Error correcting neural networks for channels with Gaussian noise

Ortuno, I.; Ortuño, M.; Delgado, Jaime

doi:10.1109/ijcnn.1992.227326

Cited by 15 publications

(15 citation statements)

References 4 publications

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“…Researchers have applied ML to the physical layer for modulation recognition [14], [15], channel modeling and identification [16], [17], encoding and decoding [18], [19], channel estimation [20], and equalization [21], [22] (see further details in [23] and [24]); however, ML has been unused commercially because handling physical channels is a complex process, and conventional ML algorithms have limited learning capacity. Researchers believe that ML can achieve further performance improvements by introducing DL to the physical layer.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for wireless physical layer: Opportunities and challenges

Wang¹,

Wen

Wang³

et al. 2017

China Commun.

532

266

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Abstract-Machine learning (ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning (DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system (for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

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Section: Introductionmentioning

confidence: 99%

Deep learning for wireless physical layer: Opportunities and challenges

Wang¹,

Wen

Wang³

et al. 2017

China Commun.

532

266

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“…Initially applied to upper layers [4], ML has recently found applications also at the PHY Layer [5]- [7] such as channel coding [8]- [10], modulation recognition [7], obstacle detection [11], [12] and physical layer security [13] etc. The use of ML for channel estimation was initially investigated in works such as [14].…”

Section: A Related State Of the Artmentioning

confidence: 99%

“…Each sidelink subframe (1 ms) contains 14 OFDM symbols out of which 10 are used for carrying user data and the remaining 4 (at positions [2,5,8,11] with 0-based indexing) are used for carrying DMRS symbols. The DMRS symbols are sequences r (α) u,v that are obtained by a cyclic shift of a base sequence r u,v (n) according to…”

Section: Channel Estimation In C-v2xmentioning

confidence: 99%

Channel Estimation in C-V2X using Deep Learning

Sattiraju¹,

Weinand²,

Schotten³

2020

Preprint

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Channel estimation forms one of the central component in current Orthogonal Frequency Division Multiplexing (OFDM) systems that aims to eliminate the inter-symbol interference by calculating the Channel State Information (CSI) using the pilot symbols and interpolating them across the entire timefrequency grid. It is also one of the most researched field in the Physical Layer (PHY) with Least-Squares (LS) and Minimum Mean Squared Error (MMSE) being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on Convolutional Neural Networks (CNNs) for channel estimation in vehicular environments used in 3GPP Rel.14 Cellular-Vehicle-to-Everything (C-V2X) technology. To this end, we compare the performance of the proposed Deep Learning (DL) architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy C-V2X channel estimation methods especially at high mobile speeds.

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“…At the same time, deep learning has also become a research hotspot and has been applied to data analysis, speech recognition, image recognition, and other fields. The fitting and expressive abilities of neural networks are potent, and deep learning is suitable for HDPC decoding [1,2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for BCH Decoding in Satellite Communication

Xue-yu¹

2023

HSET

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Deep learning is widely used in various fields due to the advancement of algorithms, the enrichment of high-efficiency databases, and the increase in computing power. Especially in the satellite communication, the learning and parallel computing capabilities of neural networks make them ideal for decoding. Many researchers have recently applied deep learning neural networks to decode high-density parity check (HDPC) codes (such as BCH and RS code), improving the decoder’s performance. This review aims to provide general insights on applying neural network decoders to satellite communications. Due to the neural network’s learning ability, the neural network-based decoder can be trained to change the weights, thereby reducing the influence of non-white noise in satellite communications, such as the influence between the satellite and the terrestrial network and the mutual interference within the satellites. To compensate for non-white noise, shortest circles in Tanner graph and unreliable information, a decoder system model for satellite communication constructed by three neural networks is presented.

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Error correcting neural networks for channels with Gaussian noise

Cited by 15 publications

References 4 publications

Deep learning for wireless physical layer: Opportunities and challenges

Deep learning for wireless physical layer: Opportunities and challenges

Channel Estimation in C-V2X using Deep Learning

Deep Learning Algorithms for BCH Decoding in Satellite Communication

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