arXiv subjects

Li Ping Qian

Publications and source records attributed to Li Ping Qian.

6 recordsLinked to original sources

arXiv2026

GenED-SC: Generative Editing Semantic Communication with Integrated Multi-Modal LLMs

Deep learning-based joint source-channel coding has recently demonstrated strong potential for semantic communication (SemComm). However, most existing approaches focus on optimizing visual-fidelity metrics, which can lead to reduced perceptual quality. Generative model-based SemComm leverages rich prior knowledge from large-scale pre-training to enhance perceptual quality, but often at the cost of increased distortion and unreliability. This paper addresses the above issues by proposing a two-stage semantic image transmission framework, integrating a multimodal large language model (MLLM) for generative editing. In the first stage, a JSCC-based discriminative transmission selectively prioritizes semantically important regions, preserving scene layout and object integrity under limited bandwidth. In the second phase, MLLM-driven generative editing refines missing details based on the textual descriptions, enhancing semantic fidelity and perceptual quality. Extensive experiments show that the proposed framework achieves state-of-the-art performance in semantic preservation, perceptual quality, and visual fidelity across a wide range of channel conditions, especially in low-SNR regimes.

eess.SP↗

arXiv2026

Structured Spectral Compression based Low-Bitrate Secure Speech Communications for Internet of Things assisted Non-Terrestrial Networks

This paper focuses on the Low-Bitrate Secure Speech Communications based on the Structured Spectral Compression (LB-S2C2). Specifically, the Mel spectral matrix of the speech signal is first encoded at the transmitter side through compressive sensing based on waveform segmentation and data quantization. Then, the Automatic Repeat Request (ARQ) is combined with forward error correction to achieve reliable transmission of speech signals over wireless channels. Thirdly, the received signals are recovered as the speech at the receiver side. Finally, we conduct a series of simulation experiments for the performance evaluation of LB-S2C2. Our simulations reveal that the dictionary matrix used for the speech reconstruction is different from the one used for the high-order matrix sparsification by even only approximately 0.1%, and then the accurate speech recovery fails. It implies that the speech data can be securely transmitted when the dictionary matrix is preserved. More importantly, the LB-S2C2 exhibits a very high privacy protection capability with the average voiceprint similarity to be only 0.3, which is much lower than the 0.8 of the semantic speech communication scheme DeepSC-S, and even lower than the 0.33 of the latest speech communication scheme OFI-OFCNB. In addition, our simulations reveal that the proposed structured speech coding boasts a time complexity of merely O(n), and the proposed speech recovery scheme requires the 12-bit memory storage only, which outperforms the traditional encryption algorithms proposed for speech communications. In comparison with the conventional compression techniques, our spectral compression method renders the coding rate of only 3.9kbps, which is lower than the current lowest speech coding rate of 6.3kbps achieved by G.723.

cs.NI↗

arXiv2026

Energy Minimization Oriented Resource Allocation for Integrated Sensing and Communication in Marine IoT Networks

Integrated sensing and communication (ISAC) has become a promising technical framework for Marine Internet of Things (MIoT) systems. Nevertheless, all devices rely on battery power, so energy efficiency becomes a core bottleneck limiting practical deployment. This paper investigates the energy consumption minimization problem of MIoT-oriented ISAC systems. In this system, an uncrewed aerial vehicle (UAV) uses non-orthogonal multiple access (NOMA) to simultaneously perform target sensing and collect data from uncrewed surface vehicles (USVs), then forwards processed sensing information and USV data to a shore-based base station (SBS). Subject to latency limits and sensing performance requirements, total system energy consumption can be minimized via joint optimization of multiple variables, UAV transmit beamforming, dedicated sensing signal, USV transmit power, UAV computation power, and time resource allocation for sensing and communication phases. To tackle this non-convex optimization problem, we build a layered solution architecture that divides the original problem into independent subproblems and optimizes each alternately according to its mathematical features. Specifically, we first derive closed-form USV transmit power solutions and conduct variable substitution. The successive convex approximation (SCA) method is adopted to convert remaining non-convex subproblems into convex forms, on which we design efficient iterative algorithms. Simulation results verify the validity and accuracy of our algorithm in reducing system energy consumption. Compared with orthogonal frequency division multiple access (OFDMA) and genetic algorithm benchmarks, our scheme lowers system energy consumption by 19.71% and 8%, respectively. In addition, our optimized energy value only has an 8.72% gap from the optimum solved by the LINGO solver.

cs.NI↗

arXiv2023

Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things

With the rapid development of Artificial Intelligent Internet of Things (AIoT), the image data from AIoT devices has been witnessing the explosive increasing. In this paper, a novel deep image semantic communication model is proposed for the efficient image communication in AIoT. Particularly, at the transmitter side, a high-precision image semantic segmentation algorithm is proposed to extract the semantic information of the image to achieve significant compression of the image data. At the receiver side, a semantic image restoration algorithm based on Generative Adversarial Network (GAN) is proposed to convert the semantic image to a real scene image with detailed information. Simulation results demonstrate that the proposed image semantic communication model can improve the image compression ratio and recovery accuracy by 71.93% and 25.07% on average in comparison with WebP and CycleGAN, respectively. More importantly, our demo experiment shows that the proposed model reduces the total delay by 95.26% in the image communication, when comparing with the original image transmission.

cs.CV↗

arXiv2021

Visualizing Deep Learning-based Radio Modulation Classifier

Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learning-based radio modulation classifiers are lack of interpretability, and there is little explanation or visibility into what kinds of radio features are extracted and chosen for classification. In this paper, we visualize different deep learning-based radio modulation classifiers by introducing a class activation vector. Specifically, both convolutional neural networks (CNN) based classifier and long short-term memory (LSTM) based classifier are separately studied, and their extracted radio features are visualized. Extensive numerical results show both the CNN-based classifier and LSTM-based classifier extract similar radio features relating to modulation reference points. In particular, for the LSTM-based classifier, its obtained radio features are similar to the knowledge of human experts. Our numerical results indicate the radio features extracted by deep learning-based classifiers greatly depend on the contents carried by radio signals, and a short radio sample may lead to misclassification.

cs.LG↗

arXiv2011

Globally Optimal Distributed Power Control for Nonconcave Utility Maximization

Transmit power control in wireless networks has long been recognized as an effective mechanism to mitigate co-channel interference. Due to the highly non-convex nature, optimal power control is known to be difficult to achieve if a system utility is to be maximized. To date, there does not yet exist a distributed power control algorithm that maximizes any form of system utility, despite the importance of distributed implementation for the wireless infrastructureless networks such as ad hoc and sensor networks. This paper fills this gap by developing a Gibbs Sampling based Asynchronous distributed power control algorithm (referred to as GLAD). The proposed algorithm quickly converges to the global optimal solution regardless of the concavity, continuity, differentiability and monotonicity of the utility function. Same as other existing distributed power control algorithms, GLAD requires extensive message passing among all users in the network, which leads to high signaling overhead and high processing complexity. To address this issue, this paper further proposes a variant of the GLAD algorithm, referred to as I-GLAD, where the prefix "I" stands for infrequent message passing. The convergence of I-GLAD can be proved regardless of the reduction in the message passing rate. To further reduce the processing complexity at each transmitter, we develop an enhanced version of I-GLAD, referred to as NI-GLAD, where only the control messages from the neighboring links are processed. Our simulation results show that I-GLAD approximately converges to the global optimal solution regardless of the type of the system utility function. Meanwhile, the optimality of the solution obtained by NI-GLAD depends on the selection of the neighborhood size.

cs.NI↗