基于变分贝叶斯推断的新型全局频谱协作感知算法

吴名; 宋铁成; 胡静; 沈连丰

doi:10.11959/j.issn.1000-436x.2016037

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基于变分贝叶斯推断的新型全局频谱协作感知算法

学术论文 | 更新时间：2024-06-05

- 基于变分贝叶斯推断的新型全局频谱协作感知算法
- Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference
- 通信学报 2016年37卷第2期页码：116-124
- 作者机构：
  
  东南大学移动通信国家重点实验室，江苏南京210096
- 作者简介：
  
  [ "吴名（1981-），男，江苏南京人，东南大学博士生，主要研究方向为认知无线电系统、协作通信、传感器网络等。" ]
  [ "宋铁成（1967-），男，江苏张家港人，博士，东南大学教授、博士生导师，主要研究方向为移动通信理论与技术、认知无线电、物联网等。" ]
  [ "胡静（1975-），女，江苏扬州人，博士，东南大学副研究员，主要研究方向为短距离无线通信、泛在网络等。" ]
  [ "沈连丰（1952-），男，江苏邳州人，东南大学教授、博士生导师，主要研究方向为宽带移动通信、短距离无线通信与泛在网络等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61271207);国家自然科学基金资助项目(61372104);国家自然科学基金资助项目(61201248)
- DOI：10.11959/j.issn.1000-436x.2016037
  中图分类号： TN914
- 网络出版日期：2016-02，
  
  纸质出版日期：2016-02-15
- 稿件说明：
移动端阅览
吴名, 宋铁成, 胡静, 等. 基于变分贝叶斯推断的新型全局频谱协作感知算法[J]. 通信学报, 2016,37(2):116-124.

Ming WU, Tie-cheng SONG, Jing HU, et al. Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference[J]. Journal on communications, 2016, 37(2): 116-124.
吴名, 宋铁成, 胡静, 等. 基于变分贝叶斯推断的新型全局频谱协作感知算法[J]. 通信学报, 2016,37(2):116-124. DOI： 10.11959/j.issn.1000-436x.2016037.

Ming WU, Tie-cheng SONG, Jing HU, et al. Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference[J]. Journal on communications, 2016, 37(2): 116-124. DOI： 10.11959/j.issn.1000-436x.2016037.

摘要

为了实现多维动态频谱接入，首先给出了主用户的全局功率谱近似模型，并构建了新型全局频谱协作感知算法的总体流程，以获得主用户网络中占用频段、功率及位置等全局信息。接着利用变分贝叶斯推断技术，设计了相应的模型系数向量估计器。仿真结果表明，该方法采用的近似模型具有较好的准确性，相应的系数向量估计算法具有较高的有效性和收敛稳定性，同时指明了信噪比和泄漏总虚假功率的关系以及两者对均方误差性能的影响。此外，还证明了该方法通过利用系数向量？的稀疏性，而在均方误差性能上具有较大优势。

Abstract

To realize multi-dimensional dynamic spectrum access

an approximate model was proposed for the global power spectral density (PSD)of primary users (PU). Based on the proposed model

a novel cooperative spectrum sensing algorithm was proposed

and its overall flow was also built to obtain global information in the network of PU. The global information included locations

occupied frequency bands and transmitting powers of the PU. Then

an estimator of mod-el coefficient vector was designed by utilizing the th of variational Bayesian inference (VBI). Simulation results show that the proposed approximate model has good accuracy

and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. Meanwhile

the relationship between SNR and the leakage of ag-gregate spurious power (LASP)was pointed out

and the influence of SNR and LASP on MSE performance was also discussed. Furthermore

it is proved that the proposed algorithm has better MSE performance than another algorithm since the sparsity of model coefficient vector is util zed.

关键词

Keywords

references

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GIANNAKIS G B , TEPEDELENLIOGLU C . Basis expansion models and diversity techniques for blind identification and equalization of time- varying channels [J ] . Proceedings of the IEEE , 1998 , 86 ( 10 ): 1969 - 1986 .

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WIPF D , OWEN J , ATTIAS H , et al . Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neura sources using MEG [J ] . Neuro Image , 2010 , 49 ( 1 ): 641 - 655 .

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