English(EN) Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking

新的贝叶斯深度学习框架跟踪MIMO接收器中的硬件损伤

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-03 04:00

研究人员开发了一个名为MP-TTBDL的新框架，该框架利用消息传递和贝叶斯深度学习来联合跟踪大规模MIMO接收器中的信道和硬件损伤。该方法通过分配不同的马尔可夫先验来模拟无线信道和硬件漂移的不同时间尺度。该框架分离信道估计和损伤校准模块，迭代交换信息直至收敛，并已证明与传统方法相比具有更低的信道估计误差。 AI

影响这项研究可能导致无线通信系统中更鲁棒和准确的信道估计，从而提高数据传输的可靠性。

排序理由这是一篇研究论文，详细介绍了MIMO系统中信号处理的新技术框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Wei Xu, An Liu · 2026-07-03 04:00

基于消息传递的两时间尺度贝叶斯学习用于联合信道和存储器硬件损伤跟踪

arXiv:2607.01660v1 Announce Type: new Abstract: Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degrading channel estimation. This paper employs a residual recurrent gated unit (RGR…

本文如何被排名

报道来源 [1]

基于消息传递的两时间尺度贝叶斯学习用于联合信道和存储器硬件损伤跟踪

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