Millimeter waves (mmWaves) in the V band have recently become widely used in cognitive/5G radio networks to improve spectral efficiency. This paper presents an algorithm based on three techniques: Alpha Beta (α-β) Filter, Simple Recursive (S-R) estimator, and double threshold energy detection, which have been used to analyze the detection of the primary signal performance by the cognitive user 5G (CU5G) in a cluttered indoor environment at 60 GHz. The CU5G is considered to be in movement using a Gauss Markov mobility model at low speed. This study evaluates the received signal power at the CU5G under the AWGN channel. To achieve this, we have implemented two propagation models: Close In and Floating-Intercept. These models simulate different line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, ensuring a comprehensive evaluation of the algorithm’s performance. Finally, the probability of total detection error has been examined in the proposed algorithm, further validating its effectiveness.