摘要：
在认知无线网络(CRN)中,基于压缩感知的宽带频谱检测仅关注频谱有效性,未考虑到频谱检测过程中节点的能效问题,在提高频谱检测性能的同时造成节点能耗开销过大。为此,提出一种在保障节点能量有效性基础上,进行基于贝叶斯压缩感知(BCS)稀疏重构的CRN宽带频谱检测方法。推导感知能耗解析式,构造节点感知能耗模型,在满足宽带压缩频谱检测概率和BCS重构均方误差(MSE)阈值的约束条件下,通过改变BCS测量矩阵中采样点数实现感知能耗最小化。仿真结果表明,当虚警概率为0.04时,在采样点数较小的情况下,该方法的检测概率高于感知-能耗折衷方法。在重构MSE小于15 dB的条件下,与正交匹配追踪方法的重构能耗相比,基于BCS的节点重构能耗明显下降。
关键词:
能量有效性,
贝叶斯压缩感知,
宽带频谱检测,
检测性能,
重构均方误差
Abstract:
In Cognitive Radio Network(CRN),wideband spectrum detection based on Compressive Sensing(CS) only focuses on spectral efficiency.Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the improvement of spectrum detection performance.Hence,CRN wideband spectrum detection method based on Bayesian Compressive Sensing(BCS) sparse reconstruction with the guarantee of energy efficiency is proposed.Sensing energy analytical formula is derived and the corresponding energy consumption model is constructed.The minimization of sensing energy consumption can be achieved via the change of sample numbers in BCS measurement matrix,with the constraint conditions of wideband compressive spectrum detection probability and BCS reconstruction Mean Square Error(MSE) thresholds.Simulation result indicates that,when false alarm probability is 0.04,the detection probability of the proposed method outperforms sensing-energy tradeoff approach obviously in fewer sample numbers scenario.Moreover,when reconstruction MSE is lower than 15 dB,compared with Orthogonal Matching Pursuit(OMP) method,reconstruction energy consumption based on BCS can be reduced obviously.
Key words:
energy efficiency,
Bayesian Compressive Sensing(BCS),
wideband spectrum detection,
detection performance,
reconstruction Mean Square Error(MSE)
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