Forward Consecutive Mean Excision Algorithm Research Articles

Adaptive threshold techniques for cognitive radio‐based low power wide area network

AbstractSome low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR‐LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)‐based applications. However, in order to be effective under dynamic sensing conditions, CR‐LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR‐LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR‐LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR‐LPWAN systems.

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.

Narrowband signal localization based on enhanced LAD method

In this paper, an enhanced localization algorithm based on double thresholds (LAD) is proposed for localizing narrow band signals in the frequency domain. A simplified LAD method is first studied to reduce the computational complexity of the original LAD method without performance loss. The upper and lower thresholds of the simplified LAD method are directly calculated by running the forward consecutive mean excision algorithm only once. By combining the simplified LAD method and binary morphological operators, the enhanced LAD method is then proposed and its performance is simply discussed. The simulation results verify the correctness of discussion and show that the enhanced LAD method is superior to the LAD with adjacent cluster combining method, especially at low signal-to-noise ratio.