Blind and Channel-agnostic Equalization Using Adversarial Networks

Lauinger, Vincent; Hoffmann, Manuel; Ney, Jonas; Wehn, Norbert; Schmalen, Laurent

doi:10.1109/globecom48099.2022.10000630

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This training data needs to be transmitted as pilot symbols, lowering the net throughput and information rate of the communication system. To solve this problem, an ANN-based channel equalizer is proposed in [5], which utilizes a generative adversarial network (GAN) to enable unsupervised training. For unsupervised training, no labels are required, therefore it can be performed without the overhead of transmitting pilot symbols.…”

Section: Introductionmentioning

confidence: 99%

FPGA-based Trainable Autoencoder for Communication Systems

Ney

Dörner

Herrmann

et al. 2022

Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

Self Cite

View full text Add to dashboard Cite

In recent years, communication engineers put strong emphasis on artificial neural network (ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system and its components. In this context, unsupervised training is of special interest as it enables adaptation without the overhead of transmitting pilot symbols. In this work, we present a novel ANN-based, unsupervised equalizer and its trainable field programmable gate array (FPGA) implementation. We demonstrate that our custom loss function allows the ANN to adapt for varying channel conditions, approaching the performance of a supervised baseline. Furthermore, as a first step towards a practical communication system, we design an efficient FPGA implementation of our proposed algorithm, which achieves a throughput in the order of Gbit/s, outperforming a high-performance GPU by a large margin.

show abstract

Section: Introductionmentioning

confidence: 99%

FPGA-based Trainable Autoencoder for Communication Systems

Ney

Dörner

Herrmann

et al. 2022

Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

Self Cite

View full text Add to dashboard Cite

show abstract

Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation

Ney¹,

Lauinger

Schmalen

et al. 2023

2023 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

View full text Add to dashboard Cite

The ever-increasing data rates of modern communication systems lead to severe distortions of the communication signal, imposing great challenges to state-of-the-art signal processing algorithms. In this context, neural network (NN)-based equalizers are a promising concept since they can compensate for impairments introduced by the channel. However, due to the large computational complexity, efficient hardware implementation of NNs is challenging. Especially the backpropagation algorithm, required to adapt the NN's parameters to varying channel conditions, is highly complex, limiting the throughput on resourceconstrained devices like field programmable gate arrays (FPGAs). In this work, we present an FPGA architecture of an NN-based equalizer that exploits batch-level parallelism of the convolutional layer to enable a custom mapping scheme of two multiplication to a single digital signal processor (DSP). Our implementation achieves a throughput of up to 20 GBd, which enables the equalization of high-data-rate nonlinear optical fiber channels while providing adaptation capabilities by retraining the NN using backpropagation. As a result, our FPGA implementation outperforms an embedded graphics processing unit (GPU) in terms of throughput by two orders of magnitude. Further, we achieve a higher energy efficiency and throughput as state-of-theart NN training FPGA implementations. Thus, this work fills the gap of high-throughput NN-based equalization while enabling adaptability by NN training on the edge FPGA.

show abstract

Blind and Channel-agnostic Equalization Using Adversarial Networks

Cited by 2 publications

References 11 publications

FPGA-based Trainable Autoencoder for Communication Systems

FPGA-based Trainable Autoencoder for Communication Systems

Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation

Contact Info

Product

Resources

About