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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.

Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is designated for multiple users, which aims to optimize the distribution of network resources. This network’s main intent is to maximize system performance by improving energy efficiency. However, the users of MIMO need many resources for effective operation. Hence, deep learning (DL) techniques are developed in this 5G network field to attain better reliability and accuracy during resource allocation. Therefore, this paper introduces a hippo graylag goose optimization with XCovNet (HGGO_XCovNet) for resource allocation. Firstly, a base station (BS) with multiple users is considered and the resource allocation is carried out by considering various objective functions, namely signal-interference noise ratio (SINR), data rate, and power consumption. Moreover, the resource allocation is performed by employing a DL model called XCovNet, where Xception convolutional neural network (XCovNet) is trained using the proposed hippo graylag goose optimization (HGGO). Further, the HGGO is formulated by the combination of greylag goose optimization (GGO) and hippopotamus optimization algorithm (HO). Furthermore, the HGGO_XCovNet technique measured a maximum energy efficiency of 74.943 kbits/joule, a sum rate of 269.93 Mbps, and throughput of 551.262 Mbps.

A constrained joint dictionary learning (CJDL) algorithm for high‐precision channel representation in massive multiple input multiple output (MIMO) satellite systems is proposed. Furthermore, taking into account the angular reciprocity of massive MIMO satellite systems, joint dictionary learning can establish a common support basis for both uplink and downlink. Previous deterministic dictionary has utilized deterministic basis, such as discrete Fourier transform (DFT) or orthogonal DFT (ODFT) basis, which tend to represent noise interference as part of channel characteristics. Furthermore, this deterministic dictionary is not able to adapt to dynamic communication environments. However, dictionary learning has shown the potential to significantly improve the accuracy of channel representation. Nevertheless, current research on training dictionary lacks analysis regarding constraints and boundary requirements, resulting in a suboptimal basis. To address this issue, conditional constraints associated with joint dictionary for channel representation are analysed. To screen for optimal basis, the joint dictionary is subject to constraints, including uplink and downlink constraints. Furthermore, the authors aim to quantify the maximum boundary of joint dictionary learning. Additionally, a joint dictionary updating method with singular value decomposition under constraint boundary conditions is proposed. Simulation results demonstrate that the proposed CJDL algorithm provides a more accurate and robust channel representation. Current research on training dictionary lacks analysis regarding constraints and boundary requirements, resulting in a suboptimal basis. To address this issue, conditional constraints associated with joint dictionary for channel representation are analysed. A constrained joint dictionary learning algorithm for high‐precision channel representation in massive multiple input multiple output satellite systems is also proposed.

The challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly. We propose a proportion factor operator for couple dictionary learning to assess accuracy. Inspired by PF operator, we introduce an optimal couple dictionary learning algorithm using singular value decomposition to obtain dictionary updating, aiming at high‐precision representation.

Coding for MIMO Communication Systems is a comprehensive introduction and overview to the various emerging coding techniques developed for MIMO communication systems. The basics of wireless communications and fundamental issues of MIMO channel capacity are introduced and the space-time block and trellis coding techniques are covered in detail. Other signaling schemes for MIMO channels are also considered, including spatial multiplexing, concatenated coding and iterative decoding for MIMO systems, and space-time coding for non-coherent MIMO channels. Practical issues including channel correlation, channel estimation and antenna selection are also explored, with problems at the end of each chapter to clarify many important topics. A comprehensive book on coding for MIMO techniques covering main strategies Theories and practical issues on MIMO communications are examined in detail Easy to follow and accessible for both beginners and experienced practitioners in the field References at the end of each chapter for further reading Can be used with ease as a research book, or a textbook on a graduate or advanced undergraduate level course This book is aimed at advanced undergraduate and postgraduate students, researchers and practitioners in industry, as well as individuals working for government, military, science and technology institutions who would like to learn more about coding for MIMO communication systems.

This paper considers a distillation column used in heavy crude oil separation where pairings exhibit negative Niederlinski Index values, potentially leading to system instability. In this study, we address this issue by constructing a Relative Gain Array matrix from a transfer matrix of order 3. We employ mathematical techniques to steer the system towards stability. Through subtle modifications to matrix entries, we achieve stable configurations.

The minimum mean square error (MMSE) signal detection algorithm is near-optimal for uplink multi-user large-scale multiple-input–multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from 𝒪(K3) to 𝒪(K2), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

In frequency-division duplex massive multiple-input multiple-output system, the base station relies on the downlink channel state information provided by the user equipment to optimize the system performance. However, due to the sheer size of the channel matrix, direct feedback of complete channel information would result in significant overhead. To minimize overhead and enhance feedback efficiency, this paper proposes a new deep neural network architecture MRWANet, which cleverly integrates an attention mechanism with a multi-scale convolution module. The multi-scale convolution module captures key features at varying granularities of channel information, while the attention mechanism precisely assesses their significance for the downstream task, jointly enabling efficient and precise channel information feedback. Experiments based on COST2100 channel data show that MRWANet has distinct advantages compared with traditional compressive sensing methods and conventional deep learning methods. Even under a low compression ratio, it can maintain a high level of feedback accuracy.

In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, block diagonalisation (BD) is a well-known precoding technique that eliminates inter-user interference. The number of simultaneously supportable users with BD is limited by the number of base station transmit antennas and the number of user receive antennas. Traditional MU-MIMO scheduling algorithms focus on sum capacity. However, these user scheduling algorithms might not be optimal with respect to energy efficiency. Here, the authors consider the energy-efficient MU-MIMO scheduling that maximises the energy efficiency. The brute-force search for the optimal user set, however, is computationally prohibitive. Therefore they propose a low-complexity user scheduling algorithm for energy-efficient MU-MIMO systems. They first obtain an approximation expression for the energy efficiency by utilising the upper bound of the MU-MIMO system capacity. Then they show that the maximum energy efficiency can be achieved if the scheduling user set is selected to obtain the largest matrix volume. The numerical results show that the proposed algorithm achieves a good tradeoff between energy efficiency and computational complexity. They also consider the problem of maintaining fairness among users, and propose a simplified proportional fair (PF) scheduling algorithm.

The development of 6G communication networks necessitates transformative advancements in massive MIMO systems to accommodate escalating data traffic and user demands. Different issues faced by the classical MIMO models are higher computational complexity, poor adaptability to dynamic environments, and suboptimal spectral-energy trade-offs. Classical algorithms often suffer from high computational complexity, limited adaptability to dynamic channel conditions, and suboptimal spectral-energy efficiency trade-offs. The primary objective of the research is to develop a hybrid precoding design using deep learning to optimize resource allocation and antenna selection in massive MIMO systems. Unlike classical telecommunication approaches, the implemented approach may achieve a superior trade-off between spectral and energy efficiency, setting a new benchmark for intelligent precoding strategies. Hence, to tackle several issues that takes place in the prior massive MIMO in 6G, a novel deep learning-based framework is designed by optimizing spectral and energy balancing in the 6G network for enhanced communication. In this research work, better spectral and energy balancing is performed using a novel technique, an Adaptive Residual Recurrent Neural Network (ARes-RNN), which is efficient to learn the structural information of the MIMO system along with the design of hybrid precoders. The applied Enhanced Dung Beetle Optimizer (EDBO) algorithm is used to optimize ARes-RNN parameters, enhancing the network’s learning ability and performance. Unlike the conventional models, the presented ARes-RNN model attained a spectral efficiency of approximately 79.4% for the SNR variation of 25 dB. The method shows improved energy and spectral efficiency balance, reduced computational complexity, and higher throughput. The performance of the 6G network in the massive MIMO is increased by the proposed deep learning with optimized parameters. The method achieves better spectral energy balance and is suitable for future wireless communication networks when compared to other classical approaches already existing in this domain.