2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)最新文献_第2页

Stochastic Games with Sensing Costs 具有感知代价的随机对策

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8636069

M. Ahmadi, Suda Bharadwaj, Takashi Tanaka, U. Topcu

引用次数: 3

Accurate Rate-Aware Flow-level Traffic Splitting 准确的速率感知流级流量分割

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635940

N. Wu, Shih-Hao Tseng, A. Tang

{"title":"Accurate Rate-Aware Flow-level Traffic Splitting","authors":"N. Wu, Shih-Hao Tseng, A. Tang","doi":"10.1109/ALLERTON.2018.8635940","DOIUrl":"https://doi.org/10.1109/ALLERTON.2018.8635940","url":null,"abstract":"This paper aims to accurately realize given traffic split ratios in switches with small performance degradation. For given traffic split ratios calculated mathematically by TE algorithms in the control plane, the load distribution mechanisms in the data plane implement such splits without breaking flows. Treating all flows equally, the state-of-the-art approaches deployed in switches do not provide enough accuracy especially when facing non-uniform flow size distribution. We instead propose a dynamic load distribution scheme based on the collected load sharing statistics. It finds the most accurate traffic splits with minimum route changes. We implement our solution in Open vSwitch (OVS). Trace-driven and end-to-end experiments demonstrate that 1) our approach effectively adjusts load distribution in real time to mitigate the inaccuracy of splits caused by the variation of flow size distribution, 2) it outperforms the existing approaches with respect to both higher accuracy and lower level of route changes, and 3) it requires path changes for less flows when routing strategies are reconfigured, hence leads to better flow experience such as higher goodput.","PeriodicalId":299280,"journal":{"name":"2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131311738","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: 7

Localized random projections with applications to coherent array imaging 局部随机投影与相干阵列成像的应用

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635868

R. S. Srinivasa, M. Davenport, J. Romberg

引用次数: 1

Model-Based Encryption: Privacy of States in Networked Control Systems 基于模型的加密:网络控制系统中的状态隐私

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635912

Nirupam Gupta, N. Chopra

引用次数: 1

Some Results on the Computation of Feedback Capacity of Gaussian Channels with Memory 具有存储器的高斯信道反馈容量计算的一些结果

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8636014

A. Pedram, Takashi Tanaka

引用次数: 5

Rate Distortion Via Restricted Boltzmann Machines 受限玻尔兹曼机的速率畸变

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635888

Qing Li, Yang Chen

引用次数: 6

Array LDPC Code-based Compressive Sensing 基于阵列LDPC码的压缩感知

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635990

M. Lotfi, M. Vidyasagar

引用次数: 0

Policy Design for Active Sequential Hypothesis Testing using Deep Learning 基于深度学习的主动序列假设检验策略设计

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8636086

D. Kartik, Ekraam Sabir, U. Mitra, P. Natarajan

{"title":"Policy Design for Active Sequential Hypothesis Testing using Deep Learning","authors":"D. Kartik, Ekraam Sabir, U. Mitra, P. Natarajan","doi":"10.1109/ALLERTON.2018.8636086","DOIUrl":"https://doi.org/10.1109/ALLERTON.2018.8636086","url":null,"abstract":"Information theory has been very successful in obtaining performance limits for various problems such as communication, compression and hypothesis testing. Likewise, stochastic control theory provides a characterization of optimal policies for Partially Observable Markov Decision Processes (POMDPs) using dynamic programming. However, finding optimal policies for these problems is computationally hard in general and thus, heuristic solutions are employed in practice. Deep learning can be used as a tool for designing better heuristics in such problems. In this paper, the problem of active sequential hypothesis testing is considered. The goal is to design a policy that can reliably infer the true hypothesis using as few samples as possible by adaptively selecting appropriate queries. This problem can be modeled as a POMDP and bounds on its value function exist in literature. However, optimal policies have not been identified and various heuristics are used. In this paper, two new heuristics are proposed: one based on deep reinforcement learning and another based on a KL-divergence zero-sum game. These heuristics are compared with state-of-the-art solutions and it is demonstrated using numerical experiments that the proposed heuristics can achieve significantly better performance than existing methods in some scenarios.","PeriodicalId":299280,"journal":{"name":"2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086081","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: 17

On Differentially Private Gaussian Hypothesis Testing 关于差分私有高斯假设检验

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635911

Kwassi H. Degue, J. L. Ny

引用次数: 10

Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms 循环ADMM:用更少的计算提高分布式算法的隐私性和准确性

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI: 10.1109/ALLERTON.2018.8635916

Xueru Zhang, Mohammad Mahdi Khalili, M. Liu

引用次数: 25