Blind Identification of LDPC Code Based on Deep Learning

Ni, Yanqin; Peng, Shengliang; Zhou, Lin; Yang, Xi

doi:10.1109/dsa.2019.00073

Cited by 14 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, researchers like Jin, Ni, Wang, Liao, and Henarejos still had minor tweaks made to their data before inputting them in the DL model. [23], [24] and [25] studied channel decoding, specifically LDPC decoding, and their methods were the division of blocks of N bit sequences into segments with a segment length of 4 bits, constellation demapping followed by the conversion into log-likelihood bits, and the addition of bits before the receiver receives different codewords to create their unique indicator section respectively. [26] and [27] both combined the real and imaginary parts of the channel matrix into a larger matrix for massive MIMO and reshaped the matrix to their specific needs.…”

Section: B Preprocessingmentioning

confidence: 99%

“…Similarly, in LDPC coding, Ni et al, also used CNN for the blind detection and the identification of quasi-cyclic LDPC, and spatially coupled LDPC for cognitive radio or military communications systems. It is an essential decoding interaction for the receiver to be capable of blindly identifying the channel code adopted by the transmitter to reduce extra channel resources [23]. A novel resource allocation method used CNN to optimize channel state information (CSI) was developed in [31] and reported that DL outperforms the traditional resource optimization methods.…”

Section: B Deep Neural Networkmentioning

confidence: 99%

“…Since 5G communications systems have adopted polar codes, specifically LDPC codes, DL has been used to discover methods to blindly identify LDPC codes [23], reduce the decoding delay [25], [36], [37], [38], analyze the trade-off of LDPC codes for channel coding [24], develop error correction codes for nonlinear channels [39] and optimize the decoding algorithm to solve a non-convex minimization problem [40]. As expected, when comparing traditional decoding to DL-based decoding, DL has a greater reward.…”

Section: B Deep Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

Yao

2021

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

The current development of 5G technology is flourishing with widespread deployment across the world at a rapid pace. However, there is still a demand concerning 5G research for service and performance improvement. Research tasks include but are not limited to quality-of-service (QoS), energy efficiency, massive connectivity, reliable communications, and security. Due to the advancement of deep learning, numerous such research has utilized this technique. This paper provides a comprehensive review of 5G communications research using deep learning. Specifically, we address the issues of low-density parity-check (LDPC) coding, massive multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), resource allocation, and security.

show abstract

Section: B Preprocessingmentioning

confidence: 99%

Section: B Deep Neural Networkmentioning

confidence: 99%

Section: B Deep Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

Yao

2021

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

“…In [13]- [16], the candidate set consists of different coding schemes. For instance, the blind recognition algorithm proposed in [16] recovers the selected coding scheme among QC-LDPC, SC-LDPC, and convolutional codes. In [17]- [30], however, the coding scheme is fixed and known, and the candidate set consists of different parameters for that coding scheme.…”

Section: Introduction Forward Error Correcting (Fec) Codes Have Been ...mentioning

confidence: 99%

Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

Dehdashtian,

Hashemi,

Salehkaleybar

2020

Preprint

View full text Add to dashboard Cite

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multipath fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.

show abstract

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

Gautam,

Thakur,

Singh

2024

Int J Communication

View full text Add to dashboard Cite

SummaryAs a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

show abstract

Blind Identification of LDPC Code Based on Deep Learning

Cited by 14 publications

References 11 publications

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security

Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

Contact Info

Product

Resources

About