IEEE Citation Examples

This paper presents a novel approach to spectrum sensing in cognitive radio networks. Building on recent advances in deep learning [1], [2], we propose a convolutional neural network architecture that achieves 95% detection accuracy while maintaining low computational complexity. Experimental results demonstrate superior performance compared to existing methods [3]-[5], with potential applications in 5G and beyond wireless systems.

I. INTRODUCTION

Cognitive radio technology has emerged as a key enabler for dynamic spectrum access [6]. Early work by Mitola [7] established the theoretical foundation, while subsequent research [8]-[12] has addressed practical implementation challenges. Recent surveys [13], [14] highlight the importance of spectrum sensing algorithms in ensuring reliable performance.

Machine learning approaches have shown promise in this domain [15]. Convolutional neural networks, originally developed for image recognition [16], have been successfully adapted for signal processing tasks [17], [18]. This paper extends these concepts to cognitive radio applications, proposing a novel architecture that addresses the specific requirements of spectrum sensing.

III. PROPOSED METHODOLOGY

The proposed CNN architecture follows the design principles outlined in [19] while incorporating domain-specific modifications. The network consists of three convolutional layers, similar to the structure proposed in [20], but with modified filter sizes optimized for spectral data.

Training data generation follows the approach described in [21, pp. 145-167]. The software-defined radio platform is configured according to GNU Radio specifications [22], with additional custom blocks implemented in Python [23]. Signal preprocessing utilizes the techniques presented in [24], ensuring compatibility with real-world scenarios.