Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver.

STRATEGIES FOR CALCULATING PERSIMMETRIC CYCLIC QUASI-ORTHOGONAL MATRICES AS THE BASIS OF CODES

Выделяется циркулянт как основная циклическая структура квазиортогональных матриц, используемых в качестве основы для создания кодов модуляции сигналов в каналах связи с корреляционным приемом. Рассматриваются три стратегии вычисления персимметричных циклических квазиортогональных матриц и приводятся характеристики их программной реализации. This article highlights the circulant as the main cyclic structure of quasi-orthogonal matrices used as a basis for creating signal modulation codes in communication channels with correlation reception. Three strategies for computing persymmetric cyclic quasi-orthogonal matrices are considered and the characteristics of their software implementation are given.