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Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total network Power?

Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize ...

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ieee International conference on Communications (ICC)

作者： Demir, Ozlem Tugfe Masoudi, Meysam Bjornson, Emil Cavdar, Cicek KTH Royal Inst Technol Dept Comp Sci Kista Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9781538683477

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).

关键词： Cell-free massive MIMO virtualized CRAN network virtualization fronthaul transport

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A Preliminary Fuzzy Markup Language based Approach for the Queue Buffer Size Optimization in Fog Nodes for Stream Processing

A Preliminary Fuzzy Markup Language based Approach for the Q...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Corpas-Prieto, Gregorio Leon-Garcia, Fernando Gamez-Granados, Juan Carlos Manuel Palomares, Jose Olivares, Joaquin Soto-Hidalgo, Jose Manuel Univ Cordoba Dept Elect & Comp Engn Cordoba Spain Univ Granada Dept Comp Architecture & Technol Granada Spain

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

The Internet of Things (IoT) is usually divided in three layers: Edge, Fog, and Cloud layers. The whole IoT infrastructure deals with large amount of data between layers. Focusing on the Fog layer, the sending/receiving data and further cascade processing of those data in the Fog layer enable the Stream Processing paradigm. Thus, aspects such as the number of connections, delays, buffer size, memory usage, among others, have to be considered to optimize the network traffic. Moreover, these characteristics are affected by uncertainty and imprecision since, for example, the number of connections or the buffer size may be considered low in some cases and high in others. Fuzzy Rule -Based Systems (FRBS) are suitable for addressing complex data and managing their imprecision. The objective of this paper is to propose an approach that optimizes network traffic with the main goal of dynamically and automatically adjusting the queue buffer size in a node to avoid network collapse. The ieee std 1855-2016 for Fuzzy Markup Language and the open source library JFML are used for their flexibility and interoperability offered by these technologies. The proposal has been simulated in three basic different scenarios involving several network traffic states in a fog infrastructure.

关键词： Fuzzy logic Knowledge engineering Uncertainty Markup languages Telecommunication traffic ieee Standards Real-time systems

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Capturing Local and Global Spatial-Temporal Correlations of Spatial-Temporal Graph Data for Traffic Flow Prediction

Capturing Local and Global Spatial-Temporal Correlations of ...

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作者： Cao, Shuqin Wu, Libing Zhang, Rui Li, Jianxin Wu, Dan Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Guangdong Lab Artificial Intelligence & Digital E Guangzhou Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Traffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.

关键词： traffic prediction graph convolutional network spatial-temporal correlations Attention

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A Two-stage Stochastic programming Approach for Timetable Rescheduling with Reassignment of Rolling Stock under Uncertain Disruptions 25

A Two-stage Stochastic Programming Approach for Timetable Re...

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ieee 25th International conference on Intelligent Transportation Systems (ITSC)

作者： Liu, Xuan Zhou, Min Ma, Jianjun Wang, Bin Zhan, Shuguang Dong, Hairong Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China China Acad Railway Sci Co Ltd Inst Elect Comp Technol Beijing 100044 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Anhui Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

High-speed railways (HSRs) are susceptible to disruptions that can cause trains to deviate from their operation plan and, in severe cases, even disrupt the rolling stock plan. The duration of disruptions is often highly uncertain and unpredictable. It makes the timetable rescheduling problem even more challenging. This paper proposes an Event-Activity network-based rescheduling model to solve the timetable rescheduling problem for HSRs with the reassignment of rolling stock under uncertain disruption. To deal with the uncertainty, the uncertain disruption is regarded to have several possible deterministic scenarios. A two-stage stochastic programming approach is utilized to make a conflict-free rescheduling scheme for all possible scenarios within the minimum end time. A case study based on the practical data of the Beijing-Tianjin Intercity Railway is carried out to demonstrate the effectiveness of the proposed method. The results show that the proposed method can decrease the total delay and cancellations, and improve the robustness of rescheduling schemes.

关键词： High-speed railway Uncertain disruption Rolling stock Two-stage stochastic programming

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Detecting Layered Bottlenecks in Microservices 15

Detecting Layered Bottlenecks in Microservices

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15th ieee International conference on Cloud Computing (ieee CLOUD) / ieee World Congress on Services (ieee SERVICES)

作者： Inagaki, Tatsushi Ueda, Yohei Ohara, Moriyoshi Choochotkaew, Sunyanan Amaral, Marcelo Trent, Scott Chiba, Tatsuhiro Zhang, Qi IBM Res Tokyo Japan Meta Platforms Inc Menlo Pk CA USA

ISBN: (数字)9781665481373

ISBN: (纸本)9781665481373

We propose a method to detect both software and hardware bottlenecks in a web service consisting of microservices. A bottleneck is a resource that limits the maximum performance of the entire web service. Bottlenecks often include both software resources such as threads, locks, and channels, and hardware resources such as processors, memories, and disks. Bottlenecks form a layered structure since a single request can utilize multiple software resources and a hardware resource simultaneously. The microservice architecture makes the detection of layered bottlenecks challenging due to the lack of a uniform analysis perspective across languages, libraries, frameworks, and middle ware. We detect layered bottlenecks in microservices by profiling numbers and status of working threads in each microservice and dependency among microservices via network connections. Our approach can be applied to various programming languages since it relies only on standard debugging tools. Nevertheless, our approach not only detects which microservice is a bottleneck but also enables us to understand why it becomes a bottleneck. This is enabled by a novel visualization method to show layered bottlenecks in microservices at a glance. We demonstrate that our approach successfully detects and visualizes layered bottlenecks in the state-of-the-art microservice benchmarks, DeathStarBench and Acme Air microservices. This enables us to optimize the microservices themselves to achieve a higher throughput per resource utilization rate compared with simply scaling the number of replicas of microservices.

关键词： Visualization Computer languages Cloud computing Web services Instruction sets Microservice architectures Throughput

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MPGraf: a Modular and Pre-trained Graphformer for Learning to Rank at Web-scale 23

MPGraf: a Modular and Pre-trained Graphformer for Learning t...

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23rd ieee International conference on Data Mining (ieee ICDM)

作者： Li, Yuchen Xiong, Haoyi Kong, Linghe Sun, Zeyi Chen, Hongyang Wang, Shuaiqiang Yin, Dawei Shanghai Jiao Tong Univ Shanghai Peoples R China Baidu Inc Beijing Peoples R China Shanghai Jiao Tong Univ Yunnan Dali Res Inst Shanghai Peoples R China Zhejiang Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350307887

Both Transformer and Graph Neural networks (GNNs) have been used in learning to rank (LTR), they however adhere to two distinct-yet-complementary problem formulations, i.e., ranking score regression based on query-webpage pairs and link prediction within query-webpage bipartite graphs, respectively. Though it is possible to pre-train GNNs or Transformers on source datasets and fine-tune them subject to sparsely annotated LTR datasets separately, the source-target distribution shifts across the pairs and bipartite graphs domains make it extremely difficult to integrate these diverse models into a single LTR framework at a web-scale. We introduce the novel MPGraf model, which utilizes a modular and capsule-based pre-training approach, aiming to incorporate regression capacities from Transformers and link prediction capabilities of GNNs cohesively. Specifically, rather than simply combining these two modules in stacking or parallelizing architectures, MPGraf proposes a three step method to (1) construct query-webpage graphs from sparsely annotated query-webpage pairs, (2) pre-train the Transformer and GNN 'nodules using source datasets from two domains, and (3) "surgically" fine-tune the integrated models with pre trained weights while handling the distribution shifts of two domains. Extensive experiments have been done to evaluate the performance of MPGraf using real-world datasets collected from large-scale search engines. The results show that MPGraf can outperform baseline algorithms on several major metrics. Further, we deploy and evaluate MPGraf atop a large-scale search engine with realistic web traffics via A/B tests, where we can still observe significant improvement. MPGraf performs consistently in both offline and online evaluations.

关键词： Learning to Rank Modular network Pretraining Web Search

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Demonstrating Situational Awareness of Remote Operators with Edge Computing and 5G Standalone

Demonstrating Situational Awareness of Remote Operators with...

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ieee conference on Computer Communications Workshops, INFOCOM Wksps

作者： Vincent Charpentier Nina Slamnik-Kriještorac Xhulio Limani Joāo Francisco Nunes Pinheiro Johann Marquez-Barja IDLab - Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

In this paper, we demonstrate and introduce a novel Situational Awareness with Event-driven network programming Edge network Application (EdgeApp), designed to optimize network resource utilization during vessel teleoperation in congested port areas. The demonstration is conducted on an open real-life EdgeApp 5G Standalone (SA) and beyond testbed situated at the port of Antwerp-Bruges. Through this showcase, we demonstrate how 5G and beyond services, utilizing an open 5G SA testbed, can enhance vessel teleoperation. The proposed solution dynamically adjusts network configurations, allowing for lower-quality camera feeds during vessel autonomy and higher-quality feeds when in the teleoperation zone. The practical application and benefits are exemplified through visual representations within the testbed environment.

关键词： Industries Visualization Technological innovation 5G mobile communication conferences programming Feeds

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Accelerate Large Language Model Inference on Edge TPU with openVX framework

Accelerate Large Language Model Inference on Edge TPU with O...

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ieee International conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： You-En Wu Hsin-I Wu Kuo-Cheng Chin Yi-Chun Yang Ren-Song Tsay Taipei Fuhsing Private School Taiepi Taiwan DeepMentor Hsin Chu Taiwan Computer Science Hua National Tsing Hua University HsinChu Taiwan Computer Science National Tsing Hua University HsinChu Taiwan

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

This paper proposes a novel method to accelerate inference of Large Language Models (LLMs) on Edge Tensor Processing Units (TPUs) by leveraging the openVX framework. The approach harnesses the synergistic integration of openVX with Edge TPUs to enable parallel processing capabilities and address precision challenges in performing convolutions exclusively through integer arithmetic. Since LLMs, unlike Convolutional Neural networks (CNNs), cannot automatically construct network architectures via open Neural network Exchange (ONNX) parsing, manual network construction using the openVX API is necessitated. This demands precise understanding of each constituent component of the original model, as any inaccuracies propagate to entirely erroneous results. Through quantization, the proposed technique preserves accuracy during convolution operations while reusing the integer arithmetic convolution capabilities of Edge TPUs for efficient inference. The method showcases the collaborative synergy achieved between openVX and Edge TPUs, enhancing efficiency of LLM inference on resource-constrained edge devices.

关键词： Convolutional codes Tensors Runtime Quantization (signal) Large language models Prototypes programming

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A Deep Learning Method for Ship-Radiated Noise Recognition Based on MFCC Feature 7

A Deep Learning Method for Ship-Radiated Noise Recognition B...

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7th ieee International conference on Transportation Information and Safety, ICTIS 2023

作者： Hu, Fucai Fan, Jinyang Kong, Yiyang Zhang, Linke Guan, Xiaoxi Yu, Yongsheng Wuhan University of Technology School of Naval Architecture Ocean and Energy Power Engineering Wuhan China Transportation College of Jilin University Jilin University Changchun China Wuhan University of Technology State Key Laboratory of Silicate Materials for Architectures Wuhan China

ISBN: (纸本)9798350308532

Underwater target recognition is one of the most challenging tasks in underwater signal processing. Previous deep learning methods have relied on fusing more acoustic features, ignoring the rich information contained in the time-frequency features of underwater acoustics. Furthermore, fusing features that are less relevant to the target may result in redundancy and affect the recognition performance of the model. In this paper, a novel method based on Multi-Scale feature extraction, Attention mechanism for feature fusion and Convolutional Recurrent Neural network (MACRN) is proposed to fully exploit the deep features of Mel-Frequency Cepstral Coefficients (MFCC) for underwater target recognition. In comparative experiments conducted on the open-source dataset ShipsEar, the proposed method achieves an average recognition accuracy of 98.1% © 2023 ieee.

关键词： Underwater acoustics

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Transforming GP-CNN Tree Search Into Trainable architectures for Image Classification

Transforming GP-CNN Tree Search Into Trainable Architectures...

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ieee International conference on Systems, Man and Cybernetics

作者： Feng Sun Yan Ke Yue-Jiao Gong Yun Li Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China i4AI Ltd London WCIN3AX United Kingdom

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Data-efficient image classification poses a challenge in achieving effectiveness with limited data, as evidenced by the current methods based on convolutional neural networks (CNNs) and genetic programming (GP). Existing works employing these two methods encounter limitations, such as a lack of flexibility and an inability to effectively explore the latent features of the data. To tackle these challenges, this paper introduces a genetic programming method for data-efficient image recognition, leveraging novel function sets, terminal sets, and program structures. This method transforms tree-based data structures in GP into trainable CNN architectures. Further, by employing block structures instead of single operations in the search space, the search space is reduced and the stability of the search structures enhanced. Comparative experiments with state-of-the-art neural network methods and GP-based methods on data-efficient classification datasets validate the GP-CNN method offering higher performance.

关键词： Accuracy Image recognition Neural networks Genetic programming Transforms Feature extraction Data structures Convolutional neural networks Periodic structures Image classification

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