High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes

Download
2020-05-01
Balevi, Eren
Andrews, Jeffrey G.
This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction code for intractable nonlinear channels. The LDPC encoder shrinks the input space of the autoencoder, which enables the autoencoder to learn more easily. The proposed error correction code shows promising results for one-bit quantization, a challenging case of a nonlinear channel. Specifically, our design gives a waterfall slope bit error rate even with high order modulation formats such as 16-QAM and 64-QAM despite one-bit quantization. This gain is theoretically grounded by proving that the trained autoencoder provides approximately Gaussian distributed data to the LDPC decoder even though the received signal has non-Gaussian statistics due to the one-bit quantization.
21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020

Suggestions

Autoencoder-Based Error Correction Coding for One-Bit Quantization
Balevi, Eren; Andrews, Jeffrey G. (2020-06-01)
This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which "perfectly" refers to converging the global minima. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder...
Non-Binary LDPC Codes for Magnetic Recording Channels: Error Floor Analysis and Optimized Code Design
Hareedy, Ahmed; Amiri, Behzad; Galbraith, Rick; Dolecek, Lara (2016-08-01)
© 2016 IEEE.In this paper, we provide a comprehensive analysis of the error floor along with code optimization guidelines for structured and regular non-binary low-density parity-check (NB-LDPC) codes in magnetic recording (MR) applications. While the topic of the error floor performance of binary LDPC codes over additive white Gaussian noise (AWGN) channels has recently received considerable attention, very little is known about the error floor performance of NB-LDPC codes over other types of channels, des...
Packet loss resilient transmission of 3D models
Bici, M. Oguz; Norkin, Andrey; Akar, Gözde (2007-09-19)
This paper presents an efficient joint source-channel coding scheme based on forward error correction (FEC) for three dimensional (3D) models. The system employs a wavelet based zero-tree 3D mesh coder based on Progressive Geometry Compression (PGC). Reed-Solomon (RS) codes are applied to the embedded output bitstream to add resiliency to packet losses. Two-state Markovian channel model is employed to model packet losses. The proposed method applies approximately optimal and unequal FEC across packets. Ther...
Upper Bounds to Error Probability with Feedback
Nakiboğlu, Barış (2009-08-18)
A new technique is proposed for upper bounding the error probability of fixed length block codes with feedback. Error analysis is inspired by Gal lager's error analysis for block codes without feedback. Zigangirov-D'yachkov (Z-D) encoding scheme is analyzed with the technique on binary input channels and k-ary symmetric channels. A strict improvement is obtained for k-ary symmetric channels.
Deep learning-based encoder for one-bit quantization
Balevi, Eren; Andrews, Jeffrey G. (2019-12-01)
© 2019 IEEE.This paper proposes a deep learning-based error correction coding for AWGN channels under the constraint of one-bit quantization in receivers. An autoencoder is designed and integrated with a turbo code that acts as an implicit regularization. This implicit regularizer facilitates approaching the Shannon bound for the one-bit quantized AWGN channels even if the autoencoder is trained suboptimally, since one-bit quantization stymies ideal training. Our empirical results show that the proposed cod...
Citation Formats
E. Balevi and J. G. Andrews, “High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes,” Georgia, Amerika Birleşik Devletleri, 2020, vol. 2020-May, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/101045.