End-to-End Deep-Learning-Based Photonic-Assisted Multi-User Fiber-mmWave Integrated Communication System

The integration of fiber and millimeter-wave (MMW) technology offers a promising solution for next-generation (6G) communication. However, the experimental investigation of multi-user fiber-MMW integrated communication with diverse channels is yet to be explored, as the complex and dynamic nature of this system makes it challenging to overcome the channel distortion. In this paper, we propose and demonstrate an adaptive multi-user end-to-end framework (AMEF) for symbol-to-symbol optimization of a multi-user fiber-MMW integrated system. This framework leverages a two-tributary heterogeneous neural network (TTHnet) based multi-channel model (MCM) and a multi-user transceiver (MUT) composed of a multi-user encoder (MUE) and a multi-user decoder (MUD). The multi-layer perceptron (MLP)-based MUE and MUD are jointly optimized as an auto-encoder, facilitated by the well-trained MCM, which enables gradient back-propagation and end-to-end optimization. The weights of different users are adaptively allocated by the AMEF to ensure a balanced transmission performance. We experimentally demonstrate the effectiveness of the AMEF through a 10 km fiber-MMW integrated system for two-user communication. With the proposed method, we realize a 66 Gbps wireless-only transmission and a 49.5 Gbps fiber-wireless transmission under a soft decision-forward error correction (SD-FEC) threshold. Compared to the conventional multiband carrierless amplitude and phase (mCAP) modulation scheme, our proposed AMEF achieves significant receiver sensitivity gains exceeding 1.1 dB and 0.6 dB for the wireless-only and integrated systems, respectively. These results prove that the AMEF can provide a robust and adaptive solution for achieving high-speed and multi-user communication in next-generation radio access networks.