Enhancing Hybrid Spectrum Access in CR-IoT Networks: Reducing Sensing Time in Low SNR Environments

Abstract

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, the high level of noise uncertainty can cause the energy detector to spend more time sensing, which results in a significant reduction in transmission time and overall throughput. In this study, a novel approach is presented to address the challenge of noise uncertainties on the energy detector by implementing an adaptive sensing threshold. The simulation outcomes reveal that the proposed technique can significantly improve the throughput in scenarios characterized by low signal-to-noise ratio (SNR), with a maximum enhancement of up to 28%. This innovative approach can pave the way for more robust and efficient energy detectors in the presence of noise uncertainties .