Neural-network-based end-to-end learning for adaptive optimization of two-dimensional signal generation in UVLC systems.

Visible light communication (VLC) benefits from the underwater blue-green window and holds immense potential for underwater wireless communication. In order to address the limitations of various equipment and harsh channel conditions in the underwater visible light communication (UVLC) system, the researchers proposed to use the method of autoencoder (AE) to tap the potential of the system. However, traditional AE schemes involve replacing the transmitting and receiving components of a communication system with a large multilayer perceptron (MLP) network, and they have significant drawbacks due to their reliance on a single network structure. In this paper, a novel 2D adaptive optimization autoencoder (2D-AOAE) framework is proposed to realize adaptive modulation and demodulation of two-dimensional signals. By implementing this scheme, we experimentally achieved a transmission rate of 2.85 Gbps over a 1.2-meter underwater VLC link. Compared to the traditional 32QAM UVLC system, the 2D-AOAE scheme demonstrated a 15.4% data rate increase. Moreover, the 2D-AOAE scheme exhibited a remarkable 73% improvement when compared to the UVLC system utilizing the traditional AE scheme. This significant enhancement highlights the superior performance and capabilities of the 2D-AOAE scheme in terms of transmission rate.

Feature decoupled knowledge distillation enabled lightweight image transmission through multimode fibers.

Multimode fibers (MMF) show tremendous potential in transmitting high-capacity spatial information. However, the quality of multimode transmission is quite sensitive to inherent scattering characteristics of MMF and almost inevitable external perturbations. Previous research has shown that deep learning may break through this limitation, while deep neural networks are intricately designed with huge computational complexity. In this study, we propose a novel feature decoupled knowledge distillation (KD) framework for lightweight image transmission through MMF. In this framework, the frequency-principle-inspired feature decoupled module significantly improves image transmission quality and the lightweight student model can reach the performance of the sophisticated teacher model through KD. This work represents the first effort, to the best of our knowledge, that successfully applies a KD-based framework for image transmission through scattering media. Experimental results demonstrate that even with up to 93.4% reduction in model computational complexity, we can still achieve averaged Structure Similarity Index Measure (SSIM) of 0.76, 0.85, and 0.90 in Fashion-MNIST, EMNIST, and MNIST images respectively, which are very close to the performance of cumbersome teacher models. This work dramatically reduces the complexity of high-fidelity image transmission through MMF and holds broad prospects for applications in resource-constrained environments and hardware implementations.

113Gbps rainbow visible light laser communication system based on 10λ laser WDM emitting module in fiber-free space-fiber link.

With the advent of the sixth-generation mobile communication standard (6 G), the visible light communication (VLC) technology based on wavelength division multiplexing (WDM) technology can effectively solve the problem of shortage of spectrum resources and insufficient channel capacity. This paper introduces one of our technical achievements, namely the construction of a near-real-time visible light laser communication (VLLC) system based on WDM, which includes a self-designed 10-λ fully-packaged visible light laser emission module, 1 m multimode fiber - 0.175 m free space - 1 m multimode fiber optical transmission link, and receiver array. In the transmitter system, we adopt adaptive discrete multitone (DMT) modulation technique combined with Quadrature Amplitude Modulation (QAM) modulation scheme to obtain maximum spectral efficiency (SE). In the receiving system, we utilize the sparse-structured reservoir computing post-equalization algorithm to achieve superior equalization performance on the basis of the traditional post-equalization algorithm. The experimental results indicate that this quasi-real-time communication system has achieved a signal transmission rate of 113.175Gbps. To the best of our knowledge, this work has set a record in the field of high-speed visible light laser communication. Therefore, the laser communication system constructed by this work, with its flexibility in deployment and high-speed performance, demonstrates the significant potential application of visible light laser communication in data center interconnection and high-speed indoor access networks.

Neural-network-based carrier-less amplitude phase modulated signal generation and end-to-end optimization for fiber-terahertz integrated communication system.

In fiber-terahertz integrated communication systems, nonlinear distortion and inter-symbol interference (ISI) will degrade transmission performance. Pre-compensation is an efficient method to handle the channel distortion as it can avoid noise boosting during channel compensation and reduce receiver side signal processing algorithmic complexity at user-end (UE) considering the asymmetric access scenario. In this paper, we propose and experimentally demonstrate a neural-network (NN)-based carrier-less amplitude phase (CAP) modulated signal generation and end-to-end optimization method for a fiber-terahertz integrated communication system. The CAP signal is generated directly from quadrature amplitude modulation symbols and pre-compensated through a transmitter NN, which allows the receiver to demodulate the signal with simple linear digital signal process (DSP). In generating the CAP signal, the NN based transmitter learns a group of filters, which can generate, up-convert, and pre-compensate the signals. Based on the proposed method, a fiber-terahertz integration access system at 220 GHz is demonstrated and a sensitivity gain of 1.2 dB is achieved at a transmission speed of 50 Gbps and the forward error correction (FEC) bit error rate (BER) threshold of 1 × 10-2 compared with the baseline after 10-km fiber transmission and 1-m wireless delivering.

Net-432-Gb/s/λ PS-PAM transmission based on advanced DSP and single DAC for 400†G/λ IM/DD links.

The escalating surge in datacenter traffic creates a pressing demand for augmenting the capacity of cost-effective intensity modulation and direct detection (IM/DD) systems. In this Letter, we report the demonstration of the single-lane 128-GBaud probabilistically shaped (PS)-PAM-20 IM/DD transmission using only a single digital-to-analog converter (DAC) for a net 400 G/λ system. Based on the advanced digital signal processing (DSP), we achieve net bitrates of up to 437 Gb/s for optical back-to-back and 432 Gb/s after the 0.5-km SSMF transmission in the C-band with 128-Gbaud PS-PAM-20 signals. This work is the latest demonstration on ultra-high-order PS-PAM signals achieving net bitrates exceeding 400 Gb/s despite symbol rate limitations. Notably, to the best of our knowledge, the realized net information rate ([net bitrate]/[symbol rate]) of 3.37 marks a new achievement within the domain of 400 G/λ IM/DD systems, with promising implications for enhancing bandwidth efficiency in the upcoming 1.6-Tb Ethernet scenario.

Wide field-of-view laser-based white light transmitter for visible light communications.

The advancement demands of high-speed wireless data link ask for higher requirements on visible light communication (VLC), where wide coverage stands as a critical criterion. Here, we present the design and implementation of a transmitter structure capable of emitting a high-power wide-coverage white light laser. This laser source exhibits excellent stability, with an irradiation range extending to a half-angle of 20°. Its high brightness satisfies the needs of indoor illumination while maintaining excellent communication performance. Utilizing bit-loading discrete multi-tone modulation, a peak data transmission rate of 3.24 Gbps has been achieved, spanning 1 to 5 m. Remarkably, the data rates exceed 2.5 Gbps within a 40° range at a distance of 5 m, enabling a long-distance, wide coverage, high-speed VLC link for future mobile network applications.

Multiple analyses of main flavor components in reconstituted tobacco and transfer behavior of their key substances during heating.

Reconstituted tobacco (RT) is a product made by reprocessing tobacco waste, experiencing a growing demand for heat-not-burn products. The purpose of this study is to analyze the main flavor ingredients in RT aerosol, as well as the transfer behavior of key flavor substances from substrates to aerosol and the concentrations of these compounds in the substrate after heating. First, we demonstrated that the odor of four RT aerosol samples could be distinguished using an electronic nose. Through non-targeted analysis, 93 volatile compounds were detected by gas chromatography-mass spectrometry, and 286 non/semi-volatile compounds were identified by ultra-high-performance liquid electrophoresis chromatography-mass spectrometry in aerosol. Furthermore, we found that the formation of RT aerosol involves primarily evaporation and distillation, however, the total content delivered from unheated RT samples to aerosol remains relatively low due to compound volatility and cigarette filtration. Thermal reactions during heating indicated the pyrolysis of chlorogenic acid to generate catechol and resorcinol, while Maillard reactions involving glucose and proline produced 2,3-dihydro-3,5-dihydroxy-6-methyl-4h-pyran-4-one. The study highlighted that heating RT at approximately 300°C could mitigate the production of harmful substances while still providing a familiar sensory experience with combusted tobacco.

Minimalist Deployment of Neural Network Equalizers in a Bandwidth-Limited Optical Wireless Communication System with Knowledge Distillation.

An equalizer based on a recurrent neural network (RNN), especially with a bidirectional gated recurrent unit (biGRU) structure, is a good choice to deal with nonlinear damage and inter-symbol interference (ISI) in optical communication systems because of its excellent performance in processing time series information. However, its recursive structure prevents the parallelization of the computation, resulting in a low equalization rate. In order to improve the speed without compromising the equalization performance, we propose a minimalist 1D convolutional neural network (CNN) equalizer, which is reconverted from a biGRU with knowledge distillation (KD). In this work, we applied KD to regression problems and explain how KD helps students learn from teachers in solving regression problems. In addition, we compared the biGRU, 1D-CNN after KD and 1D-CNN without KD in terms of Q-factor and equalization velocity. The experimental data showed that the Q-factor of the 1D-CNN increased by 1 dB after KD learning from the biGRU, and KD increased the RoP sensitivity of the 1D-CNN by 0.89 dB with the HD-FEC threshold of 1 × 10-3. At the same time, compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97% and the number of trainable parameters by 99.3%, with only a 0.5 dB Q-factor penalty. The results demonstrate that the proposed minimalist 1D-CNN equalizer holds significant promise for future practical deployments in optical wireless communication systems.

Laser-Based Mobile Visible Light Communication System.

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.

Modulation format recognition in a UVLC system based on reservoir computing with coordinate transformation and folding algorithm.

Modulation format recognition (MFR) is one of the key technologies in adaptive optical systems and widely used in both commercial and civil applications. With the rapid development of deep learning, MFR algorithm based on neural networks (NN) has achieved impressive success. Due to the high complexity of underwater channels, to gain better performance of MFR tasks in underwater visible light communication (UVLC), the NN tend to be designed with a complex structure, which is costly in computation and hinders fast allocation and real-time processing. In this paper, we propose a lightweight and efficient method based on reservoir computing (RC), whose trainable parameters are only 0.3% of common NN-based methods. To improve the performance of RC in MFR tasks, we propose powerful feature extraction algorithms including coordinate transformation and folding algorithm. The proposed RC-based methods are implemented for six modulation formats, including OOK, 4QAM, 8QAM-DIA, 8QAM-CIR, 16APSK, and 16QAM. The experimental results show that our RC-based methods take only a few seconds for training process and under different pin voltages of LED, the accuracy for almost all exceeds 90%, and the highest is close to 100%. Analysis on how to design a well-performed RC to strike a balance between accuracy and time cost is also investigated, providing a useful guide for RC implementations in MFR.

Wavefront shaping for multi-user line-of-sight and non-line-of-sight visible light communication.

Visible light communication (VLC) has become a popular method for indoor communication, due to its high transmission speed and robustness against electromagnetic interference. Indoor VLC scenarios commonly consist of multiple users in line-of-sight (LOS) and non-line-of-sight (NLOS) paths. In NLOS, the light waves suffer from attenuation due to diffuse reflection from obstacles in the room, leading to significant attenuation in light intensity. This paper proposes a wavefront shaping method to enhance indoor VLC for multiple users, including both LOS and NLOS. By employing a spatial division scheme, we can simultaneously achieve a light intensity gain of 5.43 dB in NLOS through stepwise partitioning wavefront shaping and an opening angle range of 27° for two LOS users through computational holography. By employing bit-power-loading discrete multi-tone (DMT) modulation, we demonstrated VLC with transmission data rates of 3.082 Gbit/s and 3.052 Gbit/s for two LOS links and 2.235 Gbit/s for NLOS with 30.7% improvement compared with that without wavefront shaping, satisfying the 7% forward-error-correction (FEC) threshold.

Deep-learning-based multi-user framework for end-to-end fiber-MMW communications.

Fiber-wireless integration has been widely studied as a key technology to support radio access networks in sixth-generation wireless communication, empowered by artificial intelligence. In this study, we propose and demonstrate a deep-learning-based end-to-end (E2E) multi-user communication framework for a fiber-mmWave (MMW) integrated system, where artificial neural networks (ANN) are trained and optimized as transmitters, ANN-based channel models (ACM), and receivers. By connecting the computation graphs of multiple transmitters and receivers, we jointly optimize the transmission of multiple users in the E2E framework to support multi-user access in one fiber-MMW channel. To ensure that the framework matches the fiber-MMW channel, we employ a two-step transfer learning technique to train the ACM. In a 46.2 Gbit/s 10-km fiber-MMW transmission experiment, compared with the single-carrier QAM, the E2E framework achieves over 3.5 dB receiver sensitivity gain in the single-user case and 1.5 dB gain in the three-user case under the 7% hard-decision forward error correction threshold.

Spatial pilot-aided fast-adapted framework for stable image transmission over long multi-mode fiber.

Multi-mode fiber (MMF) has emerged as a promising platform for spatial information transmission attributed to its high capacity. However, the scattering characteristic and time-varying nature of MMF pose challenges for long-term stable transmission. In this study, we propose a spatial pilot-aided learning framework for MMF image transmission, which effectively addresses these challenges and maintains accurate performance in practical applications. By inserting a few reference image frames into the transmitting image sequence and leveraging a fast-adapt network training scheme, our framework adaptively accommodates to the physical channel variations and enables online model update for continuous transmission. Experimented on 100 m length unstable MMFs, we demonstrate transmission accuracy exceeding 92% over hours, with pilot frame overhead around 2%. Our fast-adapt learning scheme requires training of less than 2% of network parameters and reduces the computation time by 70% compared to conventional tuning approaches. Additionally, we propose two pilot-insertion strategies and elaborately compare their applicability to a wide range of scenarios including continuous transmission, burst transmission and transmission after fiber re-plugging. The proposed spatial pilot-aided fast-adapt framework opens up the possibility for MMF spatial transmission in practical complicated applications.

Inverse design of an ultra-compact dual-band wavelength demultiplexing power splitter with detailed analysis of hyperparameters.

Inverse design has been widely studied as an efficient method to reduce footprint and improve performance for integrated silicon photonic (SiP) devices. In this study, we have used inverse design to develop a series of ultra-compact dual-band wavelength demultiplexing power splitters (WDPSs) that can simultaneously perform both wavelength demultiplexing and 1:1 optical power splitting. These WDPSs could facilitate the potential coexistence of dual-band passive optical networks (PONs). The design is performed on a standard silicon-on-insulator (SOI) platform using, what we believe to be, a novel two-step direct binary search (TS-DBS) method and the impact of different hyperparameters related to the physical structure and the optimization algorithm is analyzed in detail. Our inverse-designed WDPS with a minimum feature size of 130 nm achieves a 12.77-times reduction in footprint and a slight increase in performance compared with the forward-designed WDPS. We utilize the optimal combination of hyperparameters to design another WDPS with a minimum feature size reduced to 65 nm, which achieves ultra-low insertion losses of 0.36 dB and 0.37 dB and crosstalk values of -19.91 dB and -17.02 dB at wavelength channels of 1310 nm and 1550 nm, respectively. To the best of our knowledge, the hyperparameters of optimization-based inverse design are systematically discussed for the first time. Our work demonstrates that appropriate setting of hyperparameters greatly improves device performance, throwing light on the manipulation of hyperparameters for future inverse design.

8.8 Gbps PAM-4 visible light communication link using an external modulator and a neural network equalizer.

This Letter presents an experimental demonstration of a visible light communication system utilizing a LiNbO3 external modulator to support the transmission of pulse amplitude modulation (PAM)-4 signals. To solve the problem of the low-frequency fluctuations and inter-symbol interference (ISI) introduced by the external modulator-based system, a neural network with a low-frequency signal as the second label (LFNN) is proposed. A data rate of 8.8 Gbps using PAM-4 is experimentally achieved under the 7% hard-decision forward error correction (HD-FEC) bit-error-ratio (BER) limit of 3.8 × 10-3. To the best of our knowledge, this work represents the highest transmission data rate achieved thus far using external modulation in visible light communication systems.

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.

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.

Demonstration of photonics-based flexible integration of sensing and communication with adaptive waveforms for a W-band fiber-wireless integrated network.

The integration of sensing and communication (ISAC) in millimeter-waves (MMW) will play an important role in future 6G applications. Photonics-based radar sensing and communication systems have the advantages of high bandwidth in terms of high-resolution sensing and high-speed data transmission and can be inherently integrated with fiber-optic networks. To support flexible application scenarios, in this paper, we proposed and experimentally demonstrated an MMW photonics-based flexible ISAC system with adaptive signal waveforms for a W-band fiber-wireless integrated network. Photonics-based W-band ISAC signals are generated by heterodyning two free-running external cavity lasers. Microwave photonics-based radar signal processing supports centralized and seamless fiber-wireless communication and sensing networks. In our proposed system, orthogonal frequency-division multiplexing (OFDM) and linear frequency modulation (LFM) signals were combined by frequency-division multiplexing to share this bandwidth. Therefore, we can adaptively allocate bandwidths to OFDM and LFM signals according to the application requirements and realize a flexible ISAC system with high-speed communication and high-resolution radar sensing. As a proof-of-concept, a flexible W-band fiber-wireless ISAC system at 96.5 GHz over 10-km fiber transmission was demonstrated, achieving adaptive access rates from 5.98 to 41.48 Gbit/s after transmission over 1-m free space, and adaptive sensing resolutions from 1.53 to 6.94 cm with the distance error after calibration less than 4 cm. The performance of both communication and sensing under different bandwidth ratios was also studied.

Deep learning based end-to-end visible light communication with an in-band channel modeling strategy.

Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED-VLC systems. We present a deep learning-based autoencoder to deal with this challenge. A novel modeling strategy is proposed to bypass the influence of the LFN and other low signal-to-noise ratio data when training the channel model of our E2E framework. The deep learning-based autoencoder then embeds the differentiable channel model and learns to combat the majority of channel impairments. In the E2E LED-VLC experiment, 1.875 Gbps transmission is achieved under the 7% HD-FEC threshold, 0.325 Gbps faster than the baseline. The E2E framework is robust to signal bias and amplitude variations, implying dimming support in the indoor environment.

46.4 Gbps visible light communication system utilizing a compact tricolor laser transmitter.

Visible light communication (VLC), combining wireless communication with white lighting, has many advantages. It is free of electromagnetic interference, is rich in spectrum resources, and has a gigabit-per-second (Gbps) data rate. Laser diodes (LDs) are emerging as promising light sources for high-speed VLC communication due to their high modulation bandwidth. In this paper, we demonstrate a red/green/blue (R/G/B) LDs based VLC system with a recorded data rate of 46.41 Gbps, employing discrete multitone (DMT) and adaptive bit-loading technology to achieve high spectral efficiency (SE). The emission characteristics and transmission performance of R/G/B-LDs are discussed. The optimal data rates of R/G/B-LDs channels are 17.168/14.652/14.590 Gbps, respectively. The bit-error-ratio (BER) of each channel satisfies the 7% forward-error-correction (FEC) threshold (3.8×10-3) and greatly approaches the channel Shannon limit.

Signal recovery in optical wireless communication using photonic convolutional processor.

Deep neural networks (DNNs) have been applied to recover signals in optical communication systems and have shown competence of mitigating linear and nonlinear distortions. However, as the data throughput increases, the heavy computational cost of DNNs impedes them from rapid and power-efficient processing. In this paper, we propose an optical communication signal recovery technology based on a photonic convolutional processor, which is realized by dispersion delay unit and wavelength division multiplexing. Based on the photonic convolutional processor, we implement an optoelectronic convolutional neural network (OECNN) for signal post-equalization and experimentally demonstrate on 16QAM and 32QAM of an optical wireless communication system. With system parameters optimization, we verify that the OECNN can achieve accurate signal recovery where the bit error ratio (BER) is below the 7% forward error correction threshold of 3.8×10-3 at 2Gbps. With adding the OECNN-based nonlinear compensation, compared with only linear compensation, we improve the quality (Q) factor by 3.35 dB at 16QAM and 3.30 dB at 32QAM, which is comparable to that of an electronic neural network. This work proves that the photonic implementation of DNN is promising to provide a fast and power-efficient solution for optical communication signal processing.