IEEE Transactions on Cognitive Communications and Networking最新文献

{"title":"Large Language Model-Empowered Resource Allocation in Intent-Driven Wireless Networks","authors":"Haofeng Sun;Hui Tian;Jingheng Zheng;Wanli Ni;Qimei Cui;Dusit Niyato;Ping Zhang","doi":"10.1109/TCCN.2026.3663570","DOIUrl":"10.1109/TCCN.2026.3663570","url":null,"abstract":"Although large language models (LLMs) have the potential to automate resource allocation in wireless networks, generating high-precision allocation schemes remains non-trivial for cloud-based LLMs due to their inability of accurate numerical processes. The heavy re-training overhead of LLMs for handling the dynamic features of wireless networks also poses a challenge. This paper presents a new LLM-empowered framework for intent-driven wireless resource allocation. The framework features a dual-expert LLM architecture, consisting of an instruction expert for generating resource allocation instructions and a policy expert for obtaining numerically-precise policies. Both experts encompass lightweight adapters and collaborate with the locally deployed LLM for resource allocation. Decoupling the generation of instruction and policy benefits efficient adaptation and generalization under diverse task requests and dynamic network states. We further propose a new two-stage fine-tuning protocol to enhance the adaptability of LLM-empowered resource allocation through distillation and reinforcement. Experiments on two major application cases demonstrate the superior adaptability and generalization abilities of the proposed LLM-empowered resource allocation framework with two-stage fine-tuning, compared to existing schemes.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6265-6280"},"PeriodicalIF":7.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Identification of Encoders and Interleavers Using Deep Learning With Hardware Validation","authors":"Nayim Ahamed;R. Swaminathan","doi":"10.1109/TCCN.2026.3663584","DOIUrl":"10.1109/TCCN.2026.3663584","url":null,"abstract":"In the fifth-generation (5G) communication era, efficient transmitter operations are essential, with channel encoding and interleaving mitigating random and burst errors to ensure reliable communication. In military communications and spectrum surveillance systems, which operate in non-cooperative environments, and cognitive radio systems, encoder and interleaver parameters are often unknown, necessitating blind identification. While existing research focuses on separately identifying channel encoders and interleavers with high accuracy across various channels, their joint identification remains largely unexplored. We propose a joint identification framework for channel encoders and interleavers over Rayleigh and additive white Gaussian noise (AWGN) channels. Our approach leverages advanced deep learning models, including hybrid convolutional neural network (CNN)-support vector machine (SVM), deep residual networks (DRN), and capsule networks (CapsNet), and compares their performance against standard CNN, dense neural network (DenseNet), recurrent neural network (RNN), CNN+attention, and autoencoder network models. Experimental results demonstrate that our proposed models outperform the baselines, with CapsNet achieving the highest accuracy. Notably, we observe that increasing the input size from 1024 to 16,384 (in powers of two) significantly improves classification accuracy. We analyze classification time and model complexity, showing our joint model reduces latency by 26.6% compared to separate encoder and interleaver identification. We also perform an ablation study on the CapsNet model to determine its optimal architecture, achieving a balance between accuracy and classification time. Finally, we validate our models on a hardware setup using a universal software radio peripheral (USRP) B210-based GNU Radio companion interface. The hardware results closely align with the simulated outcomes, underscoring the robustness and practical applicability of our approach.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6191-6206"},"PeriodicalIF":7.0,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic Dual-Stream Neural Network Based on Adaptive Variational Mode Decomposition Denoising for Automatic Modulation Classification","authors":"Yurui Zheng;Sai Huang;Pengcheng Zhang;Yifan Zhang;Zhiyong Feng","doi":"10.1109/TCCN.2026.3662331","DOIUrl":"10.1109/TCCN.2026.3662331","url":null,"abstract":"Automatic Modulation Classification (AMC) is a key technology for electromagnetic signal cognition and plays a pivotal role in intelligent spectrum sensing and management. Although deep learning-based AMC has become the mainstream paradigm, its potential is constrained by insufficient internal synergy in hybrid network architectures and the degradation of feature extraction capability at low signal-to-noise ratios (SNRs). To address these limitations, this paper proposes an adaptive variational mode decomposition (VMD) denoising-based synergistic dual-stream network (AVDSDN) for modulation classification. The proposed network decouples the traditional hybrid network structure, enabling it to mitigate the impact of noise and achieve full synergistic utilization of different network feature extraction capabilities. AVDSDN comprises three core components: an adaptive VMD denoising module, a dual-stream feature extraction module, and a fusion module. Specifically, we first propose an adaptive VMD denoising mechanism that selectively enhances key signal components while suppressing noise components. Subsequently, we develop a synergistic dual-stream module comprising parallel CNN and Transformer streams. In the CNN stream, a multi-feature interaction extraction block and a lightweight backbone are designed to capture robust local and spatial features, while the Transformer stream innovatively integrates deformable attention and convolutional feed-forward networks to efficiently model long-range temporal dependencies. Finally, a cross-stream attention fusion module is designed to synthesize features from both streams. Simulations on three public datasets demonstrate that AVDSDN achieves superior performance compared to current state-of-the-art (SOTA) algorithms, especially under low SNR conditions. Furthermore, by employing post-training quantization (PTQ), AVDSDN achieves a 5- to 9-fold speedup in inference while maintaining high accuracy, demonstrating its suitability for deployment in real-time systems.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6061-6075"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Communication Topology Reconstruction and Parameter Filtering for Heterogeneous Decentralized Federated Learning","authors":"Mingjun Duan;Xiaoning Zhang;Lang Fan;Yijing Liu;Jiayi Jiang;Haijun Zhang","doi":"10.1109/TCCN.2026.3662666","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3662666","url":null,"abstract":"Federated learning (FL) has been strongly promoted in wireless edge networks because it facilitates collaborative training of machine learning models while ensuring the privacy and security of individual user data. Among different FL frameworks, Decentralized Federated Learning (DFL) framework offers greater flexibility and broader applicability compared to traditional Centralized Federated Learning (CFL) framework. However, existing DFL framework fails to accommodate the heterogeneity of multi-dimensional resources (e.g., communication and computing resources), and further suffer from high communication overhead. These limitations result in reduced training efficiency. To address these issues, this paper proposes SoloDFL, a framework integrating heterogeneity-aware communication topology reconstruction and parameter filtering. In SoloDFL, each client communicates with only one neighboring client, constructing a one-on-one communication topology, and exchanges only filtered local model parameters during communication. By doing so, SoloDFL copes with the synchronization barriers caused by system heterogeneity and further reduces communication overhead. The convergence of SoloDFL is proved theoretically. Extensive experiments show that SoloDFL achieves up to a <inline-formula> <tex-math>$3.9times $ </tex-math></inline-formula> acceleration and reduces communication costs by an average of 20.5% compared to the benchmark algorithms.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6293-6308"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Dynamic Computation Offloading and Resource Allocation in SCMA-Aided Space–Air–Ground Integrated Network","authors":"Pengtao Liu;Kang An;Jing Lei;Yifu Sun;Wei Liu;Symeon Chatzinotas","doi":"10.1109/TCCN.2026.3662334","DOIUrl":"10.1109/TCCN.2026.3662334","url":null,"abstract":"6G mobile is characterized by space-air-ground integrated network (SAGIN), which consists of satellites, uncrewed aerial vehicles (UAVs), and terrestrial nodes. It is envisioned to provide not only wide-ranging wireless connectivity but also extensive computing services. In this paper, we investigate a dynamic sparse code multiple access (SCMA) aided SAGIN and focus on maximizing the energy efficiency of IoT devices through task offloading and resource allocation. The problem involves stochastic optimization with long-term task queue stability constraints. Under the guidance of Lyapunov optimization method, the problem is decomposed into several sub-problems for each time slot and solved separately. To address the coupling between task offloading and resource allocation strategies, we propose the alternating optimization algorithm (TORA-AO). Furthermore, to reduce the number of iterations, we develop a twin delayed deep deterministic policy gradient based task offloading and resource allocation algorithm (TORA-TD3). Simulation results indicate that the proposed algorithms outperform other benchmark solutions in terms of task throughput, energy consumption, energy efficiency, and network stability. The TORA-TD3 algorithm achieved comparable performance compared to the TORA-AO, while reducing the running time by approximately 70%.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6135-6151"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Frame Interpolation Symbolic Semantic Communication for Low Latency Wireless Video Transmission","authors":"Xiaoxuan Qi;Nan Ma;Yijing Lin;Wenkai Liu;Zhicheng Bao;Ping Zhang","doi":"10.1109/TCCN.2026.3662328","DOIUrl":"10.1109/TCCN.2026.3662328","url":null,"abstract":"Video semantic communication has risen as one of the most promising solutions to meet the video transmission requirements of the sixth-generation (6G) wireless communication scenarios. Existing video semantic communication schemes primarily focus on ensuring high video reconstruction quality under constrained channel bandwidth, while timeliness and compatibility with digital communication systems remain open challenges requiring further exploration. By utilizing deep joint source-channel coding (JSCC) as backbone, this paper proposes a frame interpolation enabled video symbolic semantic communication system (FIVSSC) which reduces the processing latency by transmitting only the selected semantic key frames. In particular, a symbolic semantic communication system is proposed to enhance compatibility with digital communication systems through efficient symbolic representation of semantic information. Simulation experimental results demonstrate that compared to other semantic video communication and traditional video communication schemes, the proposed FIVSSC features both higher reconstruction quality and higher timeliness. And it can be better compatible with digital communication systems. A symbolic semantic communication prototype platform is developed, demonstrating the feasibility of the proposed method.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6175-6190"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Local to Global: Semantic Communication-Driven Remote 3D Scene Reconstruction Using Low-Altitude Platforms","authors":"Tianle Mai;Haipeng Yao;Gepeng Zhu;Chenlang Jin;Xiangjun Xin","doi":"10.1109/TCCN.2026.3662333","DOIUrl":"10.1109/TCCN.2026.3662333","url":null,"abstract":"Recently, Neural Radiance Fields (NeRF) have demonstrated excellent fidelity performance in 3D scene reconstruction. However, their significant data demands pose challenges for low-altitude platforms, particularly due to stringent bandwidth constraints. Semantic communication (SC) offers a solution by transmitting only the most task-relevant information, which would significantly reduce the required data throughput. Besides, the recent emergence of large AI models (LAMs) further strengthens SC by providing powerful, pre-trained semantic encoders that can extract and compress high-value features. Therefore, in this paper, we propose an end-to-end framework, LAM-SC-3DR, which integrates LAM-driven semantic extraction, semantic communication, and NeRF-based 3D reconstruction to optimize remote 3D scene recovery for low-altitude platforms. The framework is consists of three main modules: the Semantic Feature Extraction (SFE) module, which utilizes a pre-trained LAM to extract multi-level semantics (including object, appearance, and geometry) from 2D images; the Joint Semantic–Channel Coding (SCC) module, which integrates semantic compression with channel coding for reliable transmission over the noisy wireless links; and the 3D Scene Reconstruction (3DSR) module, which combines the received semantics to create photorealistic, semantically consistent volumetric renderings. Extensive evaluations demonstrate that LAM-SC-3DR can reduce transmission load by up to 96%, while maintaining 3D semantic reconstruction quality.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6207-6220"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PreConfig: A Unified Language Model Framework for Network Configuration Automation","authors":"Fuliang Li;Bocheng Liang;Haozhi Lang;Jiajie Zhang;Jiaxing Shen;Chengxi Gao;Xingwei Wang","doi":"10.1109/TCCN.2026.3662678","DOIUrl":"https://doi.org/10.1109/TCCN.2026.3662678","url":null,"abstract":"Manual network configuration tools are constrained by their reliance on extensive domain expertise and rigid, single-purpose designs, limiting their adaptability to diverse scenarios and complex applications. This paper introduces PreConfig, a novel language model-based framework for automating network configuration tasks. By framing tasks such as configuration generation, translation, analysis, and completion as text-to-text transformations, PreConfig unifies these processes under a single versatile model. Leveraging advancements in natural language processing, PreConfig eliminates the need for extensive manual re-engineering by automatically learning domain-specific patterns through continued training on a specialized network configuration corpus. To address the lack of domain knowledge in general language models, we construct a comprehensive dataset from vendor manuals and community forums and fine-tune a programming language model for robust performance across various tasks. Additionally, we propose ConfigBLEU, a novel evaluation metric that incorporates syntax-aware features to assess the accuracy of generated configurations. Experimental results demonstrate that PreConfig significantly outperforms existing tools and general-purpose language models in both syntactic accuracy and semantic correctness across diverse network configuration tasks. This work establishes a unified and adaptable approach for advancing network configuration automation.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6320-6330"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOMA and Hybrid Beamforming Aided Secure Computation Offloading for mmWave VEC Networks With Multi-Agent DRL","authors":"Ying Ju;Zhiwei Cao;Mingdong Li;Lei Liu;Qingqi Pei;Mianxiong Dong;Shahid Mumtaz;Mohsen Guizani","doi":"10.1109/TCCN.2026.3662303","DOIUrl":"10.1109/TCCN.2026.3662303","url":null,"abstract":"Mobile edge computing (MEC) meets the requirements of various delay-sensitive applications by providing high-speed computing services to a large number of user vehicles simultaneously. Nevertheless, the inherent open feature of wireless channels and the constraints of limited spectrum resources present significant challenges to achieving both secure offloading and high offloading rate simultaneously. Millimeter wave (mmWave) can provide user vehicles with abundant spectrum resources, but its short wavelength causes high path loss. In this paper, we utilize hybrid beamforming and non-orthogonal multiple access (NOMA) technologies to improve the offloading rate of user vehicles and to interfere with eavesdroppers, thus improving the security of the offloading process in mmWave vehicular edge computing (VEC) networks. We first use the K-means algorithm to cluster user vehicles. Then, we minimize the system delay by jointly optimizing the analog beamforming matrix, the user vehicle transmit power and the allocation ratio of the MEC server computation resource while ensuring the security of the offloading process. The above optimization problem is formulated as a Markov decision process (MDP) and a twin Delayed Deep Deterministic Policy Gradient (TD3)-Dueling Double Deep Q Network (D3QN) based multi-agent secure computation offloading scheme is proposed to solve the MDP problem. Simulation results demonstrate that the TD3-D3QN based multi-agent scheme is able to adapt to highly dynamic VEC networks when guaranteeing the security of the offloading process and low system delay.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6089-6103"},"PeriodicalIF":7.0,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Freshness Performance Analysis and Optimization in Timely and Secure Low Altitude Economics","authors":"Yaoqi Yang;Ming He;Wei Han;Xin Lu;Chenping Hou","doi":"10.1109/TCCN.2026.3661502","DOIUrl":"10.1109/TCCN.2026.3661502","url":null,"abstract":"Low altitude economy (LAE) can significantly promote the air-to-ground communication development by providing various data collection, transmission and computing services. However, due to the openness trait of wireless channel, LAE also faces timeliness and security concerns on sensing data’s stale and leakage at ground base station (BS) end. This can result in delayed information delivery, compromised data integrity, and increased vulnerability to malicious attacks. In this regard, to address the above concerns, this paper first establishes the blockchain-based UAV-enabled mobile crowdsensing (MCS) model. Then, after determining the impact of the blockchain on data timeliness, the mathematical expressions of the data freshness metric are derived in closed form. On this basis, a data freshness minimization problem with security premise is formulated, where the UAV’s transmission power, computing rate, and BS’s power allocation ratio are jointly optimized. Furthermore, one deep reinforcement learning-aided multi-objective optimization (MOP) algorithm is proposed to solve the formulated problem. At last, under various parameter settings, numerical results have evaluated the effectiveness of the proposals.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"12 ","pages":"6016-6030"},"PeriodicalIF":7.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}