Ideeprx Enabled 100 Gb/s DFT-s-OFDM Data Transmission over 220 GHz Testbed

Deep learning (DL) based receiver (DeepRx) has been proven to be able to greatly improve the data transmission performance compared to conventional OFDM (orthogonal frequency-division multiplexing) receivers. In order to accommodate DFT-s-OFDM (discrete Fourier transform-spread OFDM), which is a widely used single carrier waveform for uplink transmission characterized with low peak to average power ratio (PAPR), the internal structure of DeepRx needs to be redesigned. In this paper, we propose a novel deep neural network based receiver, referred to as IDFT-deprecoding embedded deep receiver (iDeepRx) customized for DFT-s-OFDM. By embedding an untrainable functionality of IDFT-deprecoding between two trainable neural network structures, the proposed iDeepRx can support DFT-s-OFDM transmission and mitigate implicit link and hardware-induced channel impairments. We demonstrate a single-layer 100 Gb/s error-free data transmission using iDeepRx over 20 GHz bandwidth at 220 GHz carrier frequency and observe 2 dB+ relative performance gain over the conventional receiver. Moreover, intermediate output of iDeepRx has been extracted with constellation-like patterns for visualization and performance monitoring during training and inference, which helps in making the internal working mechanism of iDeepRx more explainable.