Performance Evaluation of an IoT Device using a Cognitive Radio in GLRT Approach

Spectrum sensing in cutting edge remote radio systems is viewed as a critical innovation to beat the issue of range shortage. Tragically, numerous ways to deal with range detecting do not function admirably in low signal to noise ratio (SNR) situations and noise uncertainty. The GLRT approach can form other algorithms if it uses a derivation with the right assumptions. In previous research, the calculation of the GLRT approach is generally used in multi-antenna equipment, given the fact that the idea of the GLRT approach can accept multi-antenna wire parameters. Multi-antenna is used to describe the number of IoT devices because these devices are widely used in the industrial revolution now. Signal space-time block coding (MIMO-STBC-OFDM) is used as a multi-antenna signal from the primary signal (PU). Rayleigh distributed channel using a Geometric Based Single Bounce (GBSB) and also use as the multi-antenna from the secondary user (SU). This research wants to show if all algorithms derived from the GLRT approach can overcome the problem of noise uncertainty, small SNR, and choose which algorithm is very suitable for use in this condition. Simulation set up to solve issues shown by the results of the correlation multi-antenna receiver matrix (Rx) in the SU. The results show that the GLRT algorithm is robust because the GLRT approach does not require noise power assumption. On the other hand, the calculation of the algorithm using the GLRT approach shows if the algorithm can overcome the problem of vulnerability at a low SNR level and noise uncertainty. The final comparison results also show that the GLRT Kernel (KGLRT) algorithm can be stable in a multi-antenna IoT device.