2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)最新文献_第5页

Reducing Transmission Delay in EDCA Using Policy Gradient Reinforcement Learning 利用策略梯度强化学习减少EDCA的传输延迟

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045621

Masao Shinzaki, Yusuke Koda, Koji Yamamoto, T. Nishio, M. Morikura

{"title":"Reducing Transmission Delay in EDCA Using Policy Gradient Reinforcement Learning","authors":"Masao Shinzaki, Yusuke Koda, Koji Yamamoto, T. Nishio, M. Morikura","doi":"10.1109/CCNC46108.2020.9045621","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045621","url":null,"abstract":"Towards ultra-reliable and low-latency communications, this paper proposes a packet mapping algorithm in an enhanced distributed channel access (EDCA) scheme using policy gradient reinforcement learning (RL). The EDCA scheme provides higher priority packets with more transmission opportunities by mapping packets to a predefined access category (AC); thereby, the EDCA scheme supports a higher quality of service in wireless local area networks. In this paper, it is noted that by mapping high priority packets to lower priority ACs, the one-packet delay of a high priority packet can be reduced. In contrast, the mapping algorithm cannot minimize the multiple-packets delay because the mapping algorithm is based on the current status. This is because, from a long-term perspective, mapping high priority packets is required as a countermeasure for collisions, to minimize the multiple-packets delay. As a solution, this paper proposes a new mapping algorithm using RL because RL is suitable for maximizing the reward from a long-term perspective. The key idea is to design the state such that the state involves the number of packets having arrived at each AP in the past, which is an indicator expressing past status. In the designed RL task, the reward, i.e., the multiple-packets delay depends on an overall sequence of states and actions; hence, the recursive value function-based RL algorithms are not compatible. To solve this problem, this paper utilizes policy gradient RL, which learns the packet mapping policy from an overall state-action sequence and a consequent multiple-packets delay. The simulation result reveals that the transmission delay of the proposed mapping algorithm is 13.8% shorter than that of the conventional EDCA mapping algorithm.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428110","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

A Realistic Approach to Enhance the Battery Performance of Device-to-Device (D2D) Relay UEs 一种提高器件对器件(D2D)继电器ue电池性能的现实方法

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045552

Anup Chaudhari, Jagadeesh Gandikota, Arijit Sen, S. Narayan

{"title":"A Realistic Approach to Enhance the Battery Performance of Device-to-Device (D2D) Relay UEs","authors":"Anup Chaudhari, Jagadeesh Gandikota, Arijit Sen, S. Narayan","doi":"10.1109/CCNC46108.2020.9045552","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045552","url":null,"abstract":"The unprecedented trends in Proximity-based services (ProSe) have intensified the need for the Device- to- Device (D2D) communication in the next generation 5G cellular networks. ProSe allows devices in the proximity to discover and communicate with each other directly. In Prose, a Relay User Equipment (UE) is a wireless communication device which enables UEs not in presence of network coverage to connect to the network by using sidelink channel. Relay UE executes periodic discovery procedures to enable other devices to know about its presence. These periodic discovery transmissions consume a significant power on the relay UE. In the presence of multiple relay UEs operating within a close proximity, most of these operations might be redundant as all the relay UEs are operated independently. In this work, we propose a novel solution to minimize the periodic discovery transmissions by establishing a co-ordinated relay discovery mechanism. Using extensive simulations, we have shown our proposed algorithm helps in reducing the energy consumption of relay UEs significantly by selecting operating modes appropriately.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678996","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: 8

Where to Go Next?: A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets 下一步该去哪里?:对HetNets中人工智能辅助移动预测器的现实评估

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045127

Marvin Manalastas, H. Farooq, Syed Muhammad Asad Zaidi, A. Imran

{"title":"Where to Go Next?: A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets","authors":"Marvin Manalastas, H. Farooq, Syed Muhammad Asad Zaidi, A. Imran","doi":"10.1109/CCNC46108.2020.9045127","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045127","url":null,"abstract":"5G is considered as the ecosystem to abet the ever growing number of mobile devices and users requiring an unprecedented amount of data and highly demanding Quality of Experience (QoE). To accommodate these demands, 5G requires extreme densification of base station deployment, which will result in a network that requires overwhelming efforts to maintain and manage. User mobility prediction in wireless communications can be exploited to overcome these foregoing challenges. Knowledge of where users will go next enables cellular networks to improve handover management. In addition, it allows networks to engage in advanced resource allocation and reservation, cell load prediction and proactive energy saving. However, anticipating the movement of humans is, in itself, a challenge due to the lack of realistic mobility models and insufficiencies of cellular system models in capturing a real network dynamics. In this paper, we have evaluated Artificial Intelligence (AI)-assisted mobility predictors. We model mobility prediction as a multi-class classification problem to predict the future base station association of the mobile users using Extreme Gradient Boosting Trees (XGBoost) and Deep Neural Networks (DNN). Using a realistic mobility model and a 3GPP-compliant cellular network simulator, results show that, XGBoost outperforms DNN with prediction accuracy reaching up to 95% in a heterogeneous network (HetNet) scenario with shadowing varied from OdB to 4dB.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123891238","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: 4

Prototype implementation of multi-hop communication scheme among smartphones based on BLE communication 基于BLE通信的智能手机间多跳通信方案原型实现

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045208

Toru Yoshizaki, Yuki Yamada, S. Sugiura, K. Naito

引用次数: 0

Machine Learning based Video Hosting Site Identification Method for MVNO Networks 基于机器学习的MVNO网络视频托管站点识别方法

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045105

Anan Sawabe, Tomoki Ito, Takanori Iwai

{"title":"Machine Learning based Video Hosting Site Identification Method for MVNO Networks","authors":"Anan Sawabe, Tomoki Ito, Takanori Iwai","doi":"10.1109/CCNC46108.2020.9045105","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045105","url":null,"abstract":"Zero-rating service provided by Mobile Virtual Network Operators (MVNOs) has been attracting smartphone users who frequently watch web videos that are delivered by heavily bandwidth-consuming applications. With the increase of encrypted traffic, MVNOs need to identify video hosting sites accessed by smartphone users via encrypted traffic analysis for enabling such services. If traffic from permitted sites is identified as coming from non-permitted sites due to mistaken identification of video hosting sites, unreasonable payments are inevitable for MVNOs or subscribers, and vice versa. In this paper we propose two feature sets considering multiple flow transmission and analyze the feature sets by supervised machine learning for identifying video hosting sites accurately. The first set is traffic features extracted from flows for only transmitting video contents. The second is 4-tuple distribution of established flows for transmitting various contents in a single video web page. These feature sets are based on our investigation of the characteristics of four of the most popular video hosting sites in Japan. Through video hosting site identification experiments, the identification accuracy of single flow analysis reaches 85.9%, and the accuracy of the proposed method reaches 92.0%.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139846","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

Distributed Decision Fusion With M -ary Source Coding On Sensor Observation and Uncoded Data Transmission 基于多源编码的传感器观测与无编码数据传输分布式决策融合

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045746

V. Cheng, Tsang-Yi Wang, Hao Wang

{"title":"Distributed Decision Fusion With M -ary Source Coding On Sensor Observation and Uncoded Data Transmission","authors":"V. Cheng, Tsang-Yi Wang, Hao Wang","doi":"10.1109/CCNC46108.2020.9045746","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045746","url":null,"abstract":"M -ary source coding scheme on sensor detection is able to improve the detection performance on the final decision made at the fusion center (FC). In this paper, a new $M$-ary source coding scheme using analog transmission is proposed for distributed binary detection. In the scheme, the source coding is through the quantization process, but the output of the quantizer is transmitted directly without digitalizing and coding process. In the FC, the linear combiner detection rule is adopted to make the final decision. The problem of huge channel bandwidth demand can be avoided by using the considered Mary source coding scheme. The goal of the proposed scheme is to minimize the decision errors at the FC via optimizing the region allocation. The error performances using maximum-a-posteriori (MAP) and equal-gain-combining (EGC) fusion rules are analyzed. The proposed $M$-ary source coding scheme is illustrated with numerical examples highlighting its significant improvement in error performance and enhanced information available at the FC when the transmission is via either additive white Gaussian noise (AWGN) or Rayleigh faded channel.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125407443","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

Efficient Load balancing to serve Heterogeneous Requests in Clustered Systems using Kubernetes 使用Kubernetes在集群系统中实现高效的负载平衡以服务异构请求

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045136

Amit Dua, Sahil Randive, Aditi Agarwal, Neeraj Kumar

引用次数: 3

Analytical Formulas for the Ergodic Capacity in Association of RRHs in Cloud Radio Access Networks 云无线接入网中RRHs关联遍历容量的解析公式

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045519

Ayad N. Bihnam, Xian Liu, Bilal Al-Doori

引用次数: 0

PEX: Privacy-Preserved, Multi-Tier Exchange Framework for Cross Platform Virtual Assets Trading PEX:用于跨平台虚拟资产交易的保护隐私的多层交换框架

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045515

Akkarit Sangpetch, Orathai Sangpetch

{"title":"PEX: Privacy-Preserved, Multi-Tier Exchange Framework for Cross Platform Virtual Assets Trading","authors":"Akkarit Sangpetch, Orathai Sangpetch","doi":"10.1109/CCNC46108.2020.9045515","DOIUrl":"https://doi.org/10.1109/CCNC46108.2020.9045515","url":null,"abstract":"In traditional virtual asset trading market, several risks, e.g. scams, cheating users, and market reach, have been pushed to users (sellers/buyers). Users need to decide who to trust; otherwise, no business. This fact impedes the growth of virtual asset trading market. In the past few years, several virtual asset marketplaces have embraced blockchain and smart contract technology to alleviate such risks, while trying to address privacy and scalability issues. To attain both speed and non-repudiation property for all transactions, existing blockchain-based exchange systems still cannot fully accomplish. In real-life trading, users use traditional contract to provide non-repudiation to achieve accountability in all committed transactions, so-called thorough non-repudiation. This is essential when dispute happens. To achieve similar thorough non-repudiation as well as privacy and scalability, we propose PEX, Privacy-preserved, multi-tier EXchange framework for cross platform virtual assets trading. PEX creates a smart contract for each virtual asset trading request. The key to address the challenges is to devise two-level distributed ledgers with two different types of quorums where one is for public knowledge in a global ledger and the other is for confidential information in a private ledger. A private quorum is formed to process individual smart contract and record the transactions in a private distributed ledger in order to maintain privacy. Smart contract execution checkpoints will be continuously written in a global ledger to strengthen thorough non-repudiation. PEX smart contract can be executed in parallel to promote scalability. PEX is also equipped with our reputation-based network to track contribution and discourage malicious behavior nodes or users, building healthy virtual asset ecosystem.","PeriodicalId":443862,"journal":{"name":"2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129375313","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

Application-Oriented Optical Transmission Control for Computationally Efficient Edge Computing 面向应用的高效边缘计算光传输控制

2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) Pub Date : 2020-01-01 DOI: 10.1109/CCNC46108.2020.9045620

Takahiro Suzuki, Sang-Yuep Kim, Y. Koyasako, J. Kani, J. Terada

引用次数: 3