Xin Lu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xin Lu has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Jianjun Yu
Fudan University
2 shared publications - Hansong Ma
State Key Laboratory of ASIC and System, Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication - Hanyu Zhang
State Key Laboratory of ASIC and System, Key Laboratory for Information Science of Electromagnetic Waves (MoE), The Department of Communication Science and Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication - Jiali Chen
State Key Laboratory of ASIC and System, Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication - Jie Zhang
State Key Laboratory of ASIC and System, Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication - Sicong Xu
State Key Laboratory of ASIC and System, Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Information Science and Technology, Fudan University, Shanghai 200433, China
1 shared publication
Latest Publications
- Open Access
Broadband Millimeter-Wave Communication Under Water
Underwater wireless communication requires flexible solutions that balance transmission range, data rate, and latency, particularly for short-range data exchange. In this work, we experimentally investigate a photonics-assisted 85-GHz millimeter-wave transmission system in a controlled water-loaded configuration. The transmitter and receiver are separated by 3 m, including a 2.3 m free-space section and a 0.7 m interchangeable test section. The received power of a 100 MHz pilot is measured under three configurations: unobstructed air, an empty plastic tank, and a water-filled tank. Relative to the air reference, the empty tank introduces a 4.1 dB received-power penalty, while filling the tank with water produces a further 15.3 dB reduction, resulting in a total penalty of 19.4 dB. Broadband transmission is further evaluated using 10 Gbaud single-carrier QPSK and probabilistically shaped 16QAM (PS-16QAM) signals under the water-filled configuration. Both modulation formats achieve pre-FEC bit-error rates (BER) below the reference 20%-overhead soft-decision FEC threshold at suitable optical input powers. These results provide quantitative experimental insights into broadband W-band signal transmission and recovery in water-loaded environments, supporting further investigation of high-capacity, proximity-oriented wireless communication systems.
Open AccessBroadband Millimeter-Wave Communication Under Water
Underwater wireless communication requires flexible solutions that balance transmission range, data rate, and latency, particularly for short-range data exchange. In this work, we experimentally investigate a photonics-assisted 85-GHz millimeter-wave transmission system in a controlled water-loaded configuration. The transmitter and receiver are separated by 3 m, including a 2.3 m free-space section and a 0.7 m interchangeable test section. The received power of a 100 MHz pilot is measured under three configurations: unobstructed air, an empty plastic tank, and a water-filled tank. Relative to the air reference, the empty tank introduces a 4.1 dB received-power penalty, while filling the tank with water produces a further 15.3 dB reduction, resulting in a total penalty of 19.4 dB. Broadband transmission is further evaluated using 10 Gbaud single-carrier QPSK and probabilistically shaped 16QAM (PS-16QAM) signals under the water-filled configuration. Both modulation formats achieve pre-FEC bit-error rates (BER) below the reference 20%-overhead soft-decision FEC threshold at suitable optical input powers. These results provide quantitative experimental insights into broadband W-band signal transmission and recovery in water-loaded environments, supporting further investigation of high-capacity, proximity-oriented wireless communication systems.
- Open Access
Frequency-Offset-Estimation-Assisted Transformer Neural Equalization for a 4.6 km Optical-Heterodyne RoF–Wireless OFDM Link
To address the issues of subcarrier orthogonality loss and inter-carrier interference (ICI) caused by carrier frequency offset (CFO), this paper proposes and experimentally validates a frequency offset estimation (FOE)-assisted dual-domain Transformer equalizer within an advanced, high-capacity optical-heterodyne radio-over-fiber (RoF)–wireless orthogonal frequency division multiplexing (OFDM) transmission system. To rigorously test the algorithm’s robustness under extreme physical conditions, the experimental platform integrates offline 16-GBaud signal generation, optical I/Q modulation, dual-optical-tone transport over a single-mode-fiber RoF feeder, remote photonic heterodyne frequency conversion based on a uni-traveling-carrier photodiode (UTC-PD), 4.6 km free-space wireless transmission, and 160-GSa/s ultra-high-speed real-time sampling. In this system, the receiver front-end employs an FOE module to pre-compensate for the dominant global CFO-induced phase rotation; subsequently, a low-complexity, compact local-window Transformer is utilized to perform adaptive residual compensation for local data-dependent impairments—such as residual waveform distortion and residual ICI—in both the time and frequency domains (before and after the Fast Fourier Transform, or FFT). This synergistic architecture, combining a physical model-driven approach with a self-attention mechanism, effectively mitigates the adverse impact of global frequency offset on neural network convergence. Experimental results demonstrate that, under conditions of strictly aligned multiply accumulate (MAC) operation complexity, the dual-domain architecture achieves significantly superior performance—in terms of bit error rate (BER), error vector magnitude (EVM), and constellation quality—compared to traditional linear DSP methods and baseline networks such as DNNs, CNNs, and LSTMs. Operating in 16 GBaud QPSK mode with an input optical power of 0 dBm, the system achieves a BER of , representing performance improvements of approximately 5.98-fold and 1.92-fold over the standalone Transformer and FOE-assisted DNN schemes, respectively.Open AccessFrequency-Offset-Estimation-Assisted Transformer Neural Equalization for a 4.6 km Optical-Heterodyne RoF–Wireless OFDM Link
To address the issues of subcarrier orthogonality loss and inter-carrier interference (ICI) caused by carrier frequency offset (CFO), this paper proposes and experimentally validates a frequency offset estimation (FOE)-assisted dual-domain Transformer equalizer within an advanced, high-capacity optical-heterodyne radio-over-fiber (RoF)–wireless orthogonal frequency division multiplexing (OFDM) transmission system. To rigorously test the algorithm’s robustness under extreme physical conditions, the experimental platform integrates offline 16-GBaud signal generation, optical I/Q modulation, dual-optical-tone transport over a single-mode-fiber RoF feeder, remote photonic heterodyne frequency conversion based on a uni-traveling-carrier photodiode (UTC-PD), 4.6 km free-space wireless transmission, and 160-GSa/s ultra-high-speed real-time sampling. In this system, the receiver front-end employs an FOE module to pre-compensate for the dominant global CFO-induced phase rotation; subsequently, a low-complexity, compact local-window Transformer is utilized to perform adaptive residual compensation for local data-dependent impairments—such as residual waveform distortion and residual ICI—in both the time and frequency domains (before and after the Fast Fourier Transform, or FFT). This synergistic architecture, combining a physical model-driven approach with a self-attention mechanism, effectively mitigates the adverse impact of global frequency offset on neural network convergence. Experimental results demonstrate that, under conditions of strictly aligned multiply accumulate (MAC) operation complexity, the dual-domain architecture achieves significantly superior performance—in terms of bit error rate (BER), error vector magnitude (EVM), and constellation quality—compared to traditional linear DSP methods and baseline networks such as DNNs, CNNs, and LSTMs. Operating in 16 GBaud QPSK mode with an input optical power of 0 dBm, the system achieves a BER of , representing performance improvements of approximately 5.98-fold and 1.92-fold over the standalone Transformer and FOE-assisted DNN schemes, respectively.