Channel State Prediction

Latest papers

14 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels
Oct 12, 2026Shengjie Zhong, Zhongliang Zhao, Jingxuan Chen +5Wireless Communications Latent World Models
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China · Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory · Hangzhou International Innovation Institute, Beihang University, Hangzhou +8
JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels
Oct 9, 2026Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan +2Channel State Prediction Variational Autoencoders
Ericsson Research, Stockholm, Sweden · Department of Computer and Information Science, Linköping University, Linköping, Sweden · Department of Electrical Engineering, Linköping University, Linköping, Sweden
TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction
Aug 27, 2026Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari +3Channel State Prediction Uncertainty Quantification
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks
Jun 26, 2026Ahmed Y. Radwan, Fahad Syed Muhammad, Matthew Baker +1Channel State Prediction Wireless Communications
Department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada · Nokia Networks, 12 rue Jean Bart, Paris-Saclay, Massy, 91300, France · Nokia UK, Broers Building, 21 JJ Thomson Avenue, Cambridge, CB3 0FA, United Kingdom
Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction
Jun 24, 2026Uma Kishore Godavarti, K. Giridhar, Vanani Prince Dharmendrabhai +2Autoencoders Channel State Prediction
Network Modem Team, Samsung R&D India Bangalore · Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding
May 29, 2026Ruiqi Kong, He Chen, Xiaojun LinWireless Communications Physics-Informed ML
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks
May 29, 2026Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli6G Networks Channel State Prediction
Department of Electrical, Electronic, and Information Engineering (DEI), Univ. of Bologna, Bologna, Italy
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
May 28, 2026Liwen Jing, Yisha Lu, Tingting Yang +5Wireless Foundation Models Channel State Prediction
Pengcheng Laboratory Shenzhen, China
ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture
May 11, 2026Nanqing Jiang, Zhangyao Song, Tao Guo +2Frequency-Domain Feature Learning Wireless Communications
School of Cyber Science and Engineering, Southeast University, Nanjing, China
MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
Apr 23, 2026Aladin Djuhera, Haris Gacanin, Holger BocheEfficient Neural Network Inference Selective SSMs
Technical University Munich, Germany · RWTH Aachen University, Germany
Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework
Apr 2, 2026Xuejian Zhang, Ruisi He, Minseok Kim +3Channel State Prediction V2X Communication
State Key Laboratory of Advanced Rail Autonomous Operation, the School of Electronics and Information Engineering, and the Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China · Graduate School of Science and Technology, Niigata University, Niigata, Japan
Structured Latent Dynamics in Wireless CSI via Homomorphic World Models
Mar 20, 2026Salmane Naoumi, Mehdi Bennis, Marwa ChafiiLatent Dynamics Modeling Channel State Prediction
NYU WIRELESS, NYU Tandon School of Engineering, New York, USA. · Center for Wireless Communications, University of Oulu, Finland. · Engineering Division, New York University (NYU), Abu Dhabi, UAE.
Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks
Mar 20, 2026Yijia Guo, Junqing Zhang, Yao-Win Peter HongChannel State Prediction Wireless Communications
School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, United Kingdom · Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan 300044
MILAAP: Mobile Link Allocation via Attention-based Prediction
Jun 24, 2025Yung-Fu Chen, Anish AroraSelf-Attention Wireless Communications
The Ohio State University, Columbus, OH 43210, USA

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PRB-WM: Physics-Regularized Beta World Models for Predictive Link Adaptation on Real Time-Varying Underwater Acoustic Channels

Shengjie Zhong, Zhongliang Zhao, Jingxuan Chen, Dianrun Huang+4

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China · Department of Strategic and Advanced Interdisciplinary Research, Peng Cheng Laboratory · Hangzhou International Innovation Institute, Beihang University, Hangzhou+8

Wireless Communications Latent World Models Channel State Prediction

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Adaptive modulation and coding (AMC) improves the spectral efficiency of underwater acoustic (UWA) links, but its feedback is inevitably stale: sound propagates slowly, and by the time channel state information (CSI) arrives, the fast time-varying multipath channel has moved on. Predictive link adaptation therefore needs more than a point forecast: it needs a distribution over each candidate action's consequences. We propose PRB-WM, a physics-regularized Beta world model for predictive per-subcarrier link adaptation on real time-varying UWA channels. A persistence-anchored stochastic latent-space dynamics model learns how the channel evolves; a counterfactual outcome head evaluates, for every candidate modulation and coding scheme (MCS), a Beta-binomial distribution of bit errors on shared imagined futures; and a chance-constrained receding-horizon model predictive controller maximizes expected spectral efficiency subject to a bounded probability of violating the bit error rate (BER) target. To the best of our knowledge, PRB-WM is the first world model for UWA communications. In closed-loop replay of six measured sea-trial links from the public UWA Channel Library against a 24-method pool spanning the full methodological spectrum, PRB-WM attains a mean goodput 87% above the strongest baseline, ranks first on all six time-varying links at the longest lookahead, and is the only method whose realized frame risk decreases systematically with lookahead, meeting the BER-violation contract where no baseline does. Ablations confirm that rollout sampling dispersion is essential to the chance constraint and that the physics anchor is necessary for trainability.