End-to-End Learning-Based Full-Duplex Amplify-and-Forward Relay Networks

Full duplex (FD) relaying can provide double spectral efficiency. Despite advanced self-interference cancellation techniques, residual self-interference (RSI) limits the performance significantly. We present an autoencoder (AE)-based block coded modulation (BCM) and differential BCM (d-BCM) for an FD amplify-and-forward (FD-AF) relay network that can tackle the deteriorating impacts of RSI. Existing works treat AE frameworks as black-box with minimal/no focus on training convergence, limiting AE’s practical deployment. Focussing on training convergence, firstly, we show that training of AE converges above minimum signal-to-noise-ratio (SNR) and below maximum RSI level, and CSI helps in faster convergence. Secondly, we establish a relationship between training hyper-parameters and AE-based BCM/d-BCM design, by showing that, for any given hyper-parameters, training of the AE has converged to its maximum potential of decoding if AE’s encoder has designed $2^{k}$ codewords, with an emphasis on the minimum required training samples. To open the black-box AE, we reveal five observations in the AE-based designed codewords concerning Euclidean distance, packing density, hamming distance, and kurtosis, that resemble the desired observations of theoretical random coded modulations. By extensive simulations, we show that the proposed AE outperforms the conventional methods considerably for varying SNR, RSI, transmission rate, channel estimation errors, and small/practical block lengths.