基于贝叶斯学习的低峰均比可见光光学OFDM系统稀疏信道估计与信号恢复
Sparse Channel Estimation and Signal Recovery for Reduced-PAPR Visible Light Optical OFDM Systems Relying on Bayesian Learning
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- Indian Institute of Technology Kanpur（印度坎普尔理工学院）
- Indian Institute of Technology Jodhpur（印度焦特普尔理工学院）
- University of Southampton（南安普顿大学）
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中文总结 AI 辅助
针对低峰均比VLC-OFDM系统，提出基于组稀疏贝叶斯学习的信道估计与信号恢复方法，降低导频开销并提升BER、中断概率和NMSE性能。
中文摘要 AI 辅助
本文提出了一种多径信道冲激响应（CIR）估计器，随后进行稀疏频域（FD）信号检测，该方法利用了多径CIR和FD信号在不同测量向量间的联合稀疏性，专为用于可见光通信（VLC）系统的低峰均比光学OFDM（O-OFDM）而设计。首先，我们推导了非对称限幅O-OFDM（ACO-OFDM）和直流偏置O-OFDM（DCO-OFDM）系统的输入输出关系。其次，介绍了传统的信道估计（CE）基准方法，包括LMMSE和LS方法，以及一种能够利用VLC系统多径CIR稀疏性的正交匹配追踪（OMP）技术。随后，提出了一种基于新型组稀疏OMP（GOMP）概念的CE技术，该技术利用了VLC信道模型中非视距（NLoS）分量固有的多径特性，在不同测量向量间CIR的延迟域中利用组稀疏性。此外，还提出了一种基于组稀疏贝叶斯学习（GBL）方法的高级组稀疏CE技术，该技术显著降低了导频开销。同时开发了GBL的低复杂度版本LCGBL，大幅降低了GBL的计算成本。因此，GOMP和GBL框架也被扩展到稀疏FD O-OFDM符号的数据检测中，利用了FD O-OFDM符号在不同测量向量间的组稀疏性。计算了贝叶斯克拉美-罗下界（BCRLB）以评估所提CE技术的估计性能。仿真结果表明，尽管导频开销降低，所提出的GBL技术在误码率（BER）、中断概率和归一化均方误差（NMSE）方面均优于其他CE方案，定量地证实了其优越性。
英文摘要
A multipath CIR estimator is proposed, followed by sparse frequency-domain (FD) signal detection, which harnesses the simultaneous-sparsity innate in the multipath CIR and FD signals across measurement vectors, conceived for optical OFDM (O-OFDM) utilized for visible light communication (VLC) systems having reduced peak-to-average power ratio. At the outset, we derive the input-output relationships for the asymmetrically clipped O-OFDM (ACO-OFDM) as well as for the direct current-biased O-OFDM (DCO-OFDM) systems. Next, traditional benchmarking methods are introduced for channel estimation (CE), including both the LMMSE and LS methods, followed by an orthogonal matching pursuit (OMP)-based technique capable of exploiting sparsity in the multipath CIR of the VLC system. Subsequently, an CE technique based on a novel group sparse OMP (GOMP) concept is proposed, which capitalizes on the group-sparsity in the delay domain of the CIR across measurement vectors, attributed to the multipath characteristics inherent in the NLoS components of the VLC channel model. Additionally, an advanced group-sparse CE technique is put forth based on the group-sparse Bayesian learning (GBL) approach, which considerably reduces the pilot overhead. A low complexity version of GBL, termed LCGBL, is also developed that reduces the computational cost of GBL significantly. Consequently, the GOMP and GBL frameworks are also extended to the data detection of the sparse FD O-OFDM symbols, which utilizes the group-sparsity of the FD O-OFDM symbols across measurement vectors. The Bayesian Cramer-Rao lower bound (BCRLB) is computed to assess the estimation performance of the proposed CE techniques. Our simulations demonstrate that, despite its reduced pilot overhead, the proposed GBL technique outperforms the other CE schemes in terms of its BER, outage probability, and NMSE, quantitatively reinforcing its superiority.
