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

Aolong Sun,Zhongya Li,Junlian Jia,Boyu Dong,Sizhe Xing,Guoqiang Li,Changle Huang,An Yan,Penghao Luo,Jianyang Shi,Nan Chi,Junwen Zhang

JOURNAL OF LIGHTWAVE TECHNOLOGY（2024）

Fudan Univ

Cited 4|Views7

Abstract

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.

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Key words

Optical fiber networks,Optical fibers,6G mobile communication,Communication systems,Photonics,Optimization,Optical receivers,Deep neural network,end-to-end learning,fiber-wireless,millimeter wave,photonic-assisted

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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