Semantic communication (SC) systems face the challenge of completing generation and comprehension tasks simultaneously under high data compression. To address this issue, this paper proposes a You Only Transmit Once (YOTO) SC system, which achieves efficient performance by synergistically integrating images and texts. For generation tasks, YOTO integrates a multiple granularity visual encoder, a digital modulation autoencoder, and a conditional probability-based visual decoder. These parts collectively ensure high-fidelity image reconstruction and accurate object detection. Meanwhile, YOTO utilizes the Source Feature Selection (SFS) and Channel Feature Selection (CFS) module for joint compression of source and channel coding data. To achieve a globally optimal compression, the Libra module dynamically adjusts feature retention ratios between CFS and SFS. Regarding comprehension tasks, YOTO eliminates the need for additional text data transmission by simply replacing the visual decoder with a Multimodal Language Model that incorporates the visual causality and bridging module. This architecture effectively converts demodulated visual data into descriptive captions while enabling emotion-aware text generation through prompt engineering. Extensive simulation experiments demonstrate the superior performance of YOTO in diverse channel conditions. In generation tasks, the YOTO system achieves PSNR values exceeding 30 dB both in the AWGN and Rayleigh fading channels when the SNR is 6 dB. While in comprehension tasks, it attains sentence similarity scores above 0.83 at 10 dB SNR. YOTO system effectively resolves cross-modal semantic disparities, thereby paving the way for future developments of SC systems that can balance generation and comprehension tasks.

Infrared-visible image fusion (IVF) aims to integrate complementary information from infrared and visible sensors into a single, more informative representation. However, achieving both visual clarity and semantic consistency in the fused results remains a critical challenge, particularly for real-world applications like scene understanding. To address this, we propose DICFusion, a deep integrated and semantic-coordinated network, tailored for perceptually and semantically enriched infrared-visible fusion tasks. Firstly, the DICFusion employs a novel modality-aware fusion strategy to integrate infrared and visible modalities into a cohesive feature embedding. Secondly, the framework incorporates hybrid mamba-convolution blocks, which leverage the combined strengths of mamba and convolution neural networks to accurately capture both global context and localized details while maintaining computational efficiency. To mitigate the feature heterogeneity between fusion and downstream tasks, DICFusion adopts a comprehensive framework of deep integration and collaborative optimization. This design utilizes a unified multi-scale encoder to harmonize feature representations, followed by parallel fusion and segmentation branches to enhance both visual quality and task performance. Moreover, a semantic guidance module leveraging cross-attention mechanism is incorporated to refine the semantic consistency of the fused outcomes. Comprehensive experimental evaluations validate the performance and efficiency of DICFusion, demonstrating its superiority over contemporary state-of-the-art methods, both in terms of fusion visual quality and downstream task precision. The code is available at https://github.com/fd-qhwang/DICFusion.

High-altitude and low Earth orbit (LEO) platforms assisted free-space optical (FSO) communication systems have emerged as a promising solution to facilitate the development of future integrated space-air-ground networks, owing to their expansive coverage, high bandwidth, unlicensed spectrum, and large-capacity. However, these systems are highly susceptible to the adverse effects of atmospheric turbulence, degrading overall system performance and posing a risk of signal outages. In this paper, we propose a novel semantic communication paradigm, termed prior probability-based vector quantized autoencoder (PVQAE), to enable uninterrupted and efficient image transmission in free-space optical communication systems under turbulent conditions. Specifically, it utilizes a neural network-based encoder-decoder architecture for semantic information extraction and image reconstruction, and achieves uninterrupted transmission of semantic information in turbulent channels via vector beams shift keying without the need of any adaptive optics for beam compensation. A shared discrete codebook, designed for both the transmitter and receiver, facilitates latent feature representation and enables the quantization of semantic information for light-field shift keying transmission. Moreover, a power differential detection (PDD) algorithm is derived to reduce the complexity associated with receiver-side detection. Simulation results demonstrate the superior performance of PVQAE over benchmark schemes, such as conventional methods using JPEG for image source coding and Reed-Solomon (RS) for channel coding, in terms of structural similarity index (SSIM) and compression ratio (CR) product, and classification accuracy. These results highlight the effectiveness of the proposed framework in achieving robust and efficient transmission without interruption in turbulent channels.

The next generation of mobile communication puts forward higher requirements for communication rate and capacity. However, the current congested spectrum resources also make the expansion of higher frequency band communication more urgent. Visible light communication (VLC) has nearly 400 THz of spectrum resources and the potential for high-speed communication. To expand communication rate, it is necessary to explore multiplexing technologies of higher dimensions. This paper constructed a 4-wavelength wavelength division multiplexing (WDM) system based on commercial and homemade red and green lasers. To obtain a high beam quality for mode control, we utilized a lithium niobate (LiNbO3) external modulator to perform intensity modulation on the output beam of a laser driven by a stable bias current, and based on laser mode converters (LMCs), obtained 4 transverse electromagnetic modes (TEMs) for mode division multiplexing (MDM). Finally, based on these 16 channels, a total transmission rate of 269.37 Gbps was achieved in 1m of free space.

The advancement of current data-intensive services imposes increasingly demands on next-generation mobile communication systems. Visible light communication (VLC) offers significant potential for the development of next-generation communication networks. It has an abundant spectrum resource of up to 400 THz, exhibits immunity to electromagnetic interference, and its blue-green band coincides with the underwater transmission window, enabling high-speed communication with high signal-to-noise ratio (SNR). In this paper, we demonstrated a 532 nm visible light coherent communication (VLCC) system based on a lithium niobate phase modulator, and root raised-cosine (RRC) pulse shaped binary phase-shift keying (BPSK) and coherent detection scheme. Compared to intensity modulation with direct detection schemes, our system mitigates signal distortion induced by frequency chirp and relaxes the demand for high sensitivity at the receiver photodetector. Finally, we successfully achieved 12 Gbaud BPSK signal transmission. To the best of our knowledge, this represents the highest reported data rate for blue-green band visible light coherent communication to date.

Coherent detection has emerged as a key technology for advancing passive optical networks (PON) beyond 100 Gbps per wavelength, due to its advantages over the intensity and direct-detection (IM/DD) approach, which was previously dominant in PON systems. In coherent PON architectures, the cost of the optical network unit (ONU) is substantial due to the complexity of its transceivers. Consequently, numerous studies have focused on simplifying the transmitters and receivers on the ONU side. Yet, simplifying the ONU transceiver introduces issues like reduced dynamic range and lower data rates. How to make a balance between the performance and the cost, particularly in terms of component complexity, represents a critical challenge for the advancement of coherent PON. Therefore, we propose and demonstrate a hybrid, multi-format, and flexible-rate bidirectional coherent PON system, supporting a compatible OLT and ONUs with different types of transceivers. ONUs are categorized into different tiers based on their performance and cost requirements, with transceivers of varying complexity allocated accordingly. As a demonstration of concept, we have successfully conducted experimental transmissions of 25-GBaud 4/16/64- quadrature-amplitude-modulation signals across 20-km fiber in a bidirectional setup, achieving data rates ranging from 50-Gbps to 300-Gbps. The low-end, middle-end, and high-end ONUs attain power budgets of 39/31/21 dB, 43/36/26 dB, and 40/33/23 dB, respectively. This architecture serves as an effective bridge from the current 50G IM/DD PON to the anticipated 200G coherent PON, meeting the varied requirements of users at different service levels.

Multi-modal image fusion aims to amalgamate pivotal information from various sensor sources to provide informative visual representation in imaging scenes. Rapid and precise fusion of images is crucial for practical applications in fields such as autonomous driving and medical diagnostics. However, the primary challenge lies in balancing computational costs with the effectiveness of feature extraction, while ensuring the robust integration of salient features across modalities. Here, this paper introduces WaveFusion, a wavelet vision transformer equipped with an advanced saliency-guided loss strategy to optimize multi-modal image fusion. Initially, to provide a comprehensive and efficient representation of multi-modal data, we introduce an adaptive wavelet transform module for feature decomposition and reconstruction. Following this, self-attention mechanisms and convolutional networks are naturally applied in parallel to process low-frequency and high-frequency components, resulting in the development of a wavelet-enhanced vision transformer. Secondly, WaveFusion utilizes a dual-aggregation attention approach that improves cross-modal feature complementarity and intra-modal feature coherence within a single fusion module. Furthermore, we propose a dynamic saliency-informed selective loss function to refine the optimization process, with the objective of enhancing critical feature retention and maintaining overall image consistency across fusion scenarios. The efficacy and versatility of our method are validated in both infrared-visible fusion and medical image fusion tasks. Experiment results demonstrate that WaveFusion provides a superior balanced approach that optimizes both fusion performance and cost-efficiency, and additionally improves performance in downstream tasks such as multi-modal semantic segmentation and object detection.

Over the past two decades, advances in materials science and electronics have greatly supported the progress of the communication industry, and the emergence of new materials has made it possible to design higher performance communication modules. As a potential key technology for future 6G networks, the standardisation and industrialisation of visible light communication (VLC) cannot be separated from the improvement of the communication devices. In this paper, we first analyse and summarise the performance enhancement of new materials and designs of VLC devices. Then the support of improved capabilities of novel VLC devices for 6G new services is prospected. As device performance improves, VLC is expected to support more scenarios in 6G.

The context of the Internet of Vehicles (IoV) and the growing vehicular deployment of sensors and smart devices have demanded alternative wireless communication technologies. Despite the prevalent works on radio-frequency (RF)-based vehicular communication technologies, visible-light communication (VLC) is envisaged to be a promising candidate due to its unlicensed spectrum and high transmission rates. However, the long-distance and high-speed transmission performance of vehicular VLC needs more investigation considering the massive data transmission needs for automotive applications. In this article, a vehicular multiple-input–multiple-output (MIMO) VLC system based on two commercial headlights and a self-designed PIN array is experimentally demonstrated as a proof of concept for the IoV. Considering the relative movement of vehicles, different communication areas are divided based on the position-dependent receiver receiving states. The boundary condition of each area is derived. For the first time, the selection of the strategy that best suits the MIMO demultiplexing scheme is discussed by analyzing the rank and type of the channel matrix. A modified pilot-aided phase recovery method based on polynomial curve fitting (PCF) is proposed to compensate for the phase noise caused by the sampling frequency offset (SFO). The proposed method maintains good robustness even when the VLC system suffers from the strong nonlinearity and bandwidth limitation of the headlights. Based on the proposed MIMO VLC system, we achieve a record-breaking data rate of 3.08 Gb/s at a 2-m indoor transmission link. We further extend the transmission distance to 100 m, and successfully achieve overall data rates of 336 and 362 Mb/s during the daytime and nighttime, respectively. To the best of our knowledge, these are the highest transmission data rates ever reported for a 100-m vehicular VLC system. Our experimental demonstration clearly verifies the feasibility of VLC in the application of IoV.

In this paper, a novel configuration of VLC(visible light communication) system based on sub-carrier multiplexing of MIMO-OFDM is proposed. The system consists of a MIMO array of white LEDs to transmit and receive data. The data is respectively modulated by two sub-carriers at 6.25MHz and 12.5MHz. An overview of the system configuration is described, and the transmission experiment is implemented. Related experimental description, results and analysis are reported in this paper.

A novel optical signaling protocol is proposed in which the mobile agent MANet is used to transport the distribution network control and management information among the nodes. The 1310 nm band is designed to be a control optical wavelength channel to transmit BHPs and 1550 nm band is used to be burst packets optical wavelength channels to transmit BPs. Referring to RSVP-TE in DCMASON, significations and formats of signaling messages in the protocol are defined. The key design of scheme is to utilize the structure of active packets in the application of mobile agent MANet to encapsulate signaling messages and traffic together into one BHP to shorten the set-up time of an optical path. Such structure is also used to process distributed disposals to alleviate the workload to promote cooperating calculation ability of each node. Good efficiencies are shown by simulations of establishment time and blocking probability in a 40- nodes optical mesh network.