An adaptive threshold energy detection mechanism using GNU Radio on USRP

Reddy, Avuthu Avinash; Battula, Ramesh Babu; Gopalani, Dinesh; Kurra, Chaithanya

doi:10.1109/comsnets.2019.8711389

Cited by 2 publications

(1 citation statement)

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“…Sensing is an initial operation carried out by secondary users (SUs) in the cognitive radio network (CRN). Energy detection (ED) 27 is used to find unused portions in both licensed and unlicensed spectrum bands based on the signal energy. The below equation represents the hypothesis form of a received signal.…”

Section: Discern: Dynamic Noise Variance‐based Energy Sensingmentioning

confidence: 99%

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

Reddy

Battula

Gopalani

et al. 2020

Int J Communication

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Summary 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.

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Section: Discern: Dynamic Noise Variance‐based Energy Sensingmentioning

confidence: 99%