高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

动态频谱接入中基于最小贝叶斯风险的稳健频谱预测

陈曦 杨健

陈曦, 杨健. 动态频谱接入中基于最小贝叶斯风险的稳健频谱预测[J]. 电子与信息学报, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519
引用本文: 陈曦, 杨健. 动态频谱接入中基于最小贝叶斯风险的稳健频谱预测[J]. 电子与信息学报, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519
CHEN Xi, YANG Jian. Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access[J]. Journal of Electronics and Information Technology, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519
Citation: CHEN Xi, YANG Jian. Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access[J]. Journal of Electronics and Information Technology, 2018, 40(3): 734-742. doi: 10.11999/JEIT170519

动态频谱接入中基于最小贝叶斯风险的稳健频谱预测

doi: 10.11999/JEIT170519
基金项目: 

国家自然科学基金(61471395, 61471392, 61301161),江苏省自然科学基金(BK20141070)

Minimum Bayesian Risk Based Robust Spectrum Prediction in Dynamic Spectrum Access

Funds: 

The National Natural Science Foundation of China (61471395, 61471392, 61301161), The Natural Science Foundation of Jiangsu Province (BK20141070)

  • 摘要: 针对频谱感知错误累积造成频谱预测性能恶化问题,该文提出一种基于最小贝叶斯风险的稳健频谱预测策略。分布拟合检验表明频谱预测输出服从正态分布,定义频谱预测输出的贝叶斯风险函数,证明使贝叶斯风险函数最小的频谱预测输出判决门限将使频谱预测的均方误差最小,求得了使贝叶斯风险最小的最优判决门限,构建稳健频谱预测策略。仿真结果表明,与固定判决门限的神经网络频谱预测相比,稳健频谱预测策略改进了频谱感知错误下的频谱预测性能,改善了非授权用户的动态频谱接入性能。
  • NING Guoqin and NINTANAVONGSA P. Time prediction based spectrum usage detection in centralized cognitive radio networks[C]. 2012 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, 2012: 300-305. doi: 10.1109/WCNC.2012.6214320.
    WANG Tan, LI Gen, and DING Jiaxin. 5G Spectrum: Is China ready?[J]. IEEE Communications Magazine, 2015, 53(7): 58-65. doi: 10.1109/MCOM.2015.7158266.
    SONG Yi and XIE Jiang. ProSpect: A proactive spectrum handoff framework for cognitive radio ad hoc networks without common control channel[J]. IEEE Transactions on Mobile Computing, 2012, 11(7): 1127-1139. doi: 10.1109/ TMC.2011.140.
    GUAN Quansheng, RICHARD Y F, and JIANG Shengming. Prediction-based topology control and routing in cognitive radio mobile Ad hoc networks[J]. IEEE Transactions on Vehicular Technology, 2011, 59(9): 4443-4452. doi: 10.1109/ TVT.2010.2069105
    GHOSH A and SARKAR S. Quality-sensitive price competition in secondary market spectrum oligopoly-single location game[J]. IEEE/ACM Transactions on Networking, 2016, 24(3): 1894-1907. doi: 10.1109/TNET.2015.2440422.
    GHOSH A, SARKAR S, and BERRY R. The value of side information in secondary spectrum markets[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(1): 6-19. doi: 10.1109/JSAC.2016.2632579.
    KUMAR A, SINGH S, and ZHENG Haitao. Reliable open spectrum communications through proactive spectrum access[C]. 2006 International Workshop on Technology and Policy for Accessing Spectrum (TAPAS), Boston, American, 2006: 5. doi: 10.1145/1234388.1234393.
    ELTOM H, KANDEEPAN S, LIANG Yingchang, et al. HMM based cooperative spectrum occupancy prediction using hard fusion[C]. 2016 IEEE International Conference on Communications Workshops (ICC), Kuala Lumpur, Malaysia, 2016: 669-675. doi: 10.1109/ICCW.2016.7503864.
    TUMULURU V, WANG Ping, and NIYATO D. Channel status prediction for cognitive radio networks[J]. Wireless Communications and Mobile Computing, 2012, 12(10): 862-874. doi: 10.1002/wcm.1017.
    SU Jinzhao and WU Wei. Wireless spectrum prediction model based on time series analysis method[C]. 2009 ACM workshop on Cognitive radio networks, Beijing, China (CoRoNET), Beijing, China, 2009: 61-66. doi: 10.1145/ 1614235.1614250.
    WEN Zheng, LUO Tian, and WANG Xiang. Autoregressive spectrum holes prediction model for cognitive radio systems[C]. 2008 IEEE Communications Workshop (ICC), Beijing, China, 2008: 154-157. doi: 10.1109/ICCW.2008.34.
    TANG Mengyun, DING Guoru, WU Qihui, et al. A joint tensor completion and prediction scheme for multi- dimensional spectrum map construction[J]. IEEE Access, 2016(4): 8044-8052. doi: 10.1109/ACCESS.2016.2627243.
    RAZALI N M and WAH Y B. Power comparisons of shapiro- wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests[J]. Journal of Statistical Modeling and Analytics, 2011, 2(1): 21-33.
    BAO Yong. On sample skewness and kurtosis[J]. Econometric Reviews, 2013, 32(4): 415-448. doi: 10.1080/07474938.2012. 690665.
    MASONTA M, MZYECE M, and NTLATLAPA N. Spectrum decision in cognitive radio networks: A survey[J]. IEEE Communications Surveys Tutorials, 2013, 15(3): 1088-1107. doi: 10.1109/SURV.2012.111412.00160.
  • 加载中
计量
  • 文章访问数:  524
  • HTML全文浏览量:  55
  • PDF下载量:  154
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-05-27
  • 修回日期:  2017-11-29
  • 刊出日期:  2018-03-19

目录

    /

    返回文章
    返回

    官方微信,欢迎关注