- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Deep clipping noise mitigation using ISTA with the specified observations for LED-based DCO-OFDM system
摘要： Deep clipping is beneficial for the optical orthogonal frequency division multiplexing (O-OFDM) system, since it can lower the peak-to-average power ratio, reduce the direct current requirement in light emitting diodes (LEDs), and relax the bit-resolution requirement in digital-to-analogue converters (DACs). However, it is accompanied by more signal distortions. In this study, a deep clipping noise mitigation scheme using iterative shrinkage/thresholding algorithm (ISTA) with three steps is proposed to improve bit error rate (BER) performance of the LED-based DCO-OFDM systems. In the first step, the estimated observation interference is eliminated from the received symbols to minimise the negative effect of channel noise. In the second step, two strategies are presented to generate the specified observations thus reduce the component of measurement noise in the whole observation vector. In the last step, combining the generalised cross validation and the estimation of observation interference, the appropriate regularisation parameter are calculated for ISTA to improve the robustness of the sparse recovery performance. They use simulations to show that the proposed scheme can correct the deep clipping noise with favourable reconstruction quality, which significantly improves the BER performance and therefore assist the LED non-linearity mitigation.
关键词： clipping noise mitigation,ISTA,BER performance,LED-based DCO-OFDM system,Deep clipping
更新于2025-09-23 15:21:01
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Deep Coupled ISTA Network for Multi-modal Image Super-Resolution
摘要： Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find the high-resolution (HR) version of this image with the guidance of an HR image from another modality. In this paper, we use a model-based approach to design a new deep network architecture for MISR. We first introduce a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency. In JMDL, we simultaneously learn three dictionaries and two transform matrices to combine the modalities. Then, by unfolding the iterative shrinkage and thresholding algorithm (ISTA), we turn the JMDL model into a deep neural network, called deep coupled ISTA network. Since the network initialization plays an important role in deep network training, we further propose a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy. Specifically, we model the network initialization as a multi-layer dictionary learning problem, and solve it through convex optimization. The proposed LOA is demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. Finally, we compare our method with other state-of-the-art methods in the MISR task. The numerical results show that our method consistently outperforms others both quantitatively and qualitatively at different upscaling factors for various multi-modal scenarios.
关键词： ISTA,multi-modal image super-resolution,dictionary learning,deep neural network
更新于2025-09-16 10:30:52