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IEEE Transactions on Cognitive Communications and Networking
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Year: 2025 | Volume: 11 | Issue: 1
IEEE Communications Society
Year: 2025 | Volume: 11 | Issue: 1
Generative AI for Secure Physical Layer Communications: A Survey
Changyuan Zhao;Hongyang Du;Dusit Niyato;Jiawen Kang;Zehui Xiong;Dong In Kim;Xuemin Shen;Khaled B. Letaief
Publication Year: 2025,Page(s):3 - 26
Cited by: Papers (5)
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, trad...Show More
Generative AI-Driven Semantic Communication Networks: Architecture, Technologies, and Applications
Chengsi Liang;Hongyang Du;Yao Sun;Dusit Niyato;Jiawen Kang;Dezong Zhao;Muhammad Ali Imran
Publication Year: 2025,Page(s):27 - 47
Cited by: Papers (7)
Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse content intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems must fulfill stringent requirements, including high data rates, throughput, and low latency, while efficiently utili...Show More
AI-Native End-to-End Network Slicing for Next-Generation Mission-Critical Services
Abdullah Ridwan Hossain;Weiqi Liu;Nirwan Ansari;Abbas Kiani;Tony Saboorian
Publication Year: 2025,Page(s):48 - 58
Radio access networks have recently witnessed impressive strides thanks to numerous cutting-edge technologies including network slicing. While these improvements are expected to continue, optimal end-to-end network slicing requires an accurate abstraction of the core network. To properly meet the challenges associated with next-generation networks and services, research and standard organizations ...Show More
Semantic Utility Loss of Information for Energy Efficient Semantic Status Update Communications
Liang Xu;Jian Jiao;Tao Yang;Jianhao Huang;Ye Wang;Qinyu Zhang
Publication Year: 2025,Page(s):59 - 74
Cited by: Papers (7)
Semantic status update communication (SSUC) is envisioned to provide content-aware and energy efficient information delivery. In this paper, we introduce a new metric in the SSUC system, named Semantic Utility Loss (SUL), which captures both age penalty and estimated error for semantic information. By incorporating the knowledge base (KB)-enabled semantic network into a discrete time Markov chain,...Show More
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
Publication Year: 2025,Page(s):75 - 89
Cited by: Papers (2)
In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital modulation and demodulation, robust design of JSCC encoder and decoder becomes challenging not only due to the unpredictable dynamics of channel conditions but also due to diverse modulation orders. To address this challe...Show More
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
Ke Yang;Sixian Wang;Jincheng Dai;Xiaoqi Qin;Kai Niu;Ping Zhang
Publication Year: 2025,Page(s):90 - 104
Cited by: Papers (10)
As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A general trend in many recent works pushing the model adaptability or the application diversity of neural JSCC is based on the convolutional neural network (CNN) backbone, whose model capacity is yet limited, inherently le...Show More
Content Adaptive Distributed Joint Source-Channel Coding for Image Transmission With Hyperprior
Publication Year: 2025,Page(s):105 - 117
We study deep joint source-channel coding (JSCC) in a distributed dual-view scenario where the sources are correlated images and the channels are independent. The transmitters encode respective images and then send them to the same central receiver. In this situation, how to effectively use the correlation between sources to enhance the reconstruction quality is worth investigating. In existing de...Show More
Enhanced Multi-Band Spectrum Prediction Using Singular Spectrum Analysis and Attention-Based BiLSTM
Cui Ben;Yang Peng;Yu Wang;Qianyun Zhang;Lantu Guo;Yun Lin;Guan Gui
Publication Year: 2025,Page(s):118 - 126
Cited by: Papers (1)
The mismatch between growing service demands and scarce spectrum resources in wireless communications has led to spectrum shortages and deteriorating electromagnetic quality. The complexity and variability of spectrum data pose challenges for accurately predicting spectrum usage. A prediction method combining singular spectrum analysis (SSA) with bidirectional long and short time memory (BiLSTM) n...Show More
SCM-GNN: A Graph Neural Network-Based Multi-Antenna Spectrum Sensing in Cognitive Radio
Publication Year: 2025,Page(s):127 - 144
Cited by: Papers (1)
Spectrum Sensing plays a crucial role in cognitive radio and serves as a fundamental requirement for achieving dynamic spectrum access. This work investigates a novel multi-antenna spectrum sensing framework based on graph neural networks to accurately identify the state of primary users. Specifically, the work proposes a graph spectral convolution-based spectrum sensing scheme (SCM-GNN), which em...Show More
Enhanced Few-Shot Specific Emitter Identification via Phase Shift Prediction and Decoupling
Lai Xu;Xue Fu;Yu Wang;Qianyun Zhang;Haitao Zhao;Yun Lin;Guan Gui
Publication Year: 2025,Page(s):145 - 155
Cited by: Papers (3)
Specific Emitter Identification (SEI) is gaining prominence as a passive authentication technology for the physical layer of secure six-generation (6G) wireless communications. Leveraging Deep Learning (DL) for its robust data analysis and feature extraction capabilities, SEI effectively extracts Radio Frequency Fingerprints (RFFs) from received signals for identification. However, DL-based SEI fa...Show More
Spatial-Frequency Block Coding Automatic Recognition With Non-Gaussian Interference for Cognitive MIMO-OFDM Systems
Publication Year: 2025,Page(s):156 - 167
Cited by: Papers (1)
Space-time/frequency block coding (STBCs/SFBCs) scheme is a crucial technique for enhancing the effectiveness and reliability of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with cognitive radio (CR) capability. Automatic recognition of STBCs/SFBCs is a prerequisite for achieving dynamic spectrum sharing in cognitive MIMO-OFDM systems. In contrast t...Show More
A Deep Learning Method for Open-Set Transmission Mode Recognition Based on Multiresolution Time-Frequency Spectrograms
Publication Year: 2025,Page(s):168 - 183
Transmission mode recognition (TMR) is a wireless signal recognition technology that plays an essential role in high frequency (HF) communication reconnaissance systems. However, in noncooperative communications, TMR remains a challenging task due to the complexity of HF environments, and the presence of unknown transmission modes may seriously affect recognition performance. To address these chal...Show More
Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion
Publication Year: 2025,Page(s):184 - 201
Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximiz...Show More
Massive MIMO Channel Estimation With Convolutional Neural Network Structures
Leopoldo Carro-Calvo;Alejandro de la Fuente;Antonio Melgar;Eduardo Morgado
Publication Year: 2025,Page(s):202 - 217
Massive multiple-input-multiple-output (mMIMO) enables a significant increase in capacity in fifth-generation (5G) communications systems, both in beamforming and spatial multiplexing scenarios, demanding highly accurate channel estimates. We present two models based on convolutional neural networks (CNNs) for 5G mMIMO channel estimation that differ in complexity and flexibility. The results achie...Show More
Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach Using Empirical Data
M. Karam Shehzad;Luca Rose;Stefan Wesemann;Mohamad Assaad;Syed Ali Hassan
Publication Year: 2025,Page(s):218 - 230
Massive MIMO regime reaps the benefits of spatial diversity and multiplexing gains, subject to precise channel state information (CSI) acquisition. In the current communication architecture, the downlink CSI is estimated by the user equipment (UE) via dedicated pilots and then fed back to the gNodeB (gNB). The feedback information is compressed to reduce overhead. This compression increases the in...Show More
Online Learning-Based Beamwidth Optimization for Initial Access in Millimeter Wave Cellular Networks
Publication Year: 2025,Page(s):231 - 242
The use of highly directional antennas in millimeter wave (mmWave) cellular networks necessitates precise beam alignment between a base station (BS) and a user equipment (UE), which requires beam sweeping over a large number of directions and causes high initial access (IA) delay. Intuitively, wider beams could lower this delay by requiring fewer sweeping directions. However, this results in a wea...Show More
VOMTC: Vision Objects for Millimeter and Terahertz Communications
Publication Year: 2025,Page(s):243 - 257
Recent advances in sensing and computer vision (CV) technologies have opened the door for the application of deep learning (DL)-based CV technologies in the realm of 6G wireless communications. For the successful application of this emerging technology, it is crucial to have a qualified vision dataset tailored for wireless applications (e.g., RGB images containing wireless devices such as laptops ...Show More
A Self-Attention Mechanism-Based Model to Detect IPv6 Multi-Field Covert Channels
Liancheng Zhang;Jichang Wang;Yi Guo;Hongtao Zhang;Lanxin Cheng;Wenhao Xia
Publication Year: 2025,Page(s):258 - 273
IPv6 covert channels have emerged as a novel type of network threat, which poses new challenges to network security. Multi-field covert channels make use of distributed embedding technology to scatter covert information across multiple packet fields. Existing deep learning-based methods for detecting IPv6 covert channels primarily focus on detecting of single-field covert channels, limiting their ...Show More
Image Analysis Oriented Integrated Sensing and Communication via Intelligent Reflecting Surface
Ning Huang;Chenglong Dou;Yuan Wu;Liping Qian;Sheng Zhou;Rongxing Lu
Publication Year: 2025,Page(s):274 - 287
Integrated sensing and communication (ISAC) provides a promising paradigm for future beyond 5G (B5G) and 6G networks. As an important application of edge intelligence, image analysis (e.g., recognition) at the edge networks has attracted lots of interests. In this paper, we propose an image analysis oriented ISAC, in which the image captured by a wireless image-sensor is transmitted to an edge ser...Show More
Precoding Optimization for MIMO-OFDM Integrated Sensing and Communication Systems
Zhiqing Wei;Rubing Yao;Xin Yuan;Huici Wu;Qixun Zhang;Zhiyong Feng
Publication Year: 2025,Page(s):288 - 299
To meet the increasing demands of high sensing accuracy and high data rate in the intelligent applications of forthcoming 6th generation (6G) mobile communication systems, a precoding optimization scheme is presented for multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. We employ the precoding matrix as the ...Show More
Joint Target Detection and Channel Estimation for Distributed Massive MIMO ISAC Systems
Publication Year: 2025,Page(s):300 - 315
Integrated sensing and communication (ISAC) can be integrated into the beyond 5G (B5G) and 6G communication systems to achieve high-resolution radar sensing with wireless communication signals. Joint target detection and channel estimation are essential for various emerging applications in massive MIMO ISAC systems. Inspired by the idea that partial radar targets could also be the communication sc...Show More
Joint User-Target Pairing, Power Control, and Beamforming for NOMA-Aided ISAC Networks
Publication Year: 2025,Page(s):316 - 332
Cited by: Papers (5)
Integrated sensing and communication (ISAC) emerges as a pivotal solution for augmenting spectrum efficiency and fostering synergies between sensing and communication functionalities. However, ISAC efficacy grapples with inter-functionality interference, which can be efficiently managed by non-orthogonal multiple access (NOMA) schemes. Accordingly, this paper unveils multi-armed bandit (MAB)-based...Show More
On the Performance of Rate Splitting Multiple Access for ISAC in Device-to-Multi-Device IoT Communications
Sutanu Ghosh;Keshav Singh;Haejoon Jung;Chih-Peng Li;Trung Q. Duong
Publication Year: 2025,Page(s):333 - 348
Cited by: Papers (1)
In this paper, we analyze the performance of rate splitting multiple access (RSMA) technique for a multi-device communication system applying integrated sensing and communication (ISAC) to alleviate the problem of overlapping spectrum of radar signal and communication frequency bands. The system includes a cooperative access point (AP) which serves as a sensing node and a decode-and-forward (DF) r...Show More
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