Optimizing Detection Using Multiple Threshold to Combat Low SNR Regime in CRN
Satria M.H., Marwanto A., Yusof S.K.S.
Abstract
The most problem in cognitive radio networks is low SNR regime while detecting the signals of primary user. The movement of the sensing node (MSN) which runs at an average speed of 70 km/h makes the primary node (PU) signal difficult to detect properly. The random movement of MSN in urban areas is in a low SNR regime. The distance of the PU and MN also determines the detection capability of the PU signal. The further the distance, the weaker the signal. The denser an urban area, the fainter the detected signal. The low SNR detected impacted the spectrum exchange information whenever converted into subcarrier that later became null. To mitigate this situation, multiple subcarrier conjugates in spectrum exchange information is proposed. The subcarrier in OFDM has two consecutive sides, i.e. positive and negative conjugate. The positive conjugate represented energy signal which is known as upper threshold and lower threshold. The trick is to measure the received signal, which is divided into the upper and the lower signals. Each will be converted into an OFDM signal tone in the form of a double subcarrier bit level. The sensing node and master node easily recognize the converted signal based on the signal level. The results show that probability of detection of lower SNR regime by increased detection higher of 1 to 12 dB in contrast to single threshold which start from 2 dB to 11.5 dB. While false alarm detection probability of double threshold has shown lower false detection of 2.1 dB rather than single conjugate which pointed higher false alarm probability of 3.63 dB. Therefore, the detection of subcarrier using multiple thresholds can perform low signal detection level in cognitive radio networks.
Software defined radio design for OFDM based spectrum exchange information using arduino UNO and X-Bee
Marwanto A., Satria M.H., Yusof S.K.S.
Orthogonal frequency-division multiplexing-based cooperative spectrum sensing for cognitive radio networks
Marwanto A., Satria M.H., Yusof S.K.S., Marwanto A., Satria M.H., Yusof S.K.S., Marwanto A., Satria M.H., Yusof S.K.S.
Improved convolutional neural network based cooperative spectrum sensing for cognitive radio
Satya Prasad K., Uppala A.R., Uppala A.R., Venkata Narasimhulu C.
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks
Huang X., Leng S., Ning W., Wu F., Yang K.
A Fast Soft Decision Algorithm for Cooperative Spectrum Sensing
Fakharzadeh M., Golvaei M.
Compressive Learning in Communication Systems: A Neural Network Receiver for Detecting Compressed Signals in OFDM Systems
Chafii M., De Souza P.H.C., Mendes L.L.
No Title
Vimala P.
Spectrum sensing and resource allocation for proficient transmission in cognitive radio with 5G
Meena M., Rajendran V.
Overlay dynamic spectrum sharing in cognitive radio for 4G and 5G using FBMC
Edward A.S., Sofia D.S.
Dynamic OFDM Transmission for a Cognitive Radio Device Based on a Neural Network and Multiresolution Analysis
Hernandez C.A., Lopez-Guerrero M., Pedraza L.F., Prieto-Guerrero A., Rodriguez-Colina E.