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English(EN) JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels

新型JEPA-kMMVAE模型预测无线信道状态信息

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文介绍了一种名为JEPA-kMMVAE的神经网络框架，该框架旨在预测时变无线信道中未来的信道状态信息（CSI）。该模型旨在有效利用时间相关性，同时区分信道演变和观测中的噪声。JEPA-kMMVAE利用联合嵌入预测架构结合k-memory马尔可夫变分自编码器，创建用于预测的时间结构化潜在表示。仿真结果表明，在信噪比较高的情况下，该模型的残差配置优于现有基线。 AI

影响 这项研究通过改进信道状态的预测，有望带来更准确的无线通信系统。

排序理由 该集群包含一篇详细介绍新模型及其仿真结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新型JEPA-kMMVAE模型预测无线信道状态信息

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan, Zheng Chen, Nikolaos Pappas ·

    JEPA-kMMVAE：时变无线信道的CSI预测

    arXiv:2610.13315v1 Announce Type: cross Abstract: This paper considers channel state information (CSI) prediction using neural networks for time-varying multi-antenna channels from noisy historical observations. The main challenge is to exploit temporal correlation while distingu…

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