Nonlinear Digital Self-Interference Cancellation in In-Band Full-Duplex Systems with Complex-Valued Temporal Convolution

In-band full-duplex (FD) technology has emerged as a promising solution to the growing demand for higher spectrum efficiency. By enabling simultaneous transmission and reception at the same frequency, FD-based integrated sensing and communication (ISAC) architectures offer potential advantages in capacity, latency, and spectral efficiency. However, non-ideal hardware in transceivers introduces nonlinear self-interference (SI), which limits the practical deployment of FD systems. Conventional polynomial models are widely used to characterize nonlinear SI, but their computational complexity grows quadratically with the nonlinear order. To address these challenges, a complex-valued temporal convolutional network (CV-TCN) with wavelet activation function is proposed for nonlinear SI modeling in this paper. The CV-TCN canceller achieves stronger nonlinear SI cancellation (SIC) performance with lower inference complexity. Simulation results demonstrate that the proposed model surpasses traditional polynomial-based and other cancellers in nonlinear SIC performance with higher parameter efficiency. Moreover, the CV-TCN canceller suppresses the residual SI power by around 7.95 dB after linear SIC, leaving the residual SI 2.23 dB above the noise floor.