数据辅助贝叶斯学习用于双选择性DCO-OTFS MIMO VLC信道估计，采用仿射预编码叠加训练序列
Data-Aided Bayesian Learning for CSI Estimation over Doubly-Selective DCO-OTFS MIMO VLC Channels with Affine-Precoded Superimposed Training Sequences
- Indian Institute of Technology Kanpur（坎普尔印度理工学院）
- Indian Institute of Technology Jodhpur（焦特普尔印度理工学院）
- University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对双选择性DCO-OTFS MIMO VLC信道，提出仿射预编码叠加训练框架，结合导频与数据辅助贝叶斯学习估计CSI并检测数据，降低导频开销和错误率。
AI中文摘要：
针对使用任意收发脉冲成形、在双选择性信道上传输的循环前缀辅助多输入多输出直流偏置正交时频空间可见光通信链路，提出了一种基于正交仿射预编码叠加训练序列的框架。对于每个发光二极管，导频和数据矩阵被联合仿射预编码并在延迟-多普勒域中叠加。随后，推导了统一的端到端延迟-多普勒域输入-输出关系。在每个光电二极管处，利用正交预编码器分离导频和数据分量，从而抑制相互干扰。基于该模型，开发了一种期望最大化驱动的延迟-多普勒域导频辅助贝叶斯学习方案来估计信道状态信息。随后提出了一种延迟-多普勒域数据辅助贝叶斯学习过程，通过利用检测到的符号作为虚拟导频，以决策导向信道估计的精神，联合细化信道状态信息和检测数据。所采用的线性最小均方误差检测器明确考虑了因实际估计误差导致的不确定性。此外，针对所考虑的MIMO DCO-OTFS VLC场景推导了贝叶斯克拉美-罗下界。数值结果证实，与近期基准相比，归一化均方误差得到改善，导频开销降低，符号错误率得到缓解。
英文摘要：
An orthogonal affine-precoded superimposed training sequence (AP-STS)-based framework is conceived for cyclic prefix (CP)-assisted multiple-input multiple-output (MIMO) direct-current-biased orthogonal time frequency space (DCOOTFS) visible light communication (VLC) links using arbitrary transmit-receive pulse shaping for transmission over doubly selective channels. For each light-emitting diode (LED), the pilot and data matrices are jointly affine-precoded and overlaid in the delay-Doppler (DD)-domain. Then, a unified end-to-end DD-domain input-output relationship is derived. At each photodiode (PD), orthogonal precoders are utilized to separate the pilot and data components, thereby suppressing mutual interference. Building on this model, an expectation-maximization (EM)-driven DD-domain pilot-aided Bayesian learning (DD-PBL) scheme is developed to estimate the channel state information (CSI). A DD-domain data-aided Bayesian learning (DD-DBL) procedure is then proposed for jointly refining the CSI and detecting data by exploiting the detected symbols as virtual pilots in the spirit of decision-directed channel estimation. The linear minimum mean square error (LMMSE) detector harnessed explicitly accounts for CSI uncertainty due to realistic estimation errors. In addition, Bayesian Cramer-Rao lower bounds (BCRLBs) are derived for the MIMO DCO-OTFS VLC setting considered. Numerical results confirm improved normalized mean-square-error (NMSE), reduced pilot overhead, and mitigated symbol error-rate (SER) relative to recent benchmarks.
