ITU Journal on Future and Evolving Technologies最新文献_第10页

RFNet: Fast and efficient neural network for modulation classification of radio frequency signals RFNet:用于射频信号调制分类的快速有效的神经网络

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/xbpt2357

Mohammad Chegini, Pouya Shiri, Amirali Baniasadi

{"title":"RFNet: Fast and efficient neural network for modulation classification of radio frequency signals","authors":"Mohammad Chegini, Pouya Shiri, Amirali Baniasadi","doi":"10.52953/xbpt2357","DOIUrl":"https://doi.org/10.52953/xbpt2357","url":null,"abstract":"Automatic Modulation Classification (AMC) is a well-known problem in the Radio Frequency (RF) domain. Solving this problem requires determining the modulation of an RF signal. Once the modulation is determined, the signal could be demodulated making it possible to analyse the signal for various purposes. Deep Neural Networks (DNNs) have recently proven to be successful in solving this problem efficiently. However, since deep networks consist of several layers resulting in a high number of trainable parameters, the hardware implementations of these solutions are resource-demanding. In order to address this challenge, we propose an efficient deep neural network referred to as RFNet to tackle the AMC problem efficiently. This network introduces the novel Multiscale Convolutional (MSC) layer to extract robust features in different resolutions. In addition, the network takes advantage of several Separable Convolution Blocks (SCB). These blocks employ pointwise and depth-wise convolutions to reduce network complexity. We further introduce RFNet+ and RFNet++ as extensions of RFNet with fewer number of parameters. These variants include fewer floating-point operations and hence a lower hardware implementation cost. Experimental results using the challenging RadioML 2018 dataset show that RFNet-32++ achieves an average classification accuracy of 56.09% over all Signal-to-Noise Ratios (SNRs) and an accuracy of 92.21% in+20dB SNR using only 3.1K parameters. The small number of parameters makes the RFNet family a promising solution for future AMC systems.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133458740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

BacalhauNet: A tiny CNN for lightning-fast modulation classification BacalhauNet:用于闪电般快速调制分类的微型CNN

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/fywt4006

Jose Rosa, Daniel Granhao, Guilherme Carvalho, Tiago Gon�alves, Monica Figueiredo, Luis Conde Bento, Nuno Paulino, Luis M. Pessoa

引用次数: 0

Network resource allocation for emergency management based on closed-loop analysis 基于闭环分析的应急管理网络资源分配

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/hvpi8935

Guda Blessed, Ibrahim Aliyu, James Agajo, Thiago Lima Sarmento, Cleverson Veloso Nahum, Lucas Novoa, Rebecca Aben-Athar, Mariano Moura, Lucas Matni, Aldebaro Klautau, Deena Mukundan, Divyani R Achari, Mehmet Karaca, Doruk Tayli, �zge Simay Demirci, V. Udaya Sankar, Sai Jnaneswar Juvvisetty, V.M.V.S. Aditya, Abhishek Dandekar, Shabnam Sultana, Jinsul Kim, Vishnu Ram OV

{"title":"Network resource allocation for emergency management based on closed-loop analysis","authors":"Guda Blessed, Ibrahim Aliyu, James Agajo, Thiago Lima Sarmento, Cleverson Veloso Nahum, Lucas Novoa, Rebecca Aben-Athar, Mariano Moura, Lucas Matni, Aldebaro Klautau, Deena Mukundan, Divyani R Achari, Mehmet Karaca, Doruk Tayli, �zge Simay Demirci, V. Udaya Sankar, Sai Jnaneswar Juvvisetty, V.M.V.S. Aditya, Abhishek Dandekar, Shabnam Sultana, Jinsul Kim, Vishnu Ram OV","doi":"10.52953/hvpi8935","DOIUrl":"https://doi.org/10.52953/hvpi8935","url":null,"abstract":"The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop \"controllers\" in a declarative fashion (intent), triggering \"imperative actions\" in the \"underlay\" based on the intent, setup of a data pipeline between various components, and methods of \"influencing\" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124535766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-precision deep-learning-based automatic modulation recognition system 基于低精度深度学习的自动调制识别系统

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/ctyj2699

Satish Kumar, Aakash Agarwal, Neeraj Varshney, Rajarshi Mahapatra

{"title":"Low-precision deep-learning-based automatic modulation recognition system","authors":"Satish Kumar, Aakash Agarwal, Neeraj Varshney, Rajarshi Mahapatra","doi":"10.52953/ctyj2699","DOIUrl":"https://doi.org/10.52953/ctyj2699","url":null,"abstract":"Convolution Neural Network (CNN)-based deep learning models have recently been employed in Automated Modulation Classification (AMC) systems, with excellent results. However, hardware deployment of these CNN-based AMC models is very difficult due to their large size, floating point weights and activations, and real-time processing requirements in hardware such as Field Programmable Gate Arrays (FPGAs). In this study, we designed CNN-based AMC techniques for complex-valued temporal radio signal domains and made them less complex with a small memory footprint for FPGA implementation. This work mainly focuses on quantized CNN, low precision mathematics, and quantization-aware CNN training to overcome the problem of larger model sizes, floating-point weights, and activations. Low precision weights, activations, and quantized CNN, on the other hand, have a considerable impact on the accuracy of the model. Thus, we propose an iterative pruning-based training mechanism to maintain the overall accuracy above a certain threshold while decreasing the model size for hardware implementation. The proposed schemes are 21.55 times less complex and achieve at least 1.6% higher accuracy than the baseline. Moreover, results show that our convolution layer-based Quantized Modulation Classification Network (QMCNet) with pruning has 92.01% less multiply-accumulate bit operations (bit_operations), 61.39% less activation bits, and 87.58% less weight bits than the 8 bit quantized baseline model whereas the quantized and pruned Residual-Unit based model (RUNet) has 95.36% less bit_operations, 29.97% less activation bits and 98.22% less weight bits than the 8 bit quantized baseline model.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing RouteNet scalability through input and output design 通过输入和输出设计解决RouteNet的可扩展性

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/giod4389

Junior Momo Ziazet, Charles Boudreau, Brigitte Jaumard, Huy Duong

{"title":"Addressing RouteNet scalability through input and output design","authors":"Junior Momo Ziazet, Charles Boudreau, Brigitte Jaumard, Huy Duong","doi":"10.52953/giod4389","DOIUrl":"https://doi.org/10.52953/giod4389","url":null,"abstract":"With recent advances in the field of Machine Learning (ML), a multitude of problems related to communication systems and networks can be solved with data-driven solutions. Since data in these systems is mostly represented as graphs, Graph-based Neural Networks (GNNs) are a good candidate for solving such problems. These GNNs can be used as a computer network modeling technique to build models that accurately estimate the Key Performance Indicators (KPI) such as delay or jitter in real network scenarios in order to ensure their requirements in terms of service assurance. To build GNN solutions with higher accuracy, low computational resource requirements, and easy deployment of synthetic network training results into real-world networks, it is more than necessary to develop efficient and effective GNN models. This paper presents a GNN model capable of accurately estimating the average delay per flow in networks. By designing scale-independent features and using notions from queuing theory, the proposed model successfully generalizes to large size topologies, routing configurations, and traffic matrices not seen during the training phase.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134123799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-based indoor localization using mmWave MIMO channel at 60 GHz 基于人工智能的室内定位，使用60 GHz毫米波MIMO信道

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/aorf8087

Shubham Khunteta, Ashok Kumar Reddy, Avani Agrawal

{"title":"AI-based indoor localization using mmWave MIMO channel at 60 GHz","authors":"Shubham Khunteta, Ashok Kumar Reddy, Avani Agrawal","doi":"10.52953/aorf8087","DOIUrl":"https://doi.org/10.52953/aorf8087","url":null,"abstract":"In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130797047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

AI powered solution for radio link failure prediction based on link features and weather forecast 基于链路特征和天气预报的无线电链路故障预测的人工智能解决方案

ITU Journal on Future and Evolving Technologies Pub Date : 2022-09-22 DOI: 10.52953/odqq8049

Priyanshu M, Venkatesh Subramanya Iyer Giri, Shachi P, Geetishree Mishra, Suma M N

引用次数: 0

An edge abstraction layer enabling federated and hierarchical orchestration of CCAM services in 5G and beyond networks 一个边缘抽象层，支持在5G及以后的网络中对CCAM服务进行联合和分层编排

ITU Journal on Future and Evolving Technologies Pub Date : 2022-07-13 DOI: 10.52953/lnav1342

Mauro Femminella, Gianluca Reali

引用次数: 1

Decision tree-based radio link failure prediction for 5G communication reliability 基于决策树的5G通信可靠性无线电链路故障预测

ITU Journal on Future and Evolving Technologies Pub Date : 2022-07-13 DOI: 10.52953/lzlj8762

Nethraa Sivakumar, Pooja Srinivasan, Nikhil Viswanath, Venkateswaran N

{"title":"Decision tree-based radio link failure prediction for 5G communication reliability","authors":"Nethraa Sivakumar, Pooja Srinivasan, Nikhil Viswanath, Venkateswaran N","doi":"10.52953/lzlj8762","DOIUrl":"https://doi.org/10.52953/lzlj8762","url":null,"abstract":"Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. Network disruption may occur due to unexpected variations in environmental conditions such as weather, wind, and natural or man-made surroundings, and the influence of the defect is quite severe. Prediction of such undesirable events at a low cost can boost 5G communication reliability, massive network capacity, and decreased latency. This research work makes use of novel preprocessing and feature engineering techniques, followed by a trained decision tree model to predict the occurrence of Radio Link Failure (RLF). This system is designed to predict RLF for not just the next day, but also any of the next 5 days. This prediction supports reliance and increasing demand for good Internet connectivity. In order to achieve accurate RLF prediction, comprehensive data has been used which undergoes preprocessing. To account for the influence of surrounding weather conditions on radio links, the proposed system makes use of information from the past i.e., previous RLFs, and the information from the future i.e., the weather forecast from the weather station around the radio link station. The decision tree model was trained with the integration of feature engineering. A macro-averaged F1-score of 70% and 77% were obtained for RLF prediction for the next day and RLF prediction for the next 5 days, respectively. The results show improvement in performance after the incorporation of feature engineering in the pipeline. Further, an additional metric termed G-Mean is introduced in the paper. Owing to the high imbalance in the dataset, this metric was found to provide a more realistic representation of the results. The G-Mean score was found to be 98.69% and 92.89% for RLF prediction for the next day and RLF prediction for the next 5 days, respectively.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Neural network compression with feedback magnitude pruning for automatic modulation classification 基于反馈幅度修剪的神经网络压缩自动调制分类

ITU Journal on Future and Evolving Technologies Pub Date : 2022-07-13 DOI: 10.52953/eujf4214

Jakob Krzyston, Rajib Bhattacharjea, Andrew Stark

引用次数: 1