This study explores the capabilities of an Energy Detector (ED)-based Cognitive Radio (CR) device in a double shadowed (DS) Rician distribution environment for spectrum sensing. The effectiveness of the ED is evaluated using metrics such as average probability of detection (PD) and average area under the receiver operating characteristics curve (AUC). The investigation extends to single-input-multiple-output (SIMO) channel conditions, considering maximal ratio combining (MRC) and square-law selection (SLS) diversity techniques. To provide a comprehensive understanding of the system, the study also includes the asymptotic performance analysis of diversity techniques, including MRC, equal gain combining (EGC), and selection combining (SC). The findings indicated a notable enhancement in detection performance through the application of diversity combining techniques. Among the evaluated schemes, MRC demonstrated the most favorable results, followed EGC, while SC yielded comparatively weaker outcomes. To further minimize error probability, the detection threshold is optimized, resulting in improved energy detection statistics, even in scenarios involving diversity. The formulations are additionally applied to cooperative spectrum sensing (CSS). It further noted that fading parameter directly influence the array gain, while diversity gain only depends on the number of diversity branches. The accuracy and validity of the proposed results are confirmed through Monte-Carlo simulation.