Spectrum Sensing Technique for OFDM Signals
Han-Seok Bae, Young‐Sik Kim, Chang Heon Lim, Chang‐Joo Kim, Yun Suk Cho · The Journal of Korean Institute of Electromagnetic Engineering and Science · 2021
This paper presents a machine learning (ML) algorithm to detect orthogonal frequency-division multiplexing (OFDM) signals. Spectrum sensing is a key technology in cognitive radio communication, which enhances spectral efficiency. Recently, signals transmitted and received by wireless communication systems have been based on OFDM. These signals contain a pilot signal for channel calibration, which can be detected using a spectral correlation function (SCF). In this process, the FAM algorithm is applied for efficient SCF operation herein. The existence of OFDM signals is determined through a convolutional neural network-based ML algorithm using the SCF as input data. The learning data for ML use the SCF values for OFDM signals with signal-to-noise ratios (SNRs) of −20 to 0 dB. Consequently, on evaluation of the post-learning optimized neural network performance, signals were detected with a probability of 0.9 at the condition of 0.08 false alarm probability for reception signals with SNRs of −12 dB.