[1]叶芝慧,冯奇,王健.基于学习策略的动态频谱接入信道选择及系统性能[J].东南大学学报(自然科学版),2012,42(6):1041-1046.[doi:10.3969/j.issn.1001-0505.2012.06.004]
 Ye Zhihui,Feng Qi,Wang Jian.Learning-based channel selection and system performance of dynamic spectrum access[J].Journal of Southeast University (Natural Science Edition),2012,42(6):1041-1046.[doi:10.3969/j.issn.1001-0505.2012.06.004]
点击复制

基于学习策略的动态频谱接入信道选择及系统性能()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第6期
页码:
1041-1046
栏目:
信息与通信工程
出版日期:
2012-11-20

文章信息/Info

Title:
Learning-based channel selection and system performance of dynamic spectrum access
作者:
叶芝慧 冯奇 王健
南京大学电子科学与工程学院, 南京 210023
Author(s):
Ye Zhihui Feng Qi Wang Jian
School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
关键词:
认知无线电 动态频谱接入 信道选择 丢包率 吞吐量
Keywords:
cognitive radio dynamic spectrum access channel selection packet loss rate throughput
分类号:
TN92
DOI:
10.3969/j.issn.1001-0505.2012.06.004
摘要:
为了提高认知无线网络的丢包率和吞吐量性能,运用基于学习策略的动态频谱接入方法感知授权信道.在已知主用户先验信息的条件下,认知用户根据主用户信号忙闲的统计特性,按照空闲状态概率从高到低的顺序依次对目标授权信道进行频谱感知,并接入第1个检测到的空闲信道.针对固定速率和可变速率2种业务类型,分别考察了认知无线网络的丢包率和吞吐量性能.在未知主用户先验统计特性的条件下,认知用户的平均错误预测概率直接影响着系统性能.仿真结果表明,与随机频谱感知性能相比,基于机器学习和预测的频谱感知的丢包率和吞吐量性能均有所提高.认知用户基于学习策略的动态频谱接入信道选择方法可以有效利用频谱空穴,提高系统频带利用率.
Abstract:
To improve the performances of packet loss rate and throughput in cognitive radio network, an approach for learning-based dynamic spectrum access is put forward for spectrum sensing. A channel selection scheme is proposed under the condition that the priori information of primary user is known. Based on the idle and busy state statistical properties of the primary user signal, secondary user performs sensing on the objective licensed channel in accordance with the vacancy state probability in descending order, and then accesses to the first channel detected to be idle. For the fixed rate and variable rate services, the performances of packet loss rate and throughput of cognitive radio network are analyzed. As for the channel selection scheme in the case of the unknown priori information to the secondary user, the system performances are influenced by the mean probability of error prediction. Simulation results show that the proposed dynamic spectrum access scheme based on learning and prediction achieves better performances in packet loss rate and throughput than the random spectrum sensing. Learning-based channel selection of dynamic spectrum access can exploit spectrum holes efficiently and improve the channel utilization in cognitive radio systems.

参考文献/References:

[1] Mitola J,Magurire G Q.Cognitive radio:making software radios more personal [J].IEEE Personal Communications,1999,6(4):13-18.
[2] 李凡长,钱旭培,谢琳,等.机器学习理论及应用 [M].合肥:中国科学技术大学出版社,2009:1-10.
[3] Haykin S,Thomson D J,Reed J H.Spectrum sensing for cognitive radio [J].Proceedings of the IEEE,2009,97(5):849-877.
[4] Clancy C,Hecker J,Stuntebeck E,et al.Application of machine learning to cognitive radio networks [J].IEEE Wireless Communications,2007,14(4):47-52.
[5] Yu F R,Huang M,Tang H.Biologically inspired consensus-based spectrum sensing in mobile Ad-Hoc networks with cognitive radios [J].IEEE Network,2010,24(3):26-30.
[6] Geirhofer S,Lang T,Sadler B M.Cognitive radios for dynamic spectrum access-dynamic spectrum access in the time domain:modeling and exploiting white space [J].IEEE Communications Magazine,2007,45(5):66-72.
[7] Kim H,Shin K G.Fast discovery of spectrum opportunities in cognitive radio networks [C] //Proceedings of 2008 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.Chicago,IL,USA,2008:1-12.
[8] Kim H,Shin K G.Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks [J].IEEE Transactions on Mobile Computing,2008,7(5):533-545.
[9] Wang X,Wong A,Ho P H.Extended knowledge-based reasoning approach to spectrum sensing for cognitive radio [J].IEEE Transactions on Mobile Computing,2010,9(4):465-478.
[10] Vijay G,Ben A B E,Ibnkahla M.Cognition in wireless sensor networks:a perspective [J].IEEE Sensors Journal,2011,11(3):582-592.
[11] Yuan G,Grammenos R C,Yang Y,et al.Performance analysis of selective opportunistic spectrum access with traffic prediction [J].IEEE Transactions on Vehicular Technology,2010,59(4):1949-1959.
[12] 沈连丰,叶芝慧.信息论与编码 [M].北京:科学出版社,2004:113-123.

相似文献/References:

[1]叶芝慧,王涛,冯奇.一种基于博弈论价格机制的频谱分配算法[J].东南大学学报(自然科学版),2014,44(3):462.[doi:10.3969/j.issn.1001-0505.2014.03.002]
 Ye Zhihui,Wang Tao,Feng Qi.Spectrum allocation algorithm based on game theory with price mechanism[J].Journal of Southeast University (Natural Science Edition),2014,44(6):462.[doi:10.3969/j.issn.1001-0505.2014.03.002]
[2]左加阔,赵力,邹采荣.时延服务质量约束下的高能效功率分配算法[J].东南大学学报(自然科学版),2015,45(4):635.[doi:10.3969/j.issn.1001-0505.2015.04.004]
 Zuo Jiakuo,Zhao Li,Zou Cairong.Energy-efficient power allocation algorithm with delay QoS constraints[J].Journal of Southeast University (Natural Science Edition),2015,45(6):635.[doi:10.3969/j.issn.1001-0505.2015.04.004]
[3]吴名,宋铁成,沈连丰,等.一种噪声未知的新型空间频谱分布协作感知算法[J].东南大学学报(自然科学版),2016,46(2):231.[doi:10.3969/j.issn.1001-0505.2016.02.001]
 Wu Ming,Song Tiecheng,Shen Lianfeng,et al.Novel cooperative sensing algorithm for spatial spectrum distribution with unknown noises[J].Journal of Southeast University (Natural Science Edition),2016,46(6):231.[doi:10.3969/j.issn.1001-0505.2016.02.001]

备注/Memo

备注/Memo:
作者简介: 叶芝慧(1967—),女,博士,教授,zhye@nju.edu.cn.
基金项目: 国家自然科学基金资助项目(60932002)、江苏省自然科学基金资助项目(BK2010380)、江苏省科技支撑计划资助项目(BE2012155)、江苏省高校优势学科建设工程资助项目.
引文格式: 叶芝慧,冯奇,王健.基于学习策略的动态频谱接入信道选择及系统性能[J].东南大学学报:自然科学版,2012,42(6):1041-1046. [doi:10.3969/j.issn.1001-0505.2012.06.004]
更新日期/Last Update: 2012-11-20