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JCNN-Based DD Hybrid Precoding Algorithm With TPSAS Structure for FD-mimo systems

IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第2期13卷 357-361页

作者： Shi, Baozhu Liu, Fulai Du, Ruiyan Northeastern Univ Sch Comp Sci & Engn Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Lab Key Technol Millimeter Wave Large Scale MIMO S Qinhuangdao 066004 Peoples R China

This letter presents a joint convolutional neural network (JCNN)-based double dimensional (DD) hybrid precoding algorithm for adaptive fully-connected subarray structure with two-layer shared phase shifters (TPSAS) full-dimensional multiple-input multiple-output (FD-mimo) systems. Firstly, a TPSAS structure is developed to reduce hardware complexity. Then, a novel JCNN is designed to learn predicting the vectorized hybrid precoder with the vertical phase subnetwork (VpsCNN) and horizontal phase subnetwork (HpsCNN). Furthermore, the JCNN is trained by a label and a two-stage training strategy. In the first stage, minimizing residual between hybrid precoder and the label is adopted to train the JCNN. Specially, two distinct network layers are applied to meet constraints of VpsCNN and HpsCNN, respectively, including unit modulus and custom phase layers. In the second stage, estimated channel matrices are sent into the well-trained JCNN to simultaneously obtain the vertical precoder, horizontal precoder, and digital precoder. The theoretical analyses and simulation results verify that the proposed algorithm better trades off between spectral efficiency and hardware complexity than other algorithms while enhancing energy efficiency obviously.

关键词： Radio frequency Precoding Optimized production technology Hardware Phase shifters Antennas Power demand Double dimensional hybrid precoding joint convolutional neural network energy efficiency adaptive fully-connected subarrays two-layer shared phase shifter

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CNN-Based Local Quantized Hybrid Precoding for Low Complexity and Overhead

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第3期36卷

作者： Liu, Fulai Shi, Huiyang Du, Ruiyan Northeastern Univ Qinhuangdao Lab Key Technol Millimeter Wave Large Scale MIMO S Qinhuangdao Peoples R China Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

Hybrid precoding is one of the promising technologies for millimeter-wave (mmwave) massive multiple-input multiple-output (mimo) systems. Traditional hybrid precoding algorithms often suffer from high computational costs because the massive mimo systems have a large number of antennas. For this purpose, this paper proposes a convolutional neural network (CNN)-based local quantized hybrid precoding for low complexity and overhead. Firstly, a local quantized hybrid precoding approach is proposed to construct a label of the CNN framework under the lower complexity and feedback overhead. The proposed local quantized approach locally quantizes the feasible sets of the analog precoders to reduce feedback overhead according to the sparsity of the mmwave channel in the angular domain. Secondly, a new spectral efficiency-feedback overhead is defined to determine the range of local quantization bits b$$ b $$, so that the unnecessary feedback overhead can be avoided effectively while the spectral efficiency (SE) of the label is guaranteed. Finally, in order to further reduce complexity and feedback overhead, as well as make full use of the sparsity of the channel, a new CNN framework is built to enhance the spectrum efficiency of the system. Specifically, the mmwave channel and the label are used as the input-output pairs of the CNN framework, convolutional layers are employed to capture certain sparse characteristics from the angular domain of the channel. Due to the establishment of the input-output pairs of the CNN framework, the complexity of the CNN is effectively reduced. Compared with the previous works, the presented method enjoys less training time-consuming, lower feedback overhead, and higher precision. The simulation results are presented verifying the effectiveness of the proposed method.

关键词： convolutional neural network hybrid precoding millimeter wave spatial lobes channel spectral efficiency

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High Energy Efficiency Dynamic Connected Hybrid Precoding for mm wave Massive mimo systems

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China Communications 2024年第5期21卷 36-44页

作者： Du Ruiyan Liu Huajing Li Tiangui Liu Fulai Lab of Key Technology of Millimeter-wave Large-scale MIMO System Northeastern University at QinhuangdaoQinhuangdao066004China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC ***,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation ***,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound ***,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is *** results show that the HEDC structure enjoys low power consumption and satisfactory spectral *** proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

关键词： energy efficiency hybrid precoding mmwave optimized resolution phase shifter

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Multi-classification algorithm based on graph convolutional neural network for intrusion detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第6期19卷 1-8页

作者： Du, Ruiyan Huang, Minghui Liu, Fulai Northeastern Univ Qinhuangdao Lab Key Technol Millimeter Wave Large Scale MIMO S Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Intrusion detection, as an active network security protection technology, has become an important part of the network security field. However, with the increasing complexity and diversification of network attacks, it is a challenging topic to design an intrusion detection scheme with high detection accuracy and strong ability to detect unknown attack types. Therefore, this paper proposes a graph convolutional neural network (GCN)-based multi-classification network intrusion detection algorithm, named as GCNID algorithm. Firstly, in order to better represent the similarity and correlation between detection data, the preprocessed detection data is transformed into an undirected graph structure by using the k nearest neighbor method. Subsequently, a multi-classification network based on three-layer GCN is constructed to enhance the detection performance. Specifically, information from adjacent nodes is integrated into the central node through the graph convolutional layers of GCN, so that effective identification features can be extracted from the intrusion data, and the intrusion data can be identified and classified according to the obtained features. Simulation results indicate that the proposed algorithm not only enhances the precision of detection but also effectively categorizes unknown attack varieties.

关键词： Intrusion detection Multi-classification network k nearest neighbor method Graph convolution neural network

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AFC-TH Hybrid Precoding With High Energy Efficiency for millimeter wave Communication systems

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2024年第4期8卷 1622-1631页

作者： Du, Ruiyan Li, Long Guan, Zhiqing Shi, Baozhu Duan, Zhuoyao Liu, Fulai Northeastern Univ Qinhuangdao Lab Key Technol Millimeter Wave Large Scale MIMO S Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

As a structure with lower power consumption and hardware cost, the adaptive fully-connected (AFC) structure can improve energy efficiency significantly for hybrid precoding. Based on this, this paper proposes an energy-efficient AFC Tomlinson-Harashima (AFC-TH) hybrid precoding algorithm. Specifically, to alleviate inter-user interference and enhance system performance, a TH algorithm based on the mean square error minimization of signals (S-MMSE-TH) is proposed to obtain the optimal digital precoding matrix. Furthermore, to reduce the computational complexity of optimization the switch precoding matrix, a branch-and-bound based on subspace projection method is developed. Different from the traditional branch-and-bound method by the convex relaxation, the subspace projection method is used to obtain a closed-form lower bound of the objective function. It can reduce the computational cost effectively. Moreover, according to the norm and the nonlinear programming theories, a phase shifters precoding matrix optimization method is proposed. In this method, the phase-shifter precoding matrix optimization problem is transformed into a positive semidefinite quadratic form satisfying the KKT condition. Especially, a diagonal loading factor is introduced to satisfy the KKT condition to reduce the computational cost. Simulation results and complexity analysis show that the proposed method provides high energy efficiency while enjoying satisfactory spectral efficiency with a low computational complexity.

关键词： High energy efficiency adaptive architecture hybrid precoding hybrid precoding millimeter wave millimeter wave hybrid precoding millimeter wave

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JOPCNN-based hybrid precoding/combining algorithm for millimeter wave communication systems

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PHYSICAL COMMUNICATION 2025年 71卷

作者： Liu, Fulai Cao, Hong Wu, Yuchen Shi, Baozhu Du, Ruiyan Northeastern Univ Qinhuangdao Lab Key Technol Millimeter wave Large scale MIMO S Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Hebei Univ Coll Math & Informat Sci Baoding 071002 Peoples R China

To enhance the wideband hybrid precoding/combining (HPC) performance, this paper presents an antenna selection-based joint optimization HPC algorithm, namely JOPCNN. In the proposed algorithm, a JOPCNN framework is constructed to obtain the optimal analog precoder and combiner. Specially, the JOPCNN includes antenna selection convolutional neural network(ASCNN) and hybrid precoding/combining convolutional neural network (HPCCNN) subnetworks. Firstly, to improve spectral efficiency(SE) while reducing hardware cost and complexity of the system, the purpose of the ASCNN is to get the best antenna arrays. Subsequently, by employing the analog phase matrix as a label, the HPCCNN subnetwork is able to output the best analog precoder and combiner while adhering to the constant modulus restriction. Meanwhile, in order to better extract the amplitude, phase, and carrier-related information of the channel, a 3-dimensional convolutional neural network(3DCNN) is introduced into the proposed JOPCNN model. Finally, the digital coder can be solved by employing the resulting analog coder. Simulation findings indicate that the suggested approach performs better in SE than comparable algorithms.

关键词： millimeter-wave mimo Deep learning Wideband hybrid beamforming OFDM Antenna selection

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-mimo systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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