Method of improving of soft multithreshold decoder's performance for self-orthogonal codes is considered. A modified way of selecting weights coefficients on initial iterations of multithreshold decoding (MTD) at high noise is described. Some simulated results obtained by using these two decoding techniques (the base and modified ways) are presented as well. Comparison of the results showed that the change in the computing of weights coefficients can significantly improve decoding parameters of a convolutional code with soft MTD. It's shown that MTD is in many cases as effective as an optimum decoder. MTD implementation complexity is also discussed. MTD is characterized with a very small number of operations, soft versions of these decoders can correct in channels with high enough noise level streams of the data in high-speed communication systems.

Multithreshold decoder (MTD) is the simplest type of majority decoder that decodes self-orthogonal codes. Low computational complexity and simple decoding hardware implementation of multithreshold decoders allow using them in high speed communication systems and data storage systems that require decoding information at speeds above 1 Gbit/s. A high-speed software binary multithreshold decoder using a modern computing power of the graphics processing unit (GPU) is considered. Such software implementation of MTD helps to decode several hundred blocks received from the channel completely parallel and independent from each other. It is shown that the speed of software MTD based on GPU GTX 970 can reach 350 Mbit/s.

The rapid progress in artificial intelligence technologies in recent years has been largely driven by advances in reinforcement learning (RL). RL methods have proven to be highly effective in solving many practical problems. Distributed ledger technologies are finding wide application in the internet of things (IoTs), providing new approaches to solving problems of traditional IoT systems. Consensus is a fundamental component of distributed ledger technologies, responsible for ensuring data consistency between nodes, its security and accuracy. This paper is devoted to the study of the optimal choice of blockchain consensus protocol for IoT networks based on a combination of multi-criteria decision making (MCDM) and RL methods. The paper discusses the potential of merging MCDM and RL methods for selecting blockchain consensus protocols in IoT networks. It suggests a combined framework for effective protocol selection and management.

Данная работа посвящена вопросу выбора протокола консенсуса блокчейна для сетей Интернета вещей с помощью системы многокритериального принятия решений. В последние годы можно наблюдать рост публикаций на тему блокчейна и технологий распределенного реестра. Поскольку проблема консенсуса и ее решение является фундаментальной составляющей этих технологий, закономерным представляется и увеличение количества работ, посвященных разработке, анализу, сравнению и выбору протоколов консенсуса. В данной работе выполнен обзор источников, связанных с анализом, сравнением и выбором протоколов консенсуса блокчейна. В рассматриваемых работах в качестве основного метода принятия решений при сравнении и выборе протоколов консенсуса использовался бенчмаркинг, то есть сравнение и тестирование в соответствии с набором определенных критериев оценки. В работе рассматривается применение многокритериального принятия решений в области технологий распределенного реестра и блокчейна, в частности, для выбора протокола консенсуса блокчейна для Интернета вещей.