基于组合投资理论与主用户QoS保证的认知系统资源分配算法

梁辉; 赵晓晖

doi:1000-436X(2011)11-0044-07

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基于组合投资理论与主用户QoS保证的认知系统资源分配算法

学术论文 | 更新时间：2024-06-05

- 基于组合投资理论与主用户QoS保证的认知系统资源分配算法
- Portfolio selection and primary user QoS guarantee based resource allocation in cognitive radio networks
- 通信学报 2011年32卷第11期页码：44-50
- 作者机构：
- 作者简介：
  
  [ "梁辉（1983-），男，吉林长春人，吉林大学博士生，主要研究方向为认知无线电系统中的资源分配技术。" ]
  [ "赵晓晖（1957-），男，满族，北京人，吉林大学教授、博士生导师，主要研究方向为自适应信号处理理论及在通信中的应用。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61171079)
- DOI：1000-436X(2011)11-0044-07
  中图分类号： TN929.5
- 网络出版日期：2011-11，
  
  纸质出版日期：2011-11-25
- 稿件说明：
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梁辉, 赵晓晖. 基于组合投资理论与主用户QoS保证的认知系统资源分配算法[J]. 通信学报, 2011,32(11):44-50.

Hui LIANG, Xiao-hui ZHAO. Portfolio selection and primary user QoS guarantee based resource allocation in cognitive radio networks[J]. Journal on communications, 2011, 32(11): 44-50.
梁辉, 赵晓晖. 基于组合投资理论与主用户QoS保证的认知系统资源分配算法[J]. 通信学报, 2011,32(11):44-50. DOI： 1000-436X(2011)11-0044-07.

Hui LIANG, Xiao-hui ZHAO. Portfolio selection and primary user QoS guarantee based resource allocation in cognitive radio networks[J]. Journal on communications, 2011, 32(11): 44-50. DOI： 1000-436X(2011)11-0044-07.

摘要

受组合投资理论的启发，提出了一种既利用历史信道状态信息，同时又有效保证主用户不受次系统传输所产生干扰的新的资源分配算法。该算法以系统速率的方差作为优化目标，同时通过引入用户间干扰门限来衡量次系统对主用户造成的干扰，并利用二次规划的方法对该问题进行求解。最后，给出经典算法与本算法的性能比较，仿真结果表明该算法在保证次系统传输速率保持在一定的期望速率的条件下，使其方差最小，同时又使主用户所受的干扰限定在所能承受的范围内。

Abstract

Inspired by portfolio selection theory

an efficient primary user QoS guaranteed resource allocation algorithm from secondary user interference by using historical channel state information was proposed.This algorithm formulates the risk as optimization objective

and restricts the interference which comes from secondary system to primary users below a mutual interference threshold.Then quadratic programming method is used to solve this optimization problem.Finally

performance comparison between the original algorithm without protection and the proposed algorithm was given.Simulation results illustrate that the proposed algorithm can minimize secondary system risk under an expected transmission rate and guarantee primary user’s QoS.

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Keywords

references

MITOLA J . Cognitive radio for flexible mobile multimedia communications [A ] . Proc of the IEEE International Workshop on Mobile Multimedia Communications99 [C ] . San Diego,CA,USA , 1999 . 3 - 10 .

HAYKIN S . Cognitive radio:brain-empowered wireless communications [J ] . IEEE Journal on Selected Areas , 2005 , 23 ( 2 ): 201 - 220 .

MARKOWITZ H . Portfolio selection [J ] . Journal of Finance , 1952 , 7 ( 1 ): 77 - 91 .

MWANGOKA J W , BEN LETAIEF K , CAO Z G . Robust end-to-end QoS maintenance in non contiguous OFDM based cognitive radios [A ] . Proc of the IEEE International Conference on Communications08 [C ] . Beijing,China , 2008 . 2905 - 2909 .

MWANGOKA J W , BEN LETAIEF K , CAO Z G . Statistical resource allocation for multi-band cognitive radio systems [J ] . Physical Communication , 2009 , 2 ( 1-2 ): 116 - 126 .

WYSOCKI T , JAMALIPOUR A . Portfolio selection based power allocation in OFDM cognitive radio networks [A ] . The 3rd International Conference on Signal Processing and Communication Systems09 [C ] . Omaha,NE,USA , 2009 . 1 - 7 .

WYSOCKI T , JAMALIPOUR A . Mean-variance based QoS management in cognitive radio [J ] . IEEE Transactions on Wireless Communications , 2010 , 9 ( 10 ): 3281 - 3289 .

WEISS T , HILLENBRAND J , KROHN A , et al . Mutual interference in OFDM-based spectrum pooling systems [A ] . IEEE 59th Vehicular Technology Conference04-Spring [C ] . Milan,Italy , 2004 . 1873 - 1877 .

AGRESTI A . Categorical Data Analysis,2nd ed [M ] . US : Wiley Press , 2002 .

FRANKLIN J . Methods of Mathematical Economics:Linear and Nonlinear Programming,Fixed-Point Theorems [M ] . New York : Springer Press , 1980 .

BERTSEKAS D P . Nonlinear Programming,2nd ed [M ] . Boston,MA : Athena Scientific Press , 1999 .

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