionetworks AT fanzizeng dynamicflowadaptivespectrumleasingwithchannelaggregationincognitiveradionetworks AT zhenzhenhu dynamicflowadaptivespectrumleasingwithchannelaggregationincognitiveradionetworks AT leijiao dynamicflowadaptivespectrumleasingwithchannelaggregationincognitiveradionetworks |