A Soft Interference Cancellation Inspired Neural Network for SC-FDE

Baumgartner, Stefan; Lang, Oliver; Huemer, Mario

doi:10.1109/spawc51304.2022.9834005

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…In contrast, our proposed NN-based equalizers are far more similar to the underlying model-based method. More specifically, with SICNNv1an adapted version of an NN-based equalizer called SICNN proposed in our previous work [1] -we try to resemble the model-based iterative SIC method closely. However, we replace numerically demanding, computationally intensive operations, for which also approximations have to be made in the model-based approach, by low-complex NNs.…”

Section: Contributionmentioning

confidence: 99%

“…Secondly, our empirical investigations showed, that for the regarded SC-FDE communication system a precision matrix C (q) −1 vv,k exhibits significant non-zero values only on the major and the first few minor diagonals, and thus can be approximated as a band matrix. In the initial version of SICNNv1 described in [1] (where it is simply referred…”

Section: Instead Of Computing (An Approximate Of) the Precision Matrix Cmentioning

confidence: 99%

“…The non-zero elements of the complex-valued matrix B (q) k are to be estimated by two separate sub-NNs, using one sub-NN for the real part and one for the imaginary part. As stated in [1], hyperparameter optimization turns out that the best equalization performance is achieved with n md = 0, i.e., the precision matrix is assumed to be a diagonal matrix. This insight motivates an adaption of the architecture of SICNN for the structure of SICNNv1.…”

Section: Instead Of Computing (An Approximate Of) the Precision Matrix Cmentioning

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: Contributionmentioning

confidence: 99%

Section: Instead Of Computing (An Approximate Of) the Precision Matrix Cmentioning

confidence: 99%

Section: Instead Of Computing (An Approximate Of) the Precision Matrix Cmentioning

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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“…The first approach trains with data obtained from simulations and past measurements, while using the limited data corresponding to the instantaneous channel to estimate some missing parameters, e.g., a channel matrix. These parameters are then used by the network, either as an input [12], [13] or as part of some internal processing [14]- [20]. This involves imposing a relatively simple model on the channel, typically a linear Gaussian model, which limits their suitability in the presence of complex channel models.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data Augmentation for Deep Receivers

Raviv

Shlezinger

2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments.To do so, DNNs should preferably be trained using large labeled data sets with a similar statistical relationship as the one under which they are to infer. For DNN-aided receivers, obtaining labeled data conventionally involves pilot signalling at the cost of reduced spectral efficiency, typically resulting in access to limited data sets. In this paper, we study how one can enrich a small set of labeled pilots data into a larger data set for training deep receivers. Motivated by the widespread use of data augmentation techniques for enriching visual and text data, we propose dedicated augmentation schemes that exploits the characteristics of digital communication data. We identify the key considerations in data augmentations for deep receivers as the need for domain orientation, class (constellation) diversity, and low complexity. Following these guidelines, we devise three complementing augmentations that exploit the geometric properties of digital constellations. Our combined augmentation approach builds on the merits of these different augmentations to synthesize reliable data from a momentary channel distribution, to be used for training deep receivers. Furthermore, we exploit previous channel realizations to increase the reliability of the augmented samples. The superiority of our approach is numerically evaluated for training several deep receiver architectures in different channel conditions. We consider both linear and non-linear synthetic channels, as well as the COST 2100 channel generator, for both single-input single-output and multiple-input multiple-output scenarios. We show that our combined augmentations approach allows DNN-aided receivers to achieve gains of up to 1 dB in bit error rate and of up to ×3 in spectral efficiency, compared to regular non-augmented training. Moreover, we demonstrate that our augmentations benefit training even as the number of pilots increases, and perform an ablation study on the different augmentations, which shows that the combined approach surpasses each individual augmentation technique.

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ICI Mitigation Based on CIR Interpolation for Wideband SC-FDE System with High-order Modulation in Dynamic Scenario

Wang,

Luo,

et al. 2023

2023 9th International Conference on Computer and Communications (ICCC)

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A Soft Interference Cancellation Inspired Neural Network for SC-FDE

Cited by 4 publications

References 10 publications

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers

Adaptive Data Augmentation for Deep Receivers

ICI Mitigation Based on CIR Interpolation for Wideband SC-FDE System with High-order Modulation in Dynamic Scenario

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