当前位置:
X-MOL 学术
›
Trans. Emerg. Telecommun. Technol.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Generalized self‐tuning system for adaptive threshold estimators in cognitive radio systems using swarm and evolutionary‐based approaches
Transactions on Emerging Telecommunications Technologies ( IF 3.6 ) Pub Date : 2020-12-03 , DOI: 10.1002/ett.4186 Adeiza J. Onumanyi 1 , Adnan M. Abu‐Mahfouz 1, 2 , Gerhard P. Hancke 1, 3
Transactions on Emerging Telecommunications Technologies ( IF 3.6 ) Pub Date : 2020-12-03 , DOI: 10.1002/ett.4186 Adeiza J. Onumanyi 1 , Adnan M. Abu‐Mahfouz 1, 2 , Gerhard P. Hancke 1, 3
Affiliation
Parameter‐based adaptive threshold estimators (ATEs) are widely used for signal detection in cognitive radio (CR) systems. However, their performance deteriorates under dynamic spectra conditions owing to a lack of valid methods to accurately self‐tune the different parameters of such ATEs. In this article, we address this limitation by proposing a generalized system for self‐tuning the parameters of any ATE based only on the input signal measured per time. We adopt swarm and evolutionary‐based metaheuristic optimization techniques to effectively search for the optimal parameter values of any ATE. Our system controls the search process by applying the between‐class variance function adapted from Otsu's algorithm as the objective function. We tested the system using five different metaheuristic optimization algorithms (MOAs) to self‐tune two different ATEs, namely the recursive one‐sided hypothesis testing (ROHT) technique and the histogram partitioning algorithm under Rayleigh and Rician fading channels, as well as under different modulation schemes, including the 4‐quadrature amplitude modulation and 4‐phase shift keying schemes. Our findings suggest that our proposed system yields generally a small error rate irrespective of the MOA used. In addition, the ROHT‐cuckoo search optimization configuration yielded a reasonably high and low probability of detection and probability of false alarm, respectively, as a function of the signal‐to‐noise‐ratio of the input signal at a fast average processing time of 0.0699 seconds. We concluded that our system presents an effective mechanism that can be used to automatically tune the parameters of any ATE for useful signal detection in CR.
中文翻译:
使用群体和基于进化的方法的认知无线电系统中自适应阈值估计器的广义自调整系统
基于参数的自适应阈值估计器(ATE)被广泛用于认知无线电(CR)系统中的信号检测。但是,由于缺乏有效的方法来准确地自动调整此类ATE的不同参数,它们在动态频谱条件下的性能会下降。在本文中,我们通过提出一种通用系统来解决此限制,该系统可以仅根据每次测量的输入信号对任何ATE的参数进行自调整。我们采用基于群体和进化的元启发式优化技术来有效搜索任何ATE的最佳参数值。我们的系统通过应用从Otsu的算法改编的类间方差函数作为目标函数来控制搜索过程。我们使用五种不同的元启发式优化算法(MOA)对两个不同的ATE进行自调整,分别测试了系统,即递归的单侧假设测试(ROHT)技术和在瑞利衰落信道和Rician衰落信道下以及在不同衰落信道下的直方图分区算法调制方案,包括四正交幅度调制和四相移键控方案。我们的发现表明,与使用的MOA无关,我们提出的系统通常会产生较小的错误率。此外,ROHT-cuckoo搜索优化配置分别产生了相对较高的检测概率和较低的虚假概率以及虚假警报的概率,这取决于输入信号在快速平均处理时间下的信噪比。 0.0699秒
更新日期:2021-01-13
中文翻译:
使用群体和基于进化的方法的认知无线电系统中自适应阈值估计器的广义自调整系统
基于参数的自适应阈值估计器(ATE)被广泛用于认知无线电(CR)系统中的信号检测。但是,由于缺乏有效的方法来准确地自动调整此类ATE的不同参数,它们在动态频谱条件下的性能会下降。在本文中,我们通过提出一种通用系统来解决此限制,该系统可以仅根据每次测量的输入信号对任何ATE的参数进行自调整。我们采用基于群体和进化的元启发式优化技术来有效搜索任何ATE的最佳参数值。我们的系统通过应用从Otsu的算法改编的类间方差函数作为目标函数来控制搜索过程。我们使用五种不同的元启发式优化算法(MOA)对两个不同的ATE进行自调整,分别测试了系统,即递归的单侧假设测试(ROHT)技术和在瑞利衰落信道和Rician衰落信道下以及在不同衰落信道下的直方图分区算法调制方案,包括四正交幅度调制和四相移键控方案。我们的发现表明,与使用的MOA无关,我们提出的系统通常会产生较小的错误率。此外,ROHT-cuckoo搜索优化配置分别产生了相对较高的检测概率和较低的虚假概率以及虚假警报的概率,这取决于输入信号在快速平均处理时间下的信噪比。 0.0699秒