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Demodnet: Learning soft demodulation from hard information using convolutional neural network

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arXiv 2020年

作者： Zheng, Shilian Zhou, Xiaoyu Chen, Shichuan Qi, Peihan Yang, Xiaoniu Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China

Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption. © 2020, CC-BY.

关键词： Demodulation

A Spectrum Status Duration Prediction Method Based on Homotopy Theory for UAV communication 6

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A Spectrum Status Duration Prediction Method Based on Homoto...

6th IEEE International Conference on Computer and communications, ICCC 2020

作者： Zhou, Tao Luo, Shan Zheng, Peixiang Lin, Rongping School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728186351

With the development of wireless communication technology, a large number of spectrum resources are occupied and become increasingly scarce. If drones want to communicate, they need to share spectrum with others. The method of spectrum prediction in cognitive radio can efficiently achieve this. The traditional spectrum status duration prediction method based on the non-stationary hidden Markov model only predict the duration of one fixed geographic location and cannot meet the requirement of UAV's communications. We intend to apply the homotopy theory to the non-stationary hidden Markov model to obtain the spectrum status duration of different locations so that it can apply to UAV's communications. We also conducted experiments to predict the duration of the spectrum status at different locations. The experimental results show that our model can work well. © 2020 IEEE.

关键词： Hidden Markov models

A novel approach to reducing information leakage for quantum key distribution

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arXiv 2021年

作者： Mao, Hao-Kun Zhao, Qiang Qiao, Yu-Cheng Yan, Bing-Ze Xu, Bing-Jie Abd El-Latif, Ahmed A. Li, Qiong Department of Computer Science and Technology Harbin Institute of Technology Harbin150080 China College of Software Engineering ZhengZhou University of Light Industry ZhengZhou450053 China Guangxi Key Lab Cryptography & Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Quantum key distribution (QKD) is an important branch of quantum information science as it holds promise for unconditionally secure communication. For QKD research, a central issue is to improve the final secure key rate (SKR) and the maximal transmission distance. To address this issue, most works focused on reducing the information leakage of QKD. In this paper, we propose a novel approach to further reduce the information leakage by specially considering the overlap between the information leakage of quantum part and post-processing part. The overlap means that the information leakage of post-processing part caused solely by multi-photon pulses is considered twice in previous studies, i.e., both in quantum part and post-processing part. Since the information carried by multi-photon pulses has been considered as completely known by Eve through the photon-number-splitting attack in quantum part, there is no need to consider it in post-processing part repetitively during the SKR calculation. Therefore, our approach can theoretically reduce the information leakage of a QKD protocol. Based on this idea, we derive the formulas to calculate the amount of information leakage for decoy-BB84 and sending-or-not-sending twin-field protocols. Simulation results for these two typical protocols also demonstrate that our approach evidently improves the SKR as well as the maximal transmission distance under practical experimental parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

Discrete-Phase Waveform Design to Quadratic Optimization via an ADPM Framework with Convergence Guarantee

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Discrete-Phase Waveform Design to Quadratic Optimization via...

Sensor Array and Multichannel Signal Processing Workshop

作者： Xianxiang Yu Guolong Cui Zhenghong Zhang Lin Zhou Jing Yang Lingjiang Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.

关键词： Phased arrays NP-hard problem Signal processing algorithms Radar Numerical simulation Convex functions Iterative methods

Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

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arXiv 2022年

作者： Shu, Feng Chen, Yiwen Zhan, Xichao Cai, Wenlong Huang, Mengxing Jie, Qijuan Li, Yifang Shi, Baihua Wang, Jiangzhou You, Xiaohu Zhao, Xinyi The School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing210094 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Information and Communication Engineering Hainan University Haikou570228 China The National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing100854 China The School of Engineering University of Kent CanterburyCT27NT United Kingdom The School of Southeast University Nanjing211189 China

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Combining a massive HAD-MIMO with direction of arrival (DOA) will provide a high-precision even ultra-high-precision DOA measurement performance approaching the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we review three aspects: detection, estimation, and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to eliminate phase ambiguity using only one-snapshot. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB using single snapshot. Copyright © 2022, The Authors. All rights reserved.

关键词： Multilayer neural networks

A Novel Threat Sensing Architecture for Heterogeneous Cognitive Sensor Networks

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A Novel Threat Sensing Architecture for Heterogeneous Cognit...

2019 IEEE International Conference on Signal, information and Data Processing, ICSIDP 2019

作者： Ren, Yongji Zhang, Yun Zhou, Zheng Xu, Xiaofeng Liu, Xiaolei Naval Aviation University Yantai China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Yantai Vocational College Yantai China

ISBN: (纸本)9781728123455

With the rapid development and application of novel network information technologies, the various radar and/or communication transmitters have become more intelligent and networked in modern electromagnetic operational environment, which causes the threat sensing architecture of traditional electronic warfare system more inadequate, especially encountering unknown threat signals or networked targets. In this case, the threat sensing becomes one of the biggest challenges that the traditional electronic warfare system is facing. Therefore, this paper proposed a novel heterogeneous architecture for cognitive sensor networks to facilitate efficient threat sensing. To deal with the heterogeneous problem of cognitive sensor networks, multiple constraints satisfaction based approach has been employed. And the game based resources scheduling scheme has been utilized to facilitate the efficiency. To the best of our knowledge, our work is the first one that introduces the heterogeneous cognitive architecture into threat sensing domain of electronic warfare system. The simulation results show the proposed novel threat sensing architecture for heterogeneous cognitive sensor networks is feasible and effective. © 2019 IEEE.

关键词： Sensor networks

Top-down and bottom-up approaches to video Quality of Experience studies;overview and proposal of a new model

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arXiv 2023年

作者： Koniuch, Kamil Baraković, Sabina Husić, Jasmina Baraković De Moor, Katrien Janowski, Lucjan Wierzchoń, Michal Institute of Communication Technologies AGH University of Science and Technology Cracow Poland Faculty of Traffic and Communications University of Sarajevo Sarajevo Bosnia and Herzegovina Zmaja od Bosne bb Sarajevo71000 Bosnia and Herzegovina Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Zmaja od Bosne bb Sarajevo71000 Bosnia and Herzegovina Department of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway Consciousness Lab Institute of Psychology Jagiellonian University Kraków Poland Centre for Brain Research Jagiellonian University Kraków Poland Jagiellonian Human-Centered Artificial Intelligence Laboratory Jagiellonian University Kraków Poland

Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi)media researchers. They call this area of study "Quality of Experience" (QoE). In this paper, we present a review of QoE’s theoretical models together with a discussion of their properties and implications for the field. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them;therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. We hope that our framework will facilitate designing comparable experiments in the domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Quality assurance

Mainlobe Jamming Suppression Via Independent Component Analysis for Polarimetric SIMO Radar

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Mainlobe Jamming Suppression Via Independent Component Analy...

Sensor Array and Multichannel Signal Processing Workshop

作者： Mengmeng Ge Guolong Cui Zhenghong Zhang Lin Zhou Xianxiang Yu Feng Yang Lingjiang Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The presence of mainlobe jamming will significantly reduce the radar detection capabilities. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric SIMO (P-SIMO) radar to resist mainlobe jamming. Specifically, the signal model of P-SIMO radar accounting for the target and jamming signals is derived. Then, the approach based on ICA is utilized to separate the target component and jamming component while achieving the mainlobe jamming suppression. Finally, the effectiveness and capacities of proposed method are demonstrated by simulations, and the results show that the proposed method outperforms conventional ICA-based method.

关键词： Radar detection Radar Independent component analysis Resists Numerical simulation Radar polarimetry Radar signal processing

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning