Neural Network Based Single-Carrier Frequency Domain Equalization

Baumgartner, Stefan; Lang, Oliver; Huemer, Mario

doi:10.1007/978-3-031-25312-6_34

Cited by 2 publications

(13 citation statements)

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“…More specifically, for comparison with model-based equalizers, we use the LMMSE estimator [35], the iterative DFE (implemented in the same way as described in [47] for UW-OFDM systems), and the iterative SIC method, where the approximation ( 14) is employed. We compare the proposed NNs with the stateof-the-art NN-based data estimators OAMP-Net2 [19] and DetNet [18], whereby we do not use DetNet as proposed in [18] for MIMO systems, but a better performing version that is adapted for SC-FDE systems [31]. Moreover, we show the BER performance and computational complexity of KAFCNN from [31], which is an FCNN that is designed for equalization in SC-FDE systems by using a layer conducting an inverse DFT as a last layer, i.e., the knowledge that the data symbols being defined in time domain are to be estimated given a received vector in frequency domain is incorporated.…”

Section: Resultsmentioning

confidence: 99%

“…For the regarded SC-FDE systems, this approach considerably improves the performance of NN-based equalizers at high SNRs. Further, we briefly describe a data normalization scheme specifically tailored for SC-FDE, which was already presented in [31].…”

Section: Training Set Generation and Data Normalizationmentioning

confidence: 99%

“…That is, although NN-based equalizers perform well for a wide E b /N 0 range, at higher E b /N 0 values, and thus low BERs (of, e.g., 10 −5 or 10 −6 ), their BER curves do not fall as steeply as those of many model-based equalizers. Although this issue occurs for most NN-based equalizers (shown, e.g., in [31], [44]), interestingly, there are only very few works like [43], where proper training of NN-based equalizers is addressed. In this work, we propose a novel approach for the generation of training sets for NN-based equalizers.…”

Section: A Training Set Generationmentioning

confidence: 99%

“…With the same idea as for UW-OFDM in mind, namely to apply a normalization scheme leading to variances of the elements of the noise vector that are independent of the multipath channel, we implement a data normalization scheme for SC-FDE systems. This data normalization scheme for SC-FDE is elucidated in [31], and thus we repeat here only the result. To obtain channel-independent noise variances var(w i ), the system model ( 5) has to be multiplied by K = κ H − 1 /2 , where κ = tr{ H}/tr{ HMM H H} .…”

Section: B Data Normalizationmentioning

confidence: 99%

“…Employing NN-based equalizers for SC-FDE systems necessitates, in contrast to MIMO systems over uncorrelated Rayleigh fading channels, an additional pre-processing step. As extensively described in [31], for the application of NN-based equalizers in SC-FDE systems a data normalization scheme is required for a well-behaved NN training and thus a satisfying performance of the NN equalizers. We also briefly review the necessary data normalization scheme in this work.…”

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confidence: 99%

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SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

Baumgartner,

Lang,

Huemer

2024

Trans. Mach. Learn. Comm. Netw.

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In recent years data-driven machine learning approaches have been extensively studied to replace or enhance traditionally model-based processing in digital communication systems. In this work, we focus on equalization and propose a novel neural network (NN-)based approach, referred to as SICNN. SICNN is designed by deep unfolding a model-based iterative soft interference cancellation (SIC) method. It eliminates the main disadvantages of its model-based counterpart, which suffers from high computational complexity and performance degradation due to required approximations. We present different variants of SICNN. SICNNv1 is specifically tailored to single carrier frequency domain equalization (SC-FDE) systems, the communication system mainly regarded in this work. SICNNv2 is more universal and is applicable as an equalizer in any communication system with a block-based data transmission scheme. Moreover, for both SICNNv1 and SICNNv2, we present versions with highly reduced numbers of learnable parameters. Another contribution of this work is a novel approach for generating training datasets for NN-based equalizers, which significantly improves their performance at high signal-to-noise ratios. We compare the bit error ratio performance of the proposed NN-based equalizers with state-of-the-art model-based and NN-based approaches, highlighting the superiority of SICNNv1 over all other methods for SC-FDE. Exemplarily, to emphasize its universality, SICNNv2 is additionally applied to a unique word orthogonal frequency division multiplexing (UW-OFDM) system, where it achieves state-of-theart performance. Furthermore, we present a thorough complexity analysis of the proposed NN-based equalization approaches, and we investigate the influence of the training set size on the performance of NN-based equalizers.

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Section: Resultsmentioning

confidence: 99%

Section: Training Set Generation and Data Normalizationmentioning

confidence: 99%

Section: A Training Set Generationmentioning

confidence: 99%

Section: B Data Normalizationmentioning

confidence: 99%

mentioning

confidence: 99%

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SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

Baumgartner,

Lang,

Huemer

2024

Trans. Mach. Learn. Comm. Netw.

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Neural Network Approaches for Data Estimation in Unique Word OFDM Systems

Baumgartner,

Bognár,

Lang

et al. 2024

IEEE Trans. Veh. Technol.

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Data estimation is conducted with model-based estimation methods since the beginning of digital communications. However, motivated by the growing success of machine learning, current research focuses on replacing model-based data estimation methods by data-driven approaches, mainly neural networks (NNs). In this work, we particularly investigate the incorporation of existing model knowledge into data-driven approaches, which is expected to lead to complexity reduction and / or performance enhancement. We describe three different options, namely "model-inspired" pre-processing, choosing an NN architecture motivated by the properties of the underlying communication system, and inferring the layer structure of an NN with the help of model knowledge. Most of the current publications on NN-based data estimation deal with general multiple-input multiple-output (MIMO) communication systems. In this work, we investigate NN-based data estimation for socalled unique word orthogonal frequency division multiplexing (UW-OFDM) systems. We highlight differences between UW-OFDM systems and general MIMO systems one has to be aware of when using NNs for data estimation, and we introduce measures for a successful utilization of NN-based data estimators in UW-OFDM systems. Further, we investigate the use of NNs for data estimation when channel coded data transmission is conducted, and we present adaptions to be made, such that NNbased data estimators provide satisfying performance for this case. We compare the presented NNs concerning achieved bit error ratio performance and computational complexity, we show the peculiar distributions of their data estimates, and we also point out their downsides compared to model-based equalizers.

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Neural Network Based Single-Carrier Frequency Domain Equalization

Cited by 2 publications

References 7 publications

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

Neural Network Approaches for Data Estimation in Unique Word OFDM Systems

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