Edge-Fitting Based Energy Detection for Cognitive Radios

Amplitude quantization method for autonomous threshold estimation in self-reconfigurable cognitive radio systems

“…technique [19], and the first order statistical technique (FOST) [20], Barne's approach [21], and many other methods as in [22][23][24][25][26]. Essentially, these methods depend on critical parameters that are fine-tuned a priori based on some input noise level or some theoretical distribution of the input sample.…”

Generalized self‐tuning system for adaptive threshold estimators in cognitive radio systems using swarm and evolutionary‐based approaches

“…The maximum normal fit method proposed by Barnes et al, also requires the manual fine‐tuning of a few parameters, such as the initial SD and amplitude of the estimated noise distribution 9 . Most methods identified in References 10‐15 typically depend on different parameter values that must be fine‐tuned in order to enhance the performance of the different ATEs. Primarily, most authors often refine the parameters of their respective ATEs by testing them using predefined datasets, or in some other cases, by theoretical computations based on presumed distributions 16 .…”

“…Furthermore, manual tuning can be a cumbersome process, particularly for ATEs characterized by large number of parameters. It is also impossible to obtain global parameter values for every ATE across all possible sensing conditions and signal types, a fact that is evident across most ATEs 10‐15,17,18 …”