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Preprint Sep 2026

One-Step Generative CSI Prediction

Channel state information (CSI) prediction has emerged as a promising approach for mitigating channel aging in wireless communication systems. Recently, generative models have been employed to develop CSI prediction frameworks capable of modeling the uncertainty in future channel realizations. However, the high computa...

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2026

Environment-Aware Beamforming Prediction: A CKM-Inspired Approach

In multiple-input multiple-output (MIMO) systems, beamforming is a core enabler for concentrating signal power toward targeted users to enhance spectral efficiency and network capacity. However, realizing the full potential of beamforming critically relies on accurate channel state information (CSI). Conventional CSI a...

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Channel Extrapolation for Fluid Antenna Systems: Diffusion-Based Framework and CEUNet Approach

Fluid antenna systems (FAS) have emerged as a promising paradigm for wireless communications, enabling channel reconfigurability that offers a novel spatial degree of freedom. Nevertheless, efficiently acquiring accurate and high-resolution channel state information (CSI) in FAS remains challenging, primarily due to it...

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Preprint Aug 2026

A JEPA-Based Field-Layer World Model for Bridging Channel Prediction and Estimation

Experiments on single-band and cross-band reconstruction demonstrate improved symbol detection and, more notably, substantial beamforming gains despite modest NMSE improvements, indicating that FWM captures task-relevant spatial propagation structure beyond coefficient-wise CSI fitting.

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Open access Sep 2026

Lightweight LLM-based End-to-End CSI Prediction for MIMO-OFDM Systems

Simulations demonstrate that the proposed scheme delivers robust prediction accuracy across diverse mobility scenar-ios, maintains strong performance under limited training data, and exhibits zero-shot cross-scenario generalization, significantly outperforming both conventional and deep learning baselines in TDD and FD...

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Preprint Aug 2026

CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications

A novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework, which employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios.

Chao Jiang, Zhuo Xu, Yongli Yan · 0 citations

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