OFDM Modulation Classification Using Cross-SKNet With Blind IQ Imbalance and Carrier Frequency Offset Compensation

Automatic modulation classification (AMC) plays a vital role in military and civilian applications, which identifies the modulation schemes of received signals before signal demodulation. Orthogonal frequency division multiplexing (OFDM) technology against the frequency-selective fading channel is widely applied in fourth generation (4G) and fifth generation (5G) communication systems, which may suffer from various hardware impairments, including in-phase and quadrature (IQ) imbalance and carrier frequency offset (CFO). This paper proposes an OFDM modulation classification framework, which incorporates the initial blind IQ imbalance and CFO compensation modules and the cross-selective kernel network (Cross-SKNet). Firstly, a small segment of OFDM signals is utilized for consecutive IQ imbalance and CFO compensation modules to estimate compensation coefficients and reconstruct the received burst OFDM symbols. Subsequently, the Cross-SKNet is initiated using the reconstructed signals. The network comprises the consecutive Cross-Down sampling (Cross-DS) module, the Cross-selective kernel (Cross-SK) module, and the channel and spatial attention classifier, which integrate asymmetric convolution to extract intra-symbol and inter-symbol correlation with reduced computational complexity and leverage selective kernel technology to adaptively learn the receptive field. The proposed framework can identify four different OFDM modulation formats and enhance performance by IQ imbalance and CFO compensation with a fraction of additional computation. Simulation results demonstrate that the proposed Cross-SKNet outperforms the state-of-the-art method across a range of signal-to-noise ratio (SNR) levels and exhibits superior generalization capabilities at low SNR conditions. Based on the simulation results of the generated dataset, the Cross-SKNet achieves a classification accuracy of over
$\bm {95\%}$
at SNR
$\bm {>8}$
dB. Moreover, the incorporation of IQ imbalance and CFO compensation modules enhances the performance of the Cross-SKNet, which can obtain an accuracy gain of
$\bm {8\%}$
at low SNR condition of
$\bm {-10}$
dB.