DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands

Spectrum sensing is one of the critical tasks in a cognitive radio system that allows a secondary user to use the spectrum while the primary user does not use it. The energy detection (ED) sensing is one of the most common techniques to identify the unused portions in the spectrum bands. In ED, threshold plays a vital role in signal detection, and noise is one of the significant factors in threshold calculation. However, ED efficiency is degraded by the noise uncertainty phenomenon caused by the random changes in noise level. The adverse effects of noise uncertainty are reduced by changing its detection threshold dynamically to the noise circumstances encountered during each sensing period. In the proposed method, received random samples are arranged in M blocks, applied strong Pearson correlation to separate and estimate the variance from the noise samples. The enhanced dynamic noise variance‐based energy sensing is implemented in GNU radio processing blocks and tested on industrial, scientific, medical (ISM) 2.4 GHz frequency bands by using national instrument universal software radio peripheral (NI USRP‐2932) device. The experimental results of proposed energy detction mechanism are compared with existing sensing techniques.     

An efficient spectrum sensing over $$\eta - \mu$$ η - μ fading on sub 6 GHz bands: A real-time implementation on USRP RIO

Wireless Networks - Massive machine-type communications (mMTC) face spectrum scarcity problems at 5G mid-bands (i.e., sub 6 GHz) due to tens of billions of machine-type devices connected in a...