分簇认知物联网联合资源分配算法

刘鑫

doi:10.11959/j.issn.2096-3750.2019.00086

您当前的位置：

首页 >

文章列表页 >

分簇认知物联网联合资源分配算法

理论与技术 | 更新时间：2024-06-05

- 分簇认知物联网联合资源分配算法
- Joint resource allocation algorithm for clustering cognitive Internet of things
- 物联网学报 2019年3卷第1期页码：14-19
- 作者机构：
- 作者简介：
  
  [ "刘鑫（1984- ），男，黑龙江哈尔滨人，博士，大连理工大学副教授、硕士生导师，主要研究方向为认知无线电、传感器网络和物联网等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundations of China(61601221);国家自然科学基金委员会—中国民用航空局民航联合研究基金资助项目(U1833102)
- DOI：10.11959/j.issn.2096-3750.2019.00086
  中图分类号： TN915.6
- 纸质出版日期：2019-03-30，
  
  网络出版日期：2019-03，
- 稿件说明：
移动端阅览
刘鑫. 分簇认知物联网联合资源分配算法[J]. 物联网学报, 2019,3(1):14-19.

XIN LIU. Joint resource allocation algorithm for clustering cognitive Internet of things. [J]. Chinese journal on internet of things, 2019, 3(1): 14-19.
刘鑫. 分簇认知物联网联合资源分配算法[J]. 物联网学报, 2019,3(1):14-19. DOI： 10.11959/j.issn.2096-3750.2019.00086.

XIN LIU. Joint resource allocation algorithm for clustering cognitive Internet of things. [J]. Chinese journal on internet of things, 2019, 3(1): 14-19. DOI： 10.11959/j.issn.2096-3750.2019.00086.

摘要

认知物联网通过频谱感知和共享有效提高了频谱利用率，但频谱感知会产生开销。提出分簇认知物联网降低感知开销，通过联合资源分配提高物联网的传输速率。首先提出分簇认知物联网的网络模型和帧结构，推导物联网的平均传输速率和干扰功率；然后建立优化模型，通过联合优化感知时间、感知簇头和传输功率使传输速率最大化，并给出联合优化算法求解；最后提出认知物联网的节点分簇和簇头选择算法。仿真结果表明，所提方案通过联合资源分配能够获得最大传输速率，且与传统方案相比，传输性能有所提高。

Abstract

Cognitive Internet of things (CIoT) can improve spectrum utilization effectively through spectrum sensing and sharing

but spectrum sensing may generate overhead.Clustering CIoT was proposed to decrease sensing overhead

whose transmission rate could be improved through joint resource allocation.The network model and frame structure of the CIoT were proposed firstly

and the average transmission rate and interference power of the CIoT were deduced.Then

an optimization model was built to maximize the transmission rate through jointly optimizing the sensing time

sensing cluster head and transmission power

and a joint optimization algorithm was given to solve the optimization problem.Finally

the node clustering and cluster head selection algorithm of the CIoT was presented.The simulations show that the maximal transmission rate through joint resource allocation can be achieved and the transmission performance compared with the traditional schemes can be improved by the proposed scheme.

关键词

认知物联网分簇资源分配协作频谱感知传输速率

Keywords

cognitive Internet of thingsclusteringresource allocationcooperative spectrum sensingtransmission rate

references

MITOLA J . Cognitive radio for flexible mobile multimedia communications[J]. Mobile Networks and Applications, 2011,6(5): 435-441.

HU F, CHEN B, ZHU K . Full spectrum sharing in cognitive radio networks toward 5G:a survey[J]. IEEE Access, 2018(6): 15754-15776.

LIU X, JIA M, NA Z . Multi-modal cooperative spectrum sensing based on dempster-shafer fusion in 5G-based cognitive radio[J]. IEEE Access, 2018(6): 199-208.

GHASEMI A, SOUSA E S . Spectrum sensing in cognitive radio networks:requirements,challenges and design trade-offs[J]. IEEE Communications Magazine, 2008,46(4): 32-39.

LIU X, HE D, JIA M . 5G-based wideband cognitive radio system design with cooperative spectrum sensing[J]. Physical Communication, 2017(25): 539-545.

JIA M, LIU X, GU X ,et al. Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio[J]. International Journal of Satellite Communications and Networking, 2017,35(2): 139-150.

LIU L, LIU F, YANG L ,et al. A preload cooperative sensing scheme with low overhead in cognitive radio networks[J]. Wireless Communications and Mobile Computing, 2016,16(9): 1148-1157.

刘鑫, 仲伟志, 井庆丰 . 认知无线电多时隙联合频谱感知方法及优化[J]. 电子学报, 2015,43(5): 895-900.

LIU X, ZHONG W Z, JING Q F . Multi-slot cooperative spectrum sensing method with optimization in cognitive radio[J]. Acta Electronica Sinica, 2015,43(5): 895-900.

孙玉 . 我国物联网产业发展趋势[J]. 物联网学报, 2017,1(3): 1-5.

SUN Y . Development trend of IoT industry in China[J]. Chinese Journal on Internet of Things, 2017,1(3): 1-5.

邬贺铨 . 物联网技术与应用的新进展[J]. 物联网学报, 2017,1(1): 1-6.

WU H Q . Technology and application progress of Internet of things[J]. Chinese Journal on Internet of Things, 2017,1(1): 1-6.

CHEN X, XING L, QIU T ,et al. An auction-based spectrum leasing mechanism for mobile macro-femtocell networks of IoT[J]. Sensors, 2017,17(2): 1-22.

WU Q, DING G, XU Y ,et al. Cognitive Internet of things:a new paradigm beyond connection[J]. IEEE Internet of Things Journal, 2014,1(2): 129-143.

ZHANG X, MA Y, QI H ,et al. Distributed compressive sensing augmented wideband spectrum sharing for cognitive IoT[J]. IEEE Internet of Things Journal, 2018,5(4): 3234-3245.

EJAZ W, IBNKAHLA M . Multiband spectrum sensing and resource allocation for IoT in cognitive 5G networks[J]. IEEE Internet of Things Journal, 2018,5(1): 150-163.

HAN R, GAO Y, WU C ,et al. An effective multi-objective optimization algorithm for spectrum allocations in the cognitive-radio-based Internet of things[J]. IEEE Access, 2018(6): 12858-12867.

LIANG Y, ZENG Y, PEH E C Y ,et al. Sensing-through put trade off for cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2008,7(4): 1326-1337.

LIU X, Bi G, GUAN Y L ,et al. Joint optimisation algorithm of cooperative spectrum sensing with cooperative overhead and sub-band transmission power for wideband cognitive radio network[J]. Transactions on Emerging Telecommunications Technologies, 2015,26(4): 586-597.

TIAN R, MA L, WANG Z ,et al. Cognitive interference alignment schemes for IoT oriented heterogeneous two-tier networks[J]. Sensors, 2018,18(8): 1-18.

LIU X, MIN J, TAN X . Threshold optimization of cooperative spectrum sensing in cognitive radio network[J]. Radio Science, 2013,48(1): 23-32.

LIU X, LI F, NA Z . Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio[J]. IEEE Access, 2017(5): 3801-3812.

浏览量

807

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向无人机集群的感知与控制闭环调度策略

CR-RSMA网络接入控制策略研究

移动边缘计算中基于资源联合分配的部分计算卸载方法

融合射频能量采集的协同节能计算迁移研究

基于增强加权质心定位的认知物联网用户频谱接入控制