基于协作频谱感知和干扰约束的认知异构网络

叶仕通; 万智萍; 柯建波; 刘少江; 倪伟传

doi:10.3969/j.issn.1000-5641.2017.06.007

基于协作频谱感知和干扰约束的认知异构网络

doi: 10.3969/j.issn.1000-5641.2017.06.007

1.
广东工业大学华立学院, 广州 511325
2.
中山大学新华学院, 广州 510520

基金项目:

广东省青年创新人才自然科学类基金 2014KQNCX251

广东省青年创新人才自然科学类基金 2014KQNCX253

详细信息

作者简介:
叶仕通, 男, 硕士, 讲师, 研究方向为视频信号处理、计算机视觉、模式识别、图形图像处理和数据挖掘.Email:yst888_0@126.com

中图分类号: TP393
计量
- 文章访问数: 271
- HTML全文浏览量: 52
- PDF下载量: 267
- 被引次数: 0
出版历程
- 收稿日期: 2016-08-19
- 刊出日期: 2017-11-25

Cognitive heterogeneous network based on cooperative spectrum sensing and interference constraints

1.
Huali College, Guangdong University of Technology, Guangzhou 511325, China
2.
Xinhua College, Sun Yat-Sen University, Guangzhou 510520, China

摘要

摘要: 针对异构无线网络的空闲信道检测准确率及网络吞吐量的优化问题，提出一种基于协作频谱感知和干扰约束的认知异构网络.首先，所提出的认知异构网络系统模型采用多个中心次用户（Center SecondaryUsers，CSU）节点协助其他节点进行频谱感知，并引入了能量检测阈值，在提高空闲信道检测准确率的同时节省检测能耗.接着，采用最大化数据速率的联合优化方程，在干扰功率的限制约束下为节点分配最佳的发射功率，降低干扰程度并优化网络吞吐量.实验仿真结果表明，相比较基于集群的协作频谱感知分配策略算法和基于QoS约束的能量感知竞争功率分配算法，该算法的网络吞吐量分别提升了3.4%和1.5%，平均频谱利用率分别提高了9.3%和7.4%.
- 认知异构网络 /
- 协作频谱感知 /
- 干扰约束 /
- 功率分配 /
- 网络吞吐量优化
Abstract: For idle channel detection accuracy and network throughput optimized on heterogeneous wireless networks, a cognitive heterogeneous network based on cooperative spectrum sensing and interference constraints is proposed. First, the proposed model of cognitive heterogeneous network system that used several center secondary user nodes to assist other nodes spectrum sensing, and the introduction of energy detection threshold improves idle channel detection accuracy of detection while saves energy. Then, the joint optimization equation of maximizing data rate was used to assignment optimal transmit power for node under constrained to limit interference power, it reduced the degree of interference and optimize network throughput. The simulation results show that, compared with the cluster-based cooperative spectrum sensing based on energy allocation strategy algorithm and QoS constraints perceived competitive power allocation algorithm, which improved network throughput, respectively, 3.4% and 1.5%, and increased the average spectral efficiency 9.3% and 7.4%, respectively.
- cognitive heterogeneous networks /
- cooperative spectrum sensing /
- interference constraints /
- power allocation /
- network throughput optimization

HTML全文

图 1 模拟场景图

Fig. 1 Simulation scene graph

下载: 全尺寸图片幻灯片

图 2 频谱利用率

Fig. 2 Spectrum utilization

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图 3 数据平均传输速率

Fig. 3 Data average transfer rate

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图 4 网络吞吐量

Fig. 4 Network throughput

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参考文献(14)

[1]	谢威, 马文峰, 韩鹏, 等.基于博弈论的异构无线网络动态选择[J].应用科学学报, 2015, 33(3):243-252. http://d.wanfangdata.com.cn/Periodical/yykxxb201503003
[2]	吴冀衍, 程渤, 南国顺, 等.面向异构无线网移动视频传输的联合信源信道编码方式[J].计算机学报, 2015, 38(2):439-454. http://d.wanfangdata.com.cn/Periodical/jsjxb201502018
[3]	HALDAR K L, GHOSH C, AGRAWAL D P. Dynamic spectrum access and network selection in heterogeneous cognitive wireless networks[J]. Pervasive & Mobile Computing, 2013, 9(4):484-497. http://d.wanfangdata.com.cn/OAPaper/oai_doaj-articles_32e4f2443daf114921c04f79d2d69f8c
[4]	OJANPERA T, LUOTO M, MAJANEN M, et al. Cognitive network management framework and approach for video streaming optimization in heterogeneous networks[J]. Wireless Personal Communications, 2015, 84:1739-1769. doi: 10.1007/s11277-015-2519-7
[5]	石华, 李建东, 李钊.认知异构网络中基于克隆选择算法的动态频谱分配[J].通信学报, 2012, 33(7):59-66. http://d.wanfangdata.com.cn/Periodical/txxb201207008
[6]	董全, 李建东, 赵林靖, 等.基于效用最大的多小区异构网络调度和功率控制方法[J].计算机学报, 2014, 2(2):373-383. http://d.wanfangdata.com.cn/Periodical/jsjxb201402010
[7]	ZHANG W, YANG Y, YEO C K. Cluster-based cooperative spectrum sensing assignment strategy for heterogeneous cognitive radio network[J]. Vehicular Technology IEEE Transactions on, 2015, 64(6):2637-2647. doi: 10.1109/TVT.2014.2342216
[8]	BACCI G, BELMEGA E V, MERTIKOPOULOS P, et al. Energy-aware competitive power allocation for heterogeneous networks under QoS constraints[J]. Wireless Communications IEEE Transactions on, 2015, 14(9):4728-4742. doi: 10.1109/TWC.2015.2425397
[9]	YANG G, WANG J, LUO J, et al. Cooperative spectrum sensing in heterogeneous cognitive radio networks based on normalized energy detection[J]. IEEE Transactions on Vehicular Technology, 2016, 65(3):1452-1463. doi: 10.1109/TVT.2015.2413787
[10]	EWAISHA A, TEPEDELENLIOGLU C. Joint scheduling and power-control for delay guarantees in heterogeneous cognitive radios[J]. IEEE Transactions on Wireless Communications, 2015, 15(9):6298-6309. http://ieeexplore.ieee.org/abstract/document/7496923
[11]	SHI S, LIANG N, GU X. A resource allocation method of heterogeneous wireless cognitive networks based on convex optimization theory[C]//Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 20144th International Conference on. IEEE, 2014:139-144.
[12]	CHEN Y, LEI Q, YUAN X. Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks[J]. Wireless Personal Communications, 2014, 79(3):1647-1664. doi: 10.1007/s11277-014-1950-5
[13]	AL-SAEED L, LI M, AL-RAWESHIDY H S. Cognitive data routing in heterogeneous mobile cloud networks[C]//Mobile Cloud Computing, Services, and Engineering (MobileCloud), 20142nd IEEE International Conference on. IEEE, 2014:194-199.
[14]	LIU X, EVANS B G, MOESSNER K. Energy-efficient sensor scheduling algorithm in cognitive radio networks employing heterogeneous sensors[J]. IEEE Transactions on Vehicular Technology, 2015, 64(3):1243-1249. doi: 10.1109/TVT.2013.2290031