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Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
IEEE Transactions on Cognitive Communications and Networking ( IF 8.6 ) Pub Date : 2020-09-01 , DOI: 10.1109/tccn.2020.2990773
Avi Caciularu , David Burshtein

A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, without using pilot symbols. We derive an approximate maximum likelihood estimate to the channel parameters and reconstruct the transmitted data. We demonstrate significant and consistent improvements in the error rate of the reconstructed symbols, compared to existing blind equalization methods such as constant modulus, thus enabling faster channel acquisition. The VAE equalizer uses a convolutional neural network with a small number of free parameters. These results are extended to blind equalization over a noisy nonlinear ISI channel with unknown parameters. We then consider coded communication using low-density parity-check (LDPC) codes transmitted over a noisy linear or nonlinear ISI channel. The goal is to reconstruct the transmitted message from the channel observations corresponding to a transmitted codeword, without using pilot symbols. We demonstrate improvements compared to the expectation maximization (EM) algorithm using turbo equalization. Furthermore, unlike EM, the computational complexity of our method does not have exponential dependence on the size of the channel impulse response.

中文翻译:

使用变分自编码器的无监督线性和非线性信道均衡和解码

引入了一种用于盲信道均衡和解码、变分推理和变分自动编码器 (VAE) 的新方法。我们首先考虑在不使用导频符号的情况下重建在噪声线性符号间干扰 (ISI) 信道上传输的未编码数据符号,具有未知的脉冲响应。我们推导出信道参数的近似最大似然估计并重建传输的数据。与现有的盲均衡方法(例如恒模)相比,我们证明了重构符号的错误率显着且一致的改进,从而实现了更快的信道捕获。VAE 均衡器使用具有少量自由参数的卷积神经网络。这些结果被扩展到具有未知参数的嘈杂非线性 ISI 信道上的盲均衡。然后我们考虑使用低密度奇偶校验 (LDPC) 码在嘈杂的线性或非线性 ISI 信道上传输的编码通信。目标是在不使用导频符号的情况下,根据与传输的码字相对应的信道观测值来重构传输的消息。与使用涡轮均衡的期望最大化 (EM) 算法相比,我们展示了改进。此外,与 EM 不同,我们方法的计算复杂度与信道脉冲响应的大小没有指数关系。目标是在不使用导频符号的情况下,根据与传输的码字相对应的信道观测值来重构传输的消息。与使用涡轮均衡的期望最大化 (EM) 算法相比,我们展示了改进。此外,与 EM 不同,我们方法的计算复杂度与信道脉冲响应的大小没有指数关系。目标是在不使用导频符号的情况下,根据与传输的码字相对应的信道观测来重建传输的消息。与使用涡轮均衡的期望最大化 (EM) 算法相比,我们展示了改进。此外,与 EM 不同,我们方法的计算复杂度与信道脉冲响应的大小没有指数关系。
更新日期:2020-09-01
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