发表机构
San Diego State University（圣地亚哥州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对上行多用户MIMO系统中相位噪声引起的性能退化，提出变分贝叶斯框架，通过将发射端PN吸收进信号并利用von Mises先验获得闭式后验更新，实现低复杂度高精度的联合PN估计与数据检测，仿真验证其符号错误率优于SIW算法。
AI 中文摘要
相位噪声（PN）源于不完美的本地振荡器，引入乘性失真，降低通信系统性能。在上行多用户多输入多输出（MIMO）系统中，每个发射和接收天线处存在独立的振荡器，各自贡献不相关的噪声分量，进一步加剧了这种损伤。现有的接收端PN补偿算法要么依赖线性化近似，在严重PN条件下失去准确性，要么计算复杂度随天线数量呈禁止性增长。为解决这些局限，我们提出一种变分贝叶斯（VB）框架，用于上行MIMO系统中的联合PN估计和数据检测。我们开发了基于VB的检测器，将噪声统计量视为潜在变量，并通过将发射端PN吸收到发射信号中，将所得复合变量作为推断目标，重新表述推断问题。在von Mises先验下，这种重新表述产生精确的闭式共轭后验更新，由此我们推导出一种改进的检测器，在低复杂度下实现优越性能。仿真结果表明，所提出的VB算法在广泛的信道条件、调制阶数和PN严重程度范围内，实现了比自干扰白化（SIW）算法和传统相位噪声不知情检测器更低的符号错误率，同时保持计算上可扩展到大规模MIMO部署。
英文摘要
Phase noise (PN), arising from imperfect local oscillators, introduces multiplicative distortions that degrade the performance of communication systems. In uplink multiuser multiple-input multiple-output (MIMO) systems, this impairment is further compounded by the presence of independent oscillators at each transmit and receive antenna, each contributing an uncorrelated noise component. Existing PN compensation algorithms at the receiver either rely on linearization approximations that lose accuracy under severe PN conditions, or incur computational complexity that scales prohibitively with the number of antennas. To address these limitations, we propose a variational Bayes (VB) framework for joint PN estimation and data detection in uplink MIMO systems. We develop VB-based detectors that treat noise statistics as latent variables, and reformulate the inference problem by absorbing the transmitter PN into the transmitted signal, treating the resulting composite variable as the inference target. Under von Mises priors, this reformulation yields exact closed-form conjugate posterior updates, from which we derive an improved detector achieving superior performance at low complexity. Simulation results demonstrate that the proposed VB algorithm achieves lower symbol error rates than the Self-Interference Whitening (SIW) algorithm and conventional phase-noise-unaware detectors across a wide range of channel conditions, modulation orders, and PN severities, while remaining computationally scalable to large MIMO deployments.
