本期目录

2020年 27卷 6期 刊出日期：2020-12-31



Wireless

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

刘旭 谢洋

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0041

摘要 ( 420 ) PDF (1326 KB)( 106 )

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Channel state information (CSI) is essential for downlink transmission in millimeter wave( mmWave) multiple input multiple output (MIMO) systems. Multi-panel antenna array is exploited in mmWave MIMO system due to its superior performance. Two channel estimation algorithms are proposed in this paper, named as generalized joint orthogonal matching pursuit (G-JOMP) and optimized joint orthogonal matching pursuit (O-JOMP) for multi-panel mmWave MIMO system based on the compressed sensing (CS) theory. G-JOMP exploits common sparsity structure among channel response between antenna panels of base station ( BS) and users to reduce the computational complexity in channel estimation. O-JOMP algorithm is then developed to further improve the accuracy of channel estimation by optimal panel selection based on the power of the received signal. Simulation results show that the performance of the proposed algorithms is better than that of the conventional orthogonal matching pursuit (OMP) based algorithm in multi-panel mmWave MIMO system.

Channel puncturing based low-complexity belief propagation detection for multi-user MIMO systems

蔡兴蔚 季伟 邱玲 梁晓雯

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0042

摘要 ( 379 ) PDF (1194 KB)( 95 )

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A low complexity punctured belief propagation ( BP) detection utilizing channel puncturing for multi-user

multiple-input multiple-output (MU-MIMO) systems is proposed in this paper. This paper constructs a cycle-free fator graph by puncturing certain non-zero entries in a transformed channel matrix, and proposes an adjusted BP algorithm with a more exact a posteriori message updating equation. The proposed algorithm converges rapidly in several iterations due to the cycle-free structure in the factor graph. Nevertheless, puncturing brings distorted noise and thus leads to performance degradation. To tackle this issue, this article further designs a layered detection with the help of maximum likelihood detector ( MLD). Simulations demonstrate that the proposed detection algorithm achieves the identical performance to MLD with much lower complexity.

Surveys on the intelligent surface: an innovative technology for wireless networks beyond 5G

张燕航

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0043

摘要 ( 297 ) PDF (1930 KB)( 155 )

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With the rapid development of wireless communication technology and the explosive growth of mobile data traffic, more and more users are eager to get faster and better internet access. In order to meet the needs of users, energy and spectrum utilization are becoming more and more important as new challenges in wireless communication networks. In recent years, reconfigurable intelligent surface ( RIS ) technology has been proposed in a programmable intelligent way to improve the performance and quality of wireless communication systems. In addition, the RIS performs better in terms of energy efficiency than other technologies. Therefore, the RIS has become research hotspot rapidly because of its unique wireless communication ability. This paper aims to review the RIS, including channel model, design for transmitter and receiver, information theory, and the latest development of RIS-assisted multiple-input multiple-output (MIMO) systems. The applications of RISs in physical layer security, device to device (D2D) and cell coverage extension are also introduced in detail. In addition, we discuss major research challenges related to the RIS. Finally, the potential research directions are proposed.

Cloud security situation prediction method based on grey wolf optimization and BP neural network

赵国生 刘冬梅 王健

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0044

摘要 ( 552 ) PDF (1385 KB)( 104 )

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Aiming at the accuracy and error correction of cloud security situation prediction, a cloud security situation

prediction method based on grey wolf optimization (GWO) and back propagation (BP) neural network is proposed. Firstly, the adaptive disturbance convergence factor is used to improve the GWO algorithm, so as to improve the convergence speed and accuracy of the algorithm. The Chebyshev chaotic mapping is introduced into the position update formula of GWO algorithm, which is used to select the features of the cloud security situation prediction data and optimize the parameters of the BP neural network prediction model to minimize the prediction output error. Then, the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm to increase the learning efficiency and accuracy of BP neural network. Finally, the real data sets of Tencent cloud platform are predicted. The simulation results show that the proposed method has lower mean square error (MSE) and mean absolute error (MAE) compared with BP neural network, BP neural network based on genetic algorithm (GA-BP), BP neural network based on particle swarm optimization (PSO-BP) and BP neural network based on GWO algorithm (GWO-BP). The proposed method has better stability, robustness and prediction accuracy.

Two-factor ( biometric and password) authentication key exchange on lattice based on key consensus

赵宗渠 马少提 王永军 汤永利 叶青

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0045

摘要 ( 462 ) PDF (549 KB)( 137 )

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In the post-quantum era, the password-based authentication key exchange (PAKE) protocol on lattice has the

characteristics of convenience and high efficiency, however these protocols cannot resist online dictionary attack that is a common method used by attackers. A lattice-based two-factor ( biometric and password) authentication key exchange (TFAKE) protocol based on key consensus (KC) is proposed. The protocol encapsulates the hash value of biometric information and password through a splittable encryption method, and compares the decapsulated information with the server's stored value to achieve the dual identity authentication. Then the protocol utilizes the asymmetric hash structure to simplify the calculation steps, which increases the calculation efficiency. Moreover, KC algorithm is employed in reducing data transmission overhead. Compared with the current PAKE protocol, the proposed protocol has the characteristics of hybrid authentication and resisting online dictionary attack. And it reduces the number of communication rounds and improves the efficiency and the security of protocol application.

Image Processing

Memristor-based multi-channel pulse coupled neural network for image fusion

刘俭 吴成茂 田小平

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2021.0046

摘要 ( 376 ) PDF (14654 KB)( 142 )

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Image fusion is widely used in computer vision and image analysis. Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion, a memristor-based multi-channel pulse coupled neural network (M-MPCNN) for image fusion is proposed. Based on a dual-channel pulse coupled neural network (D-PCNN), a novel multi-channel pulse coupled neural network (M-PCNN) is firstly constructed in this paper. Then the exponential growth dynamic threshold model is used to improve the pulse generation of pulse coupled neural network, which can not only avoid multiple ignitions effectively, but can also improve operational efficiency and reduce complexity. At the same time, synchronous capture can also enhance image edge, which is more conducive to image fusion. Finally, the threshold and synaptic characteristics of pulse coupled neural networks (PCNNs) can be well realized by using a memristor-based pulse generator. Experimental results show that the proposed algorithm can fuse multi-source images more effectively than existing state-of-the-art fusion algorithms.

Underdetermined mixing matrix estimation by comprehensive application of cluster validity indexes

王川川 姜林 曾勇虎 汪连栋

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0047

摘要 ( 320 ) PDF (3905 KB)( 123 )

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To solve the problem of mixing matrix estimation for underdetermined blind source separation (UBSS) when the number of sources is unknown, this paper proposed a novel mixing matrix estimation method based on average information entropy and cluster validity index (CVI). Firstly, the initial cluster center is selected by using fuzzy C-means (FCM) algorithm and the corresponding membership matrix is obtained, and then the number of clusters is obtained by using the joint decision of CVI and average information entropy index of membership matrix, then multiple cluster number estimation results can be obtained by using multiple CVIs. Then, according to the results of the number of multiple clusters estimation, the number of radiation sources is determined according to the principle of the subordination of the minority to the majority. The cluster center vectors obtained from the clustering operation of the estimated number of radiation sources are fused, that is the mixing matrix is estimated based on the degree of similarity of the cluster center vectors. When the source signal is not sufficiently sparse, the time-frequency single source detection processing can be combined with the proposed method to estimate the mixing matrix. The effectiveness of the proposed method is validated by experiments.

Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor

山蕊 蒋林 邓军勇 崔朋飞 张玉婷 吴皓月 谢晓燕

中国邮电高校学报(英文), 2020, 27 (6). doi： 10.19682/j.cnki.1005-8885.2020.0048

摘要 ( 365 ) PDF (5688 KB)( 161 )

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Object recognition in very high-resolution remote sensing images is a basic problem in the field of aerial and

satellite image analysis. With the development of sensor technology and aerospace remote sensing technology, the quality and quantity of remote sensing images are improved. Traditional recognition methods have a certain limitation in describing higher-level features, but object recognition method based on convolutional neural network (CNN) can not only deal with large scale images, but also train features automatically with high efficiency. It is mainly used on object recognition for remote sensing images. In this paper, an AlexNet CNN model is trained using 2 100 remote sensing images, and correction rate can reach 97.6% after 2 000 iterations. Then based on trained model, a parallel design of CNN for remote sensing images object recognition based on data-driven array processor (DDAP) is proposed. The consuming cycles are counted. Simultaneously, the proposed architecture is realized on Xilinx V6 development board, and synthesized based on SMIC 130 nm complementary metal oxid semiconductor (CMOS) technology. The experimental results show that the proposed architecture has a certain degree of parallelism to achieve the purpose of accelerating calculations.