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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Mochigar, Srikantha Kandhgal
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Computer Science & IT Electrical & Electronics Engineering

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Improved quantum inspired evolution algorithm with ResNet50 for spectrum sensing in cognitive radio networks Mochigar, Srikantha Kandhgal; Matad, Rohitha Ujjini
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8312

Abstract

Spectrum is considered one of the most highly regulated and limited natural resources. Cognitive radio (CR) relies on cutting-edge technology which helps to rectify the issues related to spectrum shortage in wireless communication systems. The CR technology allows the secondary user to accomplish the process related to spectrum sensing for identifying the usage of spectrum in the cognitive radio network (CRN). Though various spectrum sensing approaches are introduced, they exhibit complexity during spectrum sensing. To overcome the issues related to spectrum sensing and utilization, this research introduces improved quantum inspired evolution (IQISE) algorithm with ResNet 50 architecture. The IQISE-ResNet 50 which helps to enhance the spectrum efficiency is used in spectrum sensing. The detection of occupied and unoccupied users in CRN is performed using ResNet 50 architecture, while the IQISE is utilized in the process of training the model and optimizing the weights to enhance spectrum sensing efficiency. The experimental results show that the results achieved by the proposed approach are more effective than S-QRNN and honey badger remora optimization-based AlexNet (HBRO-based AlexNet). For example, the probability of correct classification of the proposed approach at -10 dB for binary phase shift keying (BPSK) modulation is 0.55, whereas the S-QRNN achieves an accuracy of 0.49.
Enhanced spectrum sensing in MIMO-OFDM cognitive radio networks using multi-user detection and square-law combining techniques Mochigar, Srikantha Kandhgal; Matad, Rohitha Ujjini; Ramanaik, Premachand Doddamagadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5401-5410

Abstract

Spectrum sensing (SS) is essential for cognitive radio (CR) networks to enable secondary users to opportunistically access unused spectrum without interfering with primary users. This article proposes a novel multi-user detection (MUD) and square-law combining (SLC) framework for SS in multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) CR networks. Traditional SS methods, especially energy detection (ED), often underperform in low signal-to-noise ratio (SNR) conditions, resulting in high false alarm rates due to noise uncertainty and multi-user interference. The multi-user detection-square-law combining (MUD-SLC) framework addresses these limitations by using MUD to separate user signals and SLC to combine energy from multiple antennas, significantly improving probability of detection (PD) while maintaining a low false alarm probability (Pfa). Simulation results show that the proposed approach achieves a PD of 0.81 at Pfa=0.15 and SNR=15 dB, outperforming conventional and advanced SS methods. Moreover, MUD-SLC demonstrates a considerable boost in detection performance, even in the presence of severe interference and noise uncertainty, leading to more reliable spectrum utilization in systems. The framework also maintains a lower Pfa, especially in dynamic wireless environments. This research work contributes to improving the efficiency and reliability of SS in CR networks.
Co-Authors Matad, Rohitha Ujjini Ramanaik, Premachand Doddamagadi