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引用次数: 0
摘要
目前大多数的频谱传感方法都假定某些参数的概率密度函数是事先已知的。然而,在大多数实际应用中,通信环境通常随时间而变化。先前估计的假定PDF可能与实际PDF不同。这将大大降低大多数频谱传感方法的性能。在本文中,我们提出了一个健壮的方法来解决这个问题。加权经验似然比检验(weighted Empirical Likelihood Ratio Test, ELRT)是统计数学中一种有效的方法,可以提高统计数学在小样本量下对PDF知识不精确影响的稳健性。加权ELRT通过重新加权实际PDF对似然的贡献来获得鲁棒性。我们将加权ELRT用于频谱感知,并将这种新方法称为基于加权ELRT的鲁棒频谱感知。仿真结果验证了该方法优于传统方法的有效性。
A weighted ELRT-based robust spectrum sensing algorithm
Most of current spectrum sensing methods assume the probability density functions (PDFs) about some parameters are known beforehand. However, in most practical applications, the communication environments usually vary with time. The presumed PDF estimated previously might be different from the actual one. It will degrade the performance of most spectrum sensing methods dramatically. In this paper, we propose a robust approach to address this problem. The weighted Empirical Likelihood Ratio Test (ELRT) is an effective method in statistical mathematics which can improve the robustness against the effect of imprecise knowledge of PDF under small sample size. The weighted ELRT obtains the robustness by re-weighting the contributions of actual PDF to likelihood. We employ the weighted ELRT in spectrum sensing, and term this new method as weighted ELRT-based robust spectrum sensing. Simulation results corroborate the performance of the proposed method over those of conventional ones.