A Generic Composite Hypothesis Using Horizontal and Vertical Frameworks for AWGN Blind Detection in a Cognitive Radio System

Abstract

Classifying the Additive White Gaussian Noise (AWGN) and the Primary User (PU) transmission signal has been a crucial area of research interest. For effective data transmission, the Secondary User (SU) detection threshold needs to be continuously adjusted to distinguish between the AWGN and PU signals at a very low Signal-to-Noise Ratio (SNR). The research analytically compared a proposed Vertical Hypothesis (VH) against the Horizontal Hypothesis (HH) of Spectrum Sensing (SS) within a Cognitive Radio (CR) framework. The developed model aims to increase SU sensitivity and specificity to AWGN and PU signal powers. The performance indices used are the composite hypotheses of spectrum detection Pd, missed detection Pmd, to obtain a false alarm Pfa, and blind detection, Pbd. The results obtained clearly showed that the vertical approach, when varied, achieved a threshold of about 0.96 blind detection and 0.04 false alarm at -10 dB channel gain between PU and SU, which is an improvement over the IEEE guideline threshold of 0.9 spectrum detection and 0.1 false alarm, respectively. The vertical approach proved more efficient at optimizing radio spectrum utilization, potentially improving transmission quality in Fifth Generation (5G) and Sixth Generation (6G) wireless systems.