IEICE Transactions on Communications最新文献_第6页

Multitarget 2-D DOA Estimation Using Wideband LFMCW Signal and Triangle Array Composed of Three Receiver Antennas 基于宽带LFMCW信号和三天线三角阵的多目标二维DOA估计

IF 0.7 4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2022ebp3084

Wentao Zhang, C. Miao, Wen Wu

引用次数: 0

Belief Propagation Detection with MRC Reception and MMSE Pre-cancellation for Overloaded MIMO 基于MRC接收和MMSE预抵消的过载MIMO信念传播检测

4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2023ebp3076

Yuto SUZUKI, Yukitoshi SANADA

引用次数: 0

Highly Efficient Multi-Band Optical Networks with Wavelength-Selective Band Switching 具有波长选择性频带交换的高效多波段光网络

IF 0.7 4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2022ebp3067

M. Nakagawa, H. Kawahara, T. Seki, T. Miyamura

{"title":"Highly Efficient Multi-Band Optical Networks with Wavelength-Selective Band Switching","authors":"M. Nakagawa, H. Kawahara, T. Seki, T. Miyamura","doi":"10.1587/transcom.2022ebp3067","DOIUrl":"https://doi.org/10.1587/transcom.2022ebp3067","url":null,"abstract":"SUMMARY Multi-band transmission technologies promise to cost-e ﬀ ectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed ﬁbers. While such technologies o ﬀ er a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node conﬁgurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the ﬁber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available beneﬁts to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C + L-band wavelength-division-multiplexed 112-Gb / s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.","PeriodicalId":48825,"journal":{"name":"IEICE Transactions on Communications","volume":"21 1","pages":"416-426"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74175262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Neural Networks Based End-to-End DOA Estimation System 基于深度神经网络的端到端DOA估计系统

4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2023cep0006

Daniel Akira ANDO, Yuya KASE, Toshihiko NISHIMURA, Takanori SATO, Takeo OHGANE, Yasutaka OGAWA, Junichiro HAGIWARA

{"title":"Deep Neural Networks Based End-to-End DOA Estimation System","authors":"Daniel Akira ANDO, Yuya KASE, Toshihiko NISHIMURA, Takanori SATO, Takeo OHGANE, Yasutaka OGAWA, Junichiro HAGIWARA","doi":"10.1587/transcom.2023cep0006","DOIUrl":"https://doi.org/10.1587/transcom.2023cep0006","url":null,"abstract":"Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.","PeriodicalId":48825,"journal":{"name":"IEICE Transactions on Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135357541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Mixing Probability Scheme in Mixed Gibbs Sampling MIMO Signal Detection 混合吉布斯采样MIMO信号检测中的自适应混合概率方案

4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2023ebp3018

Kenshiro CHUMAN, Yukitoshi SANADA

引用次数: 0

Information-Centric Function Chaining for ICN-based In-Network Computing in the Beyond 5G/6G Era 超5G/6G时代基于icn的网内计算以信息为中心的功能链

4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2023wwp0005

Yusaku HAYAMIZU, Masahiro JIBIKI, Miki YAMAMOTO

{"title":"Information-Centric Function Chaining for ICN-based In-Network Computing in the Beyond 5G/6G Era","authors":"Yusaku HAYAMIZU, Masahiro JIBIKI, Miki YAMAMOTO","doi":"10.1587/transcom.2023wwp0005","DOIUrl":"https://doi.org/10.1587/transcom.2023wwp0005","url":null,"abstract":"Information-Centric Networking (ICN) originally innovated for efficient data distribution, is currently discussed to be applied to edge computing environment. In this paper, we focus on a more flexible context, in-network computing, which is enabled by ICN architecture. In ICN-based in-network computing, a function chaining (routing) method for chaining multiple functions located at different routers widely distributed in the network is required. Our proposal is a twofold approach, On-demand Routing for Responsive Route (OR3) and Route Records (RR). OR3 efficiently chains data and multiple functions compared with an existing routing method. RR reactively stores routing information to reduce communication/ computing overhead. In this paper, we conducted a mathematical analytics in order to verify the correctness of the proposed routing algorithm. Moreover, we investigate applicabilities of OR3/RR to an edge computing context in the future Beyond 5G/6G era, in which rich computing resources are provided by mobile nodes thanks to the cutting-edge mobile device technologies. In the mobile environments, the optimum from viewpoint of “routing” is largely different from the stable wired environment. We address this challenging issue and newly propose protocol enhancements for OR3 by considering node mobility. Evaluation results reveal that mobility-enhanced OR3 can discover stable paths for function chaining to enable more reliable ICN-based in-network computing under the highly-dynamic network environment.","PeriodicalId":48825,"journal":{"name":"IEICE Transactions on Communications","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135953559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource Allocation for Mobile Edge Computing System Considering User Mobility with Deep Reinforcement Learning 考虑用户移动性的深度强化学习移动边缘计算系统资源分配

4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2023ebp3043

Kairi TOKUDA, Takehiro SATO, Eiji OKI

{"title":"Resource Allocation for Mobile Edge Computing System Considering User Mobility with Deep Reinforcement Learning","authors":"Kairi TOKUDA, Takehiro SATO, Eiji OKI","doi":"10.1587/transcom.2023ebp3043","DOIUrl":"https://doi.org/10.1587/transcom.2023ebp3043","url":null,"abstract":"Mobile edge computing (MEC) is a key technology for providing services that require low latency by migrating cloud functions to the network edge. The potential low quality of the wireless channel should be noted when mobile users with limited computing resources offload tasks to an MEC server. To improve the transmission reliability, it is necessary to perform resource allocation in an MEC server, taking into account the current channel quality and the resource contention. There are several works that take a deep reinforcement learning (DRL) approach to address such resource allocation. However, these approaches consider a fixed number of users offloading their tasks, and do not assume a situation where the number of users varies due to user mobility. This paper proposes Deep reinforcement learning model for MEC Resource Allocation with Dummy (DMRA-D), an online learning model that addresses the resource allocation in an MEC server under the situation where the number of users varies. By adopting dummy state/action, DMRA-D keeps the state/action representation. Therefore, DMRA-D can continue to learn one model regardless of variation in the number of users during the operation. Numerical results show that DMRA-D improves the success rate of task submission while continuing learning under the situation where the number of users varies.","PeriodicalId":48825,"journal":{"name":"IEICE Transactions on Communications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135953563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrusion Detection Model of Internet of Things Based on LightGBM 基于LightGBM的物联网入侵检测模型

IF 0.7 4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.2139/ssrn.3993056

Guo-sheng Zhao, Yang Wang, Jian Wang

{"title":"Intrusion Detection Model of Internet of Things Based on LightGBM","authors":"Guo-sheng Zhao, Yang Wang, Jian Wang","doi":"10.2139/ssrn.3993056","DOIUrl":"https://doi.org/10.2139/ssrn.3993056","url":null,"abstract":"SUMMARY Internet of Things (IoT) devices are widely used in various ﬁelds. However, their limited computing resources make them extremely vulnerable and di ﬃ cult to be e ﬀ ectively protected. Traditional intrusion detection systems (IDS) focus on high accuracy and low false alarm rate (FAR), making them often have too high spatiotemporal complexity to be deployed in IoT devices. In response to the above problems, this paper proposes an intrusion detection model of IoT based on the light gradient boosting machine (LightGBM). Firstly, the one-dimensional convolutional neural network (CNN) is used to extract features from network tra ﬃ c to reduce the feature dimensions. Then, the LightGBM is used for classi-ﬁcation to detect the type of network tra ﬃ c belongs. The LightGBM is more lightweight on the basis of inheriting the advantages of the gradient boosting tree. The LightGBM has a faster decision tree construction process. Experiments on the TON-IoT and BoT-IoT datasets show that the proposed model has stronger performance and more lightweight than the comparison models. The proposed model can shorten the prediction time by 90.66% and is better than the comparison models in accuracy and other performance metrics. The proposed model has strong detection capability for denial of service (DoS) and distributed denial of service (DDoS) attacks. Experimental results on the testbed built with IoT devices such as Raspberry Pi show that the proposed model can perform e ﬀ ective and real-time intrusion detection on IoT devices.","PeriodicalId":48825,"journal":{"name":"IEICE Transactions on Communications","volume":"16 1","pages":"622-634"},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82610810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Networking Experiment of Domain-Specific Networking Platform Based on Optically Interconnected Reconfigurable Communication Processors 基于光互联可重构通信处理器的特定领域网络平台组网实验

IF 0.7 4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2022ebp3131

Masaki Murakami, T. Kurimoto, S. Okamoto, N. Yamanaka, T. Muranaka

引用次数: 0

UE Set Selection for RR Scheduling in Distributed Antenna Transmission with Reinforcement Learning 基于强化学习的分布式天线传输RR调度UE集选择

IF 0.7 4区计算机科学

IEICE Transactions on Communications Pub Date : 2023-01-01 DOI: 10.1587/transcom.2022ebp3136

Go Otsuru, Y. Sanada

引用次数: 0