Information Bottleneck Graphs for receiver design

Lewandowsky, Jan; Stark, Maximilian; Bauch, Gerhard

doi:10.1109/isit.2016.7541827

Cited by 22 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1b shows such a mutual-informationmaximizing lookup table. This figure uses the information bottleneck graph notation introduced in [19]. The inputs (y 0 , y 1 ) of the shown lookup table are compressed by the mutual-information-maximizing lookup table such that the output t is highly informative about the relevant variable X.…”

Section: B the Information Bottleneck Methods And Relevant-informatiomentioning

confidence: 99%

Decoding of Non-Binary LDPC Codes using the Information Bottleneck Method

Stark¹,

Bauch²,

Lewandowsky³

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

Recently, a novel lookup table based decoding method for binary low-density parity-check codes has attracted considerable attention. In this approach, mutual-informationmaximizing lookup tables replace the conventional operations of the variable nodes and the check nodes in message passing decoding. Moreover, the exchanged messages are represented by integers with very small bit width. A machine learning framework termed the information bottleneck method is used to design the corresponding lookup tables. In this paper, we extend this decoding principle from binary to non-binary codes. This is not a straightforward extension but requires a more sophisticated lookup table design to cope with the arithmetic in higher order Galois fields. Provided bit error rate simulations show that our proposed scheme outperforms the log-max decoding algorithm and operates close to sum-product decoding.

show abstract

Section: B the Information Bottleneck Methods And Relevant-informatiomentioning

confidence: 99%

Decoding of Non-Binary LDPC Codes using the Information Bottleneck Method

Stark¹,

Bauch²,

Lewandowsky³

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…So far, design of receiver subsystems with the proposed Information Bottleneck design approach has only been described for applications in LDPC decoding in this paper. However, the method can also be applied to other receiver sided signal processing such as channel estimation and detection [10], [13]. The applied principle is identical.…”

Section: Information Bottleneck Ldpc Decodingmentioning

confidence: 99%

“…Adjusting this parameter allows to easily trade preservation of relevant information for a more compact representation of the system output and thus a smaller bit width in the hardware. In [10], [13] we have presented complete signal processing chains performing channel estimation, detection and decoding by processing only unsigned integers with the proposed method. Their performance is very close to double precision receivers.…”

Section: Information Bottleneck Ldpc Decodingmentioning

confidence: 99%

“…These LUTs are designed in a relevant information preserving manner with the Information Bottleneck method. Works using similar approaches to a number of signal processing problems have been published so far, e.g., in [5]- [10]. Despite in this paper, we stick with LDPC decoding, these works show that the described method is generic and can also be applied to other receiver sided signal processing tasks, such that finally receivers which implement all signal processing with concatenated information preserving LUTs can be built.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Design and Evaluation of Information Bottleneck LDPC Decoders for Digital Signal Processors

Lewandowsky

Bauch

Tschauner

et al. 2019

IEICE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

Receiver implementations with very low quantization resolution will play an important role in 5G, as high precision quantization and signal processing are costly in terms of computational resources and chip area. Therefore, low resolution receivers with quasi optimum performance will be required to meet complexity and latency constraints. The Information Bottleneck method allows for a novel, information centric approach to design such receivers. The method was originally introduced by Naftali Tishby et al. and mostly used in the machine learning field so far. Interestingly, it can also be applied to build surprisingly good digital communication receivers which work fundamentally different than stateof-the-art receivers. Instead of minimizing the quantization error, receiver components with maximum preservation of relevant information for a given bit width can be designed. All signal processing in the resulting receivers is performed using only simple lookup operations. In this paper, we first provide a brief introduction to the design of receiver components with the Information Bottleneck method. We keep referring to decoding of lowdensity parity-check codes as a practical example. The focus of the paper lies on practical decoder implementations on a digital signal processor which illustrate the potential of the proposed technique. An Information Bottleneck decoder with 4 bit message passing decoding is found to outperform 8 bit implementations of the well-known min-sum decoder in terms of bit error rate and to perform extremely close to an 8 bit belief propagation decoder, while offering considerably higher net decoding throughput than both conventional decoders.

show abstract

“…Due to the system-oriented design, finding the most efficient structure which optimizes the flow of relevant information can be quite cumbersome [15]. Therefore, information bottleneck graphs were introduced in [27] which extend factor graphs [28] by a compact description of the flow of relevant information to provide a vivid graphical representation of the system. Information bottleneck graphs indicate where the use of information-bottleneck clusterings is beneficial, and thus help to simplify the design [27].…”

Section: Information-bottleneck Signal Processing and Information Botmentioning

confidence: 99%

Information-Bottleneck Decoding of High-Rate Irregular LDPC Codes for Optical Communication Using Message Alignment

2018

Self Cite

View full text Add to dashboard Cite

In high-throughput applications, low-complexity and low-latency channel decoders are inevitable. Hence, for low-density parity-check (LDPC) codes, message passing decoding has to be implemented with coarse quantization—that is, the exchanged beliefs are quantized with a small number of bits. This can result in a significant performance degradation with respect to decoding with high-precision messages. Recently, so-called information-bottleneck decoders were proposed which leverage a machine learning framework (i.e., the information bottleneck method) to design coarse-precision decoders with error-correction performance close to high-precision belief-propagation decoding. In these decoders, all conventional arithmetic operations are replaced by look-up operations. Irregular LDPC codes for next-generation fiber optical communication systems are characterized by high code rates and large maximum node degrees. Consequently, the implementation complexity is mainly influenced by the memory required to store the look-up tables. In this paper, we show that the complexity of information-bottleneck decoders remains manageable for irregular LDPC codes if our proposed construction approach is deployed. Furthermore, we reveal that in order to design information bottleneck decoders for arbitrary degree distributions, an intermediate construction step which we call message alignment has to be included. Exemplary numerical simulations show that incorporating message alignment in the construction yields a 4-bit information bottleneck decoder which performs only 0.15 dB worse than a double-precision belief propagation decoder and outperforms a min-sum decoder.

show abstract

Information Bottleneck Graphs for receiver design

Cited by 22 publications

References 11 publications

Decoding of Non-Binary LDPC Codes using the Information Bottleneck Method

Decoding of Non-Binary LDPC Codes using the Information Bottleneck Method

Design and Evaluation of Information Bottleneck LDPC Decoders for Digital Signal Processors

Information-Bottleneck Decoding of High-Rate Irregular LDPC Codes for Optical Communication Using Message Alignment

Contact Info

Product

Resources

About