Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks

Wei YANG; Dong-song BAN; Huan-zhong LI; Wen-hua DOU

doi:1000-436X(2011)11-0118-08

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Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks

Papers | 更新时间：2024-06-05

- Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks
- Journal on Communications Vol. 32, Issue 11, Pages: 118-125(2011)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：1000-436X(2011)11-0118-08
  CLC： TP393
- Online First：2011-11，
  
  Published：25 November 2011
- 稿件说明：
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Wei YANG, Dong-song BAN, Huan-zhong LI, et al. Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks[J]. Journal on Communications, 2011, 32(11): 118-125.
DOI：

Wei YANG, Dong-song BAN, Huan-zhong LI, et al. Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks[J]. Journal on Communications, 2011, 32(11): 118-125. DOI： 1000-436X(2011)11-0118-08.

摘要

针对认知无线电网络中多个认知节点对多个通道进行协作感知的系统建立优化模型。该模型在各通道错误接入概率小于给定阈值的约束下，以最大化系统吞吐量为目标，对包括感知时间和各SU对各通道检测结果的权重系数在内的参数进行优化，是一个约束非线性规划模型。为求解该模型，提出了一种启发式的顺序参数优化方法（SPO

sequential parameters optimization method）。该方法首先推导出优化问题的下界并转而对该下界进行优化，随后通过构建了一系列仅含有权重系数的子优化问题并采用拉格朗日方法求解出优化的权重系数，待权重系数确定后，最后采用黄金分割搜索法确定优化的感知时间。仿真实验的结果表明了SPO的有效性并验证了提出的模型在提高系统吞吐量方面的优势。

Abstract

An optimization model under the scenario where multi-channels are cooperatively sensed and used by multi-secondary users (SU) was proposed.The model aims to maximize the system throughput and optimizes the parameters including the sensing time and the weight coefficient of the sampling result of each SU for each channel

meanwhile the false access probability for each channel must not violate the given constraints.To solve this non-linear optimization model

a sequential parameters optimization method(SPO)was proposed.The method begins with deriving the lower bound of the objective function of the optimization model.Then it maximizes this lower bound by optimizing the weight coefficients through solving a series of sub-optimal problems using Lagrange method

and finally finding an optimized sensing time parameter by the golden search algorithm.Extensive experiments by simulations demonstrate the effectiveness of the proposed method and the advantage of the proposed model on improving the system throughput.

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references

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