Neural-network-based direct waveform to symbol conversion for joint ISI and ICI cancellation in non-orthogonal multi-band CAP based UDWDM fiber-mmWave integration

Abstract

The six-generation mobile network (6G) based on millimeter-wave (mmWave) is expected to deliver more capacity and higher connection density compared with 5G. We demonstrate an ultra-dense wavelength division multiplexing (UDWDM) fiber-mmWave integration network based on non-orthogonal multiband carrier less amplitude and phase (NM-CAP) modulation to address the needs for dense access cells, high-spectral efficiency, and high data rate. We demonstrate a neural-network-based waveform to symbol converter (NNWSC), which can directly convert the received NM-CAP waveform into quadrature amplitude modulation (QAM) symbols to simultaneously handle the inter-symbol interference (ISI) and inter-channel interference (ICI), without the need for conventional matched filters and additional ISI and ICI equalizers. Experimental results show that this method is also effective for QAM constellations with probabilistic shaping. Since NNWSC simplifies the demodulation process of NM-CAP and avoids error accumulation caused by cascading filters and post-equalizers, NNWSC can reduce the computational complexity and provide good performance. Compared with the regular receiver with cascaded least mean square equalizer, matched filters, and ICI equalizer, NNWSC can reduce the computational complexity by 93%. The demonstrated spectrally efficient fiber-mmWave transmission is achieved at a total 414-Gbps net data rate with 24 PS-QAM NM-CAP sub-bands on 8 UDWDM channels with 25-GHz spacing.