Multi-Kernel Polar Codes versus Classical Designs with Different Rate-Matching Approaches

Polar codes, which have been proposed as a family of linear block codes, has garnered a lot of attention from the scientific community, owing to their low-complexity implementation and provably capacity-achieving capability. Thus, they have been proposed to be used for encoding information on the control channels in the upcoming 5G wireless networks. The basic approach introduced by Arikan in his landmark paper to polarize bit channels of equal capacities to those of unequal capacities can be used to design only codewords of length N=2n, which is a major limitation when codewords of different lengths are required for the underlying applications. In the predecessor paper, this aspect was partially addressed by using a 3×3 kernel circuit (used to generate codewords of length M=3m), along with downsizing techniques such as puncturing and shortening to asses the optimal design and resizing techniques based on the underlying system parameters. In this article, we extend this research to include the assessment of multi-kernel rate-matched polar codes for applicability over a much wider range of codeword lengths.

An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

Deep Neural Networks Classification via Binary Error-Detecting Output Codes

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

Research on EDAC Schemes for Memory in Space Applications

Memory used for storing the configuration bitstream of field programmable gate array in space applications often encounters single event upset problems, which may disrupt the integrity of data in memory and lead to unpredictable failures. For commercial memories used in low Earth orbit (LEO), single-bit errors and double-byte errors account for a large proportion. Meanwhile, error detection and correction (EDAC) schemes, e.g., triple modular redundancy, linear block codes, memory scrubbing, and the combination of these schemes, are very popular in LEO missions. To further these works, a novel EDAC scheme with cascaded “Bose–Chaudhuri–Hocquenghem and cyclic redundancy check” codes and a proper scrubbing method is presented in this paper. The performance of the proposed design is measured and compared with state-of-the-art EDAC schemes in terms of hardware overhead, time overhead and error correction and detection capabilities. It is concluded that the proposed EDAC scheme is better suited for memory in space applications.

STOCHASTIC DECODING OF LINEAR BLOCK CODES USING CHECK MATRIX

Для итеративного декодирования на графах используется новый альтернативный подход - это стохастическое декодирование. Возможность стохастического декодирования была недавно предложена для декодирования LDPC-кодов. Эта статья расширяет применение стохастического подхода для декодирования линейных блочных кодов с помощью проверочных матриц (PCM), таких как коды Боуза - Чоудхури - Хоквингема (BCH), коды Рида - Соломона (RS) и блочные турбокоды на основе компонентов кодов BCH. Показано, как стохастический подход способен генерировать информацию мягкого выхода для итеративного декодирования с мягким входом и мягким выходом Soft - Input Soft - Output (SISO). Описывается структура стохастических переменных узлов высокой степени, используемых в кодах с помощью проверочных матриц PCM. Результаты моделирования для кода BCH (128, 120), кода RS (31, 25) и RS (63, 55) и турбокода блока BCH (256, 121) и (1024, 676) демонстрируют эффективность декодирования при закрытии к итеративному декодеру SISO с реализацией с плавающей запятой. Эти результаты показывают производительность декодирования, близкую к адаптивному алгоритму распространения доверия и/или турбо-ориентированному адаптированному алгоритму распространения доверия Stochastic decoding capability has recently been proposed for decoding LDPC codes. This paper expands on the application of the stochastic approach to decoding linear block codes using parity check matrices (PCMs) such as Bose-Chowdhury-Hawkingham (BCH) codes, Reed-Solomon (RS) codes, and BCH component-based block turbo codes. We show how the stochastic approach is able to generate soft-output information for iterative decoding with soft-input and soft-output Soft-Input Soft-Output (SISO). We describe the structure of high degree stochastic node variables used in codes using PCM parity check matrices. Simulation results for BCH code (128, 120), RS code (31, 25) and RS (63, 55), and BCH block turbo code (256, 121) and (1024, 676) demonstrate the decoding efficiency on close to SISO iterative decoder with floating point implementation. These results show decoding performance close to the adaptive trust propagation algorithm and / or turbo-oriented adapted trust propagation algorithm