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.

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.

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.

Fast hyperspectral single-pixel imaging via frequency-division multiplexed illumination.

Hyperspectral imaging that detects 3D spectra-spatial information has been used in a wide range of applications. Among reported techniques, multiplexed spectral imaging with a single-pixel detector provides as a photon-efficient and low-cost implementation; however, the previous spectral modulation schemes are mostly complicated and sacrifice the imaging speed. Here, we propose a fast and compact hyperspectral single-pixel imaging technique based on programmable chromatic illumination. A multi-wavelength LED array modulated by independent carriers achieves stable and accurate spectral modulation up to MHz in a frequency-division multiplexed manner, hence allowing the full use of the spatial light modulation speed. Additionally, we propose a multi-channel deep convolutional autoencoder network to reconstruct hyperspectral data from highly-compressed 1D measurement. Experimental reconstructions of 12 spectral channels and 64 × 64 pixels are demonstrated for dynamic imaging at 12 fps image rate. The proposed imaging scheme is highly extensible to a wide spectrum range, and holds potential for portable spectral imagers in low-light or scattering applications.

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.

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.

Hybrid frequency domain aided temporal convolutional neural network with low network complexity utilized in UVLC system.

Deep neural network has been used to compensate the nonlinear distortion in the field of underwater visible light communication (UVLC) system. Considering the tradeoff between the equalization performance and the network complexity is the priority in practical applications. In this paper, we propose a novel hybrid frequency domain aided temporal convolutional neural network (TFCNN) with attention scheme as the post-equalizer in CAP modulated UVLC system. Experiments illustrate that the proposed TFCNN can achieve better equalization performance and remain the bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8×10-3 when other equalizers loss effectiveness under serious distortion condition. Compared with the standard deep neural network, TFCNN shows 76.4% network parameters complexity reduction.