Xiaodong Xu

Weizhi Li, Haotai Liang, Chen Dong et al.

Semantic communication serves as a novel paradigm and attracts the broad interest of researchers. One critical aspect of it is the multi-user semantic communication theory, which can further promote its application to the practical network environment. While most existing works focused on the design of end-to-end single-user semantic transmission, a novel non-orthogonal multiple access (NOMA)-based multi-user semantic communication system named NOMASC is proposed in this paper. The proposed system can support semantic tranmission of multiple users with diverse modalities of source information. To avoid high demand for hardware, an asymmetric quantizer is employed at the end of the semantic encoder for discretizing the continuous full-resolution semantic feature. In addition, a neural network model is proposed for mapping the discrete feature into self-learned symbols and accomplishing intelligent multi-user detection (MUD) at the receiver. Simulation results demonstrate that the proposed system holds good performance in non-orthogonal transmission of multiple user signals and outperforms the other methods, especially at low-to-medium SNRs. Moreover, it has high robustness under various simulation settings and mismatched test scenarios.

Senran Fan, Haotai Liang, Chen Dong et al.

Intelligent inspection robots are widely used in substation patrol inspection, which can help check potential safety hazards by patrolling the substation and sending back scene images. However, when patrolling some marginal areas with weak signal, the scene images cannot be sucessfully transmissted to be used for hidden danger elimination, which greatly reduces the quality of robots'daily work. To solve such problem, a Specific Task-oriented Semantic Communication System for Imag-STSCI is designed, which involves the semantic features extraction, transmission, restoration and enhancement to get clearer images sent by intelligent robots under weak signals. Inspired by that only some specific details of the image are needed in such substation patrol inspection task, we proposed a new paradigm of semantic enhancement in such specific task to ensure the clarity of key semantic information when facing a lower bit rate or a low signal-to-noise ratio situation. Across the reality-based simulation, experiments show our STSCI can generally surpass traditional image-compression-based and channel-codingbased or other semantic communication system in the substation patrol inspection task with a lower bit rate even under a low signal-to-noise ratio situation.

Xiangyu Gao, Yaping Sun, Dongyu Wei et al.

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.

Haotai Liang, Mengran Shi, Chen Dong et al.

The key feature of model-driven semantic communication is the propagation of the model. The semantic model component (SMC) is designed to drive the intelligent model to transmit in the physical channel, allowing the intelligence to flow through the networks. According to the characteristics of neural networks with common and individual model parameters, this paper designs the cross-source-domain and cross-task semantic component model. Considering that the basic model is deployed on the edge node, the large server node updates the edge node by transmitting only the semantic component model to the edge node so that the edge node can handle different sources and different tasks. In addition, this paper also discusses how channel noise affects the performance of the model and proposes methods of injection noise and regularization to improve the noise resistance of the model. Experiments show that SMCs use smaller model parameters to achieve cross-source, cross-task functionality while maintaining performance and improving the model's tolerance to noise. Finally, a component transfer-based unmanned vehicle tracking prototype was implemented to verify the feasibility of model components in practical applications.

Yingbin Zhou, Yaping Sun, Guanying Chen et al.

Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face challenges with conflicting requirements between codebook design and digital constellation modulation. Traditional codebooks need wide index ranges, while modulation favors few discrete states. To address this, we propose a multilevel generative semantic communication system with a two-stage training framework. In the first stage, we train a high-quality codebook, using a multi-head octonary codebook (MOC) to compress the index range. In addition, a residual vector quantization (RVQ) mechanism is also integrated for effective multilevel communication. In the second stage, a noise reduction block (NRB) based on Swin Transformer is introduced, coupled with the multilevel codebook from the first stage, serving as a high-quality semantic knowledge base (SKB) for generative feature restoration. Finally, to simulate modern image transmission scenarios, we employ a diverse collection of high-resolution 2K images as the test set. The experimental results consistently demonstrate the superior performance of MOC-RVQ over conventional methods such as BPG or JPEG. Additionally, MOC-RVQ achieves comparable performance to an analog JSCC scheme, while needing only one-sixth of the channel bandwidth ratio (CBR) and being directly compatible with digital transmission systems.

Nan Xue, Yaping Sun, Zhiyong Chen et al.

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but the role of wireless networks in supporting LLMs has not been thoroughly explored. In this paper, we propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks. Specifically, we decompose the MoE layer in LLMs by placing the gating network and the preceding neural network layer at BS, while distributing the expert networks among the devices. This deployment leverages the parallel inference capabilities of expert networks on mobile devices, effectively utilizing the limited computing and caching resources of these devices. Accordingly, we develop a performance metric for WDMoE-based LLMs, which accounts for both model capability and latency. To minimize the latency while maintaining accuracy, we jointly optimize expert selection and bandwidth allocation based on the performance metric. Moreover, we build a hardware testbed using NVIDIA Jetson kits to validate the effectiveness of WDMoE. Both theoretical simulations and practical hardware experiments demonstrate that the proposed method can significantly reduce the latency without compromising LLM performance.

Peigen Ye, Yaping Sun, Shumin Yao et al.

Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver. However, due to the fact that the semantic relations among code vectors are not necessarily related to the distance of the corresponding code indices, the performance of the codebook-enabled semantic communication system is susceptible to the channel noise. Thus, how to improve the system robustness against the noise requires careful design. This paper proposes a robust codebook-assisted image semantic communication system, where semantic codec and codebook are first jointly constructed, and then vector-to-index transformer is designed guided by the codebook to eliminate the effects of channel noise, and achieve image generation. Thanks to the assistance of the high-quality codebook to the Transformer, the generated images at the receiver outperform those of the compared methods in terms of visual perception. In the end, numerical results and generated images demonstrate the advantages of the generative semantic communication method over JPEG+LDPC and traditional joint source channel coding (JSCC) methods.

Senran Fan, Zhicheng Bao, Chen Dong et al.

The end-to-end image communication system has been widely studied in the academic community. The escalating demands on image communication systems in terms of data volume, environmental complexity, and task precision require enhanced communication efficiency, anti-noise ability and semantic fidelity. Therefore, we proposed a novel paradigm based on Semantic Feature Decomposition (SeFD) for the integration of semantic communication and large-scale visual generation models to achieve high-performance, highly interpretable and controllable image communication. According to this paradigm, a Texture-Color based Semantic Communication system of Images TCSCI is proposed. TCSCI decomposing the images into their natural language description (text), texture and color semantic features at the transmitter. During the transmission, features are transmitted over the wireless channel, and at the receiver, a large-scale visual generation model is utilized to restore the image through received features. TCSCI can achieve extremely compressed, highly noise-resistant, and visually similar image semantic communication, while ensuring the interpretability and editability of the transmission process. The experiments demonstrate that the TCSCI outperforms traditional image communication systems and existing semantic communication systems under extreme compression with good anti-noise performance and interpretability.

Lei Teng, Senran Fan, Chen Dong et al.

Semantic communication with joint semantic-channel coding robustly transmits diverse data modalities but faces challenges in mitigating semantic information loss due to packet drops in packet-based systems. Under current protocols, packets with errors are discarded, preventing the receiver from utilizing erroneous semantic data for robust decoding. To address this issue, a packet-loss-resistant MoE Swin Transformer-based Video Semantic Communication (MSTVSC) system is proposed in this paper. Semantic vectors are encoded by MSTVSC and transmitted through upper-layer protocol packetization. To investigate the impact of the packetization, a theoretical analysis of the packetization strategy is provided. To mitigate the semantic loss caused by packet loss, a 3D CNN at the receiver recovers missing information using un-lost semantic data and an packet-loss mask matrix. Semantic-level interleaving is employed to reduce concentrated semantic loss from packet drops. To improve compression, a common-individual decomposition approach is adopted, with downsampling applied to individual information to minimize redundancy. The model is lightweighted for practical deployment. Extensive simulations and comparisons demonstrate strong performance, achieving an MS-SSIM greater than 0.6 and a PSNR exceeding 20 dB at a 90% packet loss rate.

Yanzhao Hou, Jiaxiang Geng, Boyu Li et al.

Federated LoRA has emerged as a promising technique for efficiently fine-tuning large language models (LLMs) on distributed devices by reducing the number of trainable parameters. However, existing approaches often inadequately overlook the theoretical and practical implications of system and data heterogeneity, thereby failing to optimize the overall training efficiency, particularly in terms of wall-clock time. In this paper, we propose an adaptive federated LoRA strategy with independent client sampling to minimize the convergence wall-clock time of federated fine-tuning under both computation and communication heterogeneity. We first derive a new convergence bound for federated LoRA with arbitrary and independent client sampling, notably without requiring the stringent bounded gradient assumption. Then, we introduce an adaptive bandwidth allocation scheme that accounts for heterogeneous client resources and system bandwidth constraints. Based on the derived theory, we formulate and solve a non-convex optimization problem to jointly determine the LoRA sketching ratios and sampling probabilities, aiming to minimize wall-clock convergence time. An efficient and low-complexity algorithm is developed to approximate the solution. Finally, extensive experiments demonstrate that our approach significantly reduces wall-clock training time compared to state-of-the-art methods across various models and datasets.

Jianqiao Chen, Nan Ma, Wenkai Liu et al.

Channel reconstruction and generalization capability are of equal importance for developing channel estimation schemes within deep learning (DL) framework. In this paper, we exploit a novel DL-based scheme for efficient OFDM channel estimation where the neural networks for channel reconstruction and generalization are respectively designed. For the former, we propose a dual-attention-aided super-resolution neural network (DA-SRNN) to map the channels at pilot positions to the whole time-frequency channels. Specifically, the channel-spatial attention mechanism is first introduced to sequentially infer attention maps along two separate dimensions corresponding to two types of underlying channel correlations, and then the lightweight SR module is developed for efficient channel reconstruction. For the latter, we introduce continual learning (CL)-aided training strategies to make the neural network adapt to different channel distributions. Specifically, the elastic weight consolidation (EWC) is introduced as the regularization term in regard to loss function of channel reconstruction, which can constrain the direction and space of updating the important weights of neural networks among different channel distributions. Meanwhile, the corresponding training process is provided in detail. By evaluating under 3rd Generation Partnership Project (3GPP) channel models, numerical results verify the superiority of the proposed channel estimation scheme with significantly improved channel reconstruction and generalization performance over counterparts.

Senran Fan, Zhicheng Bao, Chen Dong et al.

Semantic communication, as a revolutionary communication architecture, is considered a promising novel communication paradigm. Unlike traditional symbol-based error-free communication systems, semantic-based visual communication systems extract, compress, transmit, and reconstruct images at the semantic level. However, widely used image similarity evaluation metrics, whether pixel-based MSE or PSNR or structure-based MS-SSIM, struggle to accurately measure the loss of semantic-level information of the source during system transmission. This presents challenges in evaluating the performance of visual semantic communication systems, especially when comparing them with traditional communication systems. To address this, we propose a semantic evaluation metric -- SeSS (Semantic Similarity Score), based on Scene Graph Generation and graph matching, which shifts the similarity scores between images into semantic-level graph matching scores. Meanwhile, semantic similarity scores for tens of thousands of image pairs are manually annotated to fine-tune the hyperparameters in the graph matching algorithm, aligning the metric more closely with human semantic perception. The performance of the SeSS is tested on different datasets, including (1)images transmitted by traditional and semantic communication systems at different compression rates, (2)images transmitted by traditional and semantic communication systems at different signal-to-noise ratios, (3)images generated by large-scale model with different noise levels introduced, and (4)cases of images subjected to certain special transformations. The experiments demonstrate the effectiveness of SeSS, indicating that the metric can measure the semantic-level differences in semantic-level information of images and can be used for evaluation in visual semantic communication systems.

Nan Xue, Yaping Sun, Zhiyong Chen et al.

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless communications can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, deploying LLMs collaboratively across edge servers of base station (BS) and mobile devices in the wireless communications system. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at BS, while distributing the expert networks across the devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.