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DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic communication

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arXiv 2023年

作者： Zhang, Wenyu Bai, Kaiyuan Zeadally, Sherali Zhang, Haijun Shao, Hua Ma, Hui Leung, Victor C.M. The School of Intelligence Science and Technology The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The China Telecom Research Institute Beijing100096 China The College of Communication and Information University of Kentucky LexingtonKY40506 United States Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes. © 2023, CC BY.

关键词： Signal to noise ratio

Movable Antennas-Assisted Secure Transmission Without Eavesdroppers’ Instantaneous CSI

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arXiv 2024年

作者： Hu, Guojie Wu, Qingqing Xu, Donghui Xu, Kui Si, Jiangbo Cai, Yunlong Al-Dhahir, Naofal College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China College of Communications Engineering Army Engineering University of PLA Nanjing210007 China Integrated Service Networks Lab of Xidian University Xi’an710100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas. © 2024, CC BY.

关键词： Beamforming

Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

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Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged P...

Conference on Lasers and Electro-Optics (CLEO)

作者： Jun Qin Qipeng Yang Changhao Han Yan Zhou Yunhao Zhang Yichen WU Yu Sun Junde Lu Siming Liu Yueqin Li Jian Sun Miao Min Weiwei Hu Zhixue He Lei Wang Shaohua Yu Haowen Shu Xingjun Wang Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Peking University Yangtze Delta Institute of Optoelectronics Nantong China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9798350369311

关键词： Electric potential Optical interconnections System performance Artificial neural networks Transceivers Silicon Electro-optical waveguides Photonics

Movable Antennas-Enabled Two-User Multicasting: Do We Really Need Alternating Optimization for Minimum Rate Maximization?

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arXiv 2024年

作者： Hu, Guojie Wu, Qingqing Xu, Donghui Xu, Kui Si, Jiangbo Cai, Yunlong Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The College of Communications Engineering Army Engineering University of PLA Nanjing210007 China The Integrated Service Networks Lab of Xidian University Xi’an710100 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Movable antenna (MA) technology, which can reconfigure wireless channels by flexibly moving antenna positions in a specified region, has great potential for improving communication performance. In this paper, we consider a new setup of MAs-enabled multicasting, where we adopt a simple setting in which a linear MA array-enabled source (S) transmits a common message to two single-antenna users U1 and U2. We aim to maximize the minimum rate among these two users, by jointly optimizing the transmit beamforming and antenna positions at S. Instead of utilizing the widely-used alternating optimization (AO) approach, we reveal, with rigorous proof, that the above two variables can be optimized separately: i) the optimal antenna positions can be firstly determined via the successive convex approximation technique, based on the rule of maximizing the correlation between S-U1 and S-U2 channels;ii) afterwards, the optimal closed-form transmit beamforming can be derived via simple arguments. Compared to AO, this new approach yields the same performance but reduces the computational complexities significantly. Moreover, it can provide insightful conclusions which are not possible with AO. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Two-Timescale Design for Movable Antenna Array-Enabled Multiuser Uplink communications

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arXiv 2024年

作者： Hu, Guojie Wu, Qingqing Xu, Donghui Xu, Kui Si, Jiangbo Cai, Yunlong Al-Dhahir, Naofal The College of Communication Engineering Rocket Force University of Engineering Xi’an710025 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The College of Communications Engineering Army Engineering University of PLA Nanjing210007 China The Integrated Service Networks Lab Xidian University Xi’an710100 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Movable antenna (MA) technology can flexibly reconfigure wireless channels by adjusting antenna positions in a local region, thus owing great potential for enhancing communication performance. This letter investigates MA technology-enabled multiuser uplink communications over general Rician fading channels, which consist of a base station (BS) equipped with the MA array and multiple single-antenna users. Since it is practically challenging to collect all instantaneous channel state information (CSI) by traversing all possible antenna positions at the BS, we instead propose a two-timescale scheme for maximizing the ergodic sum rate. Specifically, antenna positions at the BS are first optimized using only the statistical CSI. Subsequently, the receiving beamforming at the BS (for which we consider the three typical zero-forcing (ZF), minimum mean-square error (MMSE) and MMSE with successive interference cancellation (MMSE-SIC) receivers) is designed based on the instantaneous CSI with optimized antenna positions, thus significantly reducing practical implementation complexities. The formulated problems are highly non-convex and we develop projected gradient ascent (PGA) algorithms to effectively handle them. Simulation results illustrate that compared to conventional fixed-position antenna (FPA) array, the MA array can achieve significant performance gains by reaping an additional spatial degree of freedom. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Smart e-Health Security and Safety Monitoring with Machine Learning Services

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Smart e-Health Security and Safety Monitoring with Machine L...

International Conference on computer communications and networks (ICCCN)

作者： W. Liu E.K. Park U. Krieger S.S. Zhu School of Science and Technology GGC Associate Provost for Research NCCU Computer Science in Communication and Networks Otto-Friedrich University Bamberg Department of Computer Science Shantou University

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.

关键词： Monitoring Safety Medical services Machine learning Protocols

EVOLVE: towards converging big-data, high-performance and cloud-computing worlds 22

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EVOLVE: towards converging big-data, high-performance and cl...

Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe

作者： Achilleas Tzenetopoulos Dimosthenis Masouros Konstantina Koliogeorgi Sotirios Xydis Dimitrios Soudris Antony Chazapis Christos Kozanitis Angelos Bilas Christian Pinto Huy-Nam Nguyen Stelios Louloudakis Georgios Gardikis George Vamvakas Michelle Aubrun Christy Symeonidou Vassilis Spitadakis Konstantinos Xylogiannopoulos Bernhard Peischl Tahir Emre Kalayci Alexander Stocker Jean-Thomas Acquaviva Institute of Communication and Computer Systems (ICCS) Athens Greece Institute of Communication and Computer Systems (ICCS) Athens Greece and Harokopio University of Athens (HUA) Greece Institute of Computer Science FORTH Heraklion Greece IBM Research Europe Dublin Ireland ATOS/Bull Paris France Sunlight.io Heraklion Greece Space Hellas S.A. Athens Greece Thales Alenia Space Toulouse France NEUROCOM Luxembourg Luxembourg AVL List GmbH Graz Austria Virtual Vehicle Research GmbH Graz Austria DataDirect Networks Paris France

ISBN: (纸本)9783981926361

EVOLVE is a pan European Innovation Action that aims to fully-integrate High-Performance-Computing (HPC) hardware with state-of-the-art software technologies under a unique testbed, that enables the convergence of HPC, Cloud and Big-Data worlds and increases our ability to extract value from massive and demanding datasets. EVOLVE's advanced compute platform combines HPC-enabled capabilities, with transparent deployment in high abstraction level, and a versatile Big-Data processing stack for end-to-end workflows. Hence, domain experts have the potential to improve substantially the efficiency of existing services or introduce new models in the respective domains, e.g., automotive services, bus transportation, maritime surveillance and others. In this paper, we describe EVOLVE's testbed, and evaluate the performance of the integrated pilots from different domains.

关键词： resource orchestration HPC cloud-computing accelerators computing platform interference big-data

LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations

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arXiv 2020年

作者： Sliwa, Benjamin Piatkowski, Nico Wietfeld, Christian Communication Networks Institute Department of Computer Science AI Group TU Dortmund University Dortmund44227 Germany

Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advancements for IoT-based data analytics, there remains a huge methodological gap for the deployment phase of trained machine learning models. For given resource-constrained platforms such as Microcontroller Units (MCUs), model choice and parametrization are typically performed based on heuristics or analytical models. However, these approaches are only able to provide rough estimates of the required system resources as they do not consider the interplay of hardware, compiler-specific optimizations, and code dependencies. In this paper, we present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high-level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. The solid foundations of validated low-level model implementations are provided by the coupled well-established data analysis framework Waikato Environment for Knowledge Analysis (WEKA). We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification, where we compare different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB and demonstrate its potential to catalyze the development of machine learning-enabled IoT systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Data Analytics

A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

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A Horizontal Federated Learning Approach to IoT Malware Traf...

International Conference on Advanced communication Technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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