278,436 research outputs found
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Channel estimation and signal detection are very challenging for an
orthogonal frequency division multiplexing (OFDM) system without cyclic prefix
(CP). In this article, deep learning based on orthogonal approximate message
passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver
includes a channel estimation neural network (CE-Net) and a signal detection
neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the
least square channel estimation algorithm and refined by minimum mean-squared
error (MMSE) neural network. The OAMP-Net is established by unfolding the
iterative OAMP algorithm and adding some trainable parameters to improve the
detection performance. The DL-OAMP receiver is with low complexity and can
estimate time-varying channels with only a single training. Simulation results
demonstrate that the bit-error rate (BER) of the proposed scheme is lower than
those of competitive algorithms for high-order modulation.Comment: 5 pages, 4 figures, updated manuscript, International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2019). arXiv admin note:
substantial text overlap with arXiv:1903.0476
Deep Learning and Inverse Problems
Machine Learning (ML) methods and tools have gained great success in many
data, signal, image and video processing tasks, such as classification,
clustering, object detection, semantic segmentation, language processing,
Human-Machine interface, etc. In computer vision, image and video processing,
these methods are mainly based on Neural Networks (NN) and in particular
Convolutional NN (CNN), and more generally Deep NN. Inverse problems arise
anywhere we have indirect measurement. As, in general, those inverse problems
are ill-posed, to obtain satisfactory solutions for them needs prior
information. Different regularization methods have been proposed, where the
problem becomes the optimization of a criterion with a likelihood term and a
regularization term. The main difficulty, however, in great dimensional real
applications, remains the computational cost. Using NN, and in particular Deep
Learning (DL) surrogate models and approximate computation, can become very
helpful. In this work, we focus on NN and DL particularly adapted for inverse
problems. We consider two cases: First the case where the forward operator is
known and used as physics constraint, the second more general data driven DL
methods.Comment: arXiv admin note: text overlap with arXiv:2308.1549
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