Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter
Photonic millimeter-wave communication systems are promising for high-capacity, high-speed wireless networks, and their production is driven by the growing demand from data-intensive applications. However, challenges such as inter-symbol interferences (ISIs), inter-band interferences (IBIs), symbol...
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MDPI AG
2025-03-01
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| Series: | Photonics |
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| author | Kexin Liu Boyu Dong Zhongya Li Yinjun Liu Yaxuan Li Fangbing Wu Yongzhu Hu Junwen Zhang |
| author_facet | Kexin Liu Boyu Dong Zhongya Li Yinjun Liu Yaxuan Li Fangbing Wu Yongzhu Hu Junwen Zhang |
| author_sort | Kexin Liu |
| collection | DOAJ |
| description | Photonic millimeter-wave communication systems are promising for high-capacity, high-speed wireless networks, and their production is driven by the growing demand from data-intensive applications. However, challenges such as inter-symbol interferences (ISIs), inter-band interferences (IBIs), symbol timing offsets (STOs), and nonlinearity impairments exist, especially in non-orthogonal multiband configurations. This paper proposes and demonstrates the neural network-based waveform-to-symbol converter (NNWSC) for a coordinated multi-input and single-output (MISO) photonic millimeter-wave system with multiband multiplexing. The NNWSC replaces conventional matched filtering, down-sampling, and equalization, simplifying the receiver and enhancing interference resilience. Additionally, it reduces computational complexity, improving operational feasibility. As a proof of concept, experiments are conducted in a 16QAM non-orthogonal multiband carrierless amplitude and phase (NM-CAP) modulation system with coordinated MISO configurations in a scenario where two base stations have 5 km and 10 km fiber links, respectively. Data were collected across various roll-off factors, sub-band spacings, and received optical power (ROP) levels. Based on the proposed method, a coordinated MISO photonic millimeter-wave (mmWave) communication system at 91.9 GHz is demonstrated at a transmission speed of 30 Gbps. The results show that the NNWSC-based receiver achieves significant bit error rate (BER) reductions compared to conventional receivers across all configurations. The tolerances to the STO of NNWSC are also studied. These findings highlight NNWSC integration as a promising solution for high-frequency, interference-prone environments, with potential improvements for low-SNR and dynamic STO scenarios. |
| format | Article |
| id | doaj-art-63c0e01c3ce544888bc60cb7df7d7eae |
| institution | DOAJ |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Photonics |
| spelling | doaj-art-63c0e01c3ce544888bc60cb7df7d7eae2025-08-20T02:42:28ZengMDPI AGPhotonics2304-67322025-03-0112324810.3390/photonics12030248Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol ConverterKexin Liu0Boyu Dong1Zhongya Li2Yinjun Liu3Yaxuan Li4Fangbing Wu5Yongzhu Hu6Junwen Zhang7Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaPhotonic millimeter-wave communication systems are promising for high-capacity, high-speed wireless networks, and their production is driven by the growing demand from data-intensive applications. However, challenges such as inter-symbol interferences (ISIs), inter-band interferences (IBIs), symbol timing offsets (STOs), and nonlinearity impairments exist, especially in non-orthogonal multiband configurations. This paper proposes and demonstrates the neural network-based waveform-to-symbol converter (NNWSC) for a coordinated multi-input and single-output (MISO) photonic millimeter-wave system with multiband multiplexing. The NNWSC replaces conventional matched filtering, down-sampling, and equalization, simplifying the receiver and enhancing interference resilience. Additionally, it reduces computational complexity, improving operational feasibility. As a proof of concept, experiments are conducted in a 16QAM non-orthogonal multiband carrierless amplitude and phase (NM-CAP) modulation system with coordinated MISO configurations in a scenario where two base stations have 5 km and 10 km fiber links, respectively. Data were collected across various roll-off factors, sub-band spacings, and received optical power (ROP) levels. Based on the proposed method, a coordinated MISO photonic millimeter-wave (mmWave) communication system at 91.9 GHz is demonstrated at a transmission speed of 30 Gbps. The results show that the NNWSC-based receiver achieves significant bit error rate (BER) reductions compared to conventional receivers across all configurations. The tolerances to the STO of NNWSC are also studied. These findings highlight NNWSC integration as a promising solution for high-frequency, interference-prone environments, with potential improvements for low-SNR and dynamic STO scenarios.https://www.mdpi.com/2304-6732/12/3/248photonic mmWave communication systemneural network (NN)waveform-to-symbol converter |
| spellingShingle | Kexin Liu Boyu Dong Zhongya Li Yinjun Liu Yaxuan Li Fangbing Wu Yongzhu Hu Junwen Zhang Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter Photonics photonic mmWave communication system neural network (NN) waveform-to-symbol converter |
| title | Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter |
| title_full | Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter |
| title_fullStr | Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter |
| title_full_unstemmed | Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter |
| title_short | Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter |
| title_sort | coordinated multi input and single output photonic millimeter wave communication in w band using neural network based waveform to symbol converter |
| topic | photonic mmWave communication system neural network (NN) waveform-to-symbol converter |
| url | https://www.mdpi.com/2304-6732/12/3/248 |
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