2017 51st Asilomar Conference on Signals, Systems, and Computers最新文献

milliProxy: A TCP proxy architecture for 5G mmWave cellular systems milliProxy: 5G毫米波蜂窝系统的TCP代理架构

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-12-07 DOI: 10.1109/ACSSC.2017.8335489

Michele Polese, M. Mezzavilla, Menglei Zhang, Jing Zhu, S. Rangan, S. Panwar, M. Zorzi

引用次数: 25

Joint user scheduling and power optimization in full-duplex cells with successive interference cancellation 具有连续干扰消除的全双工小区联合用户调度和功率优化

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-12-04 DOI: 10.1109/ACSSC.2017.8335520

Shahram Shahsavari, David Ramírez, E. Erkip

{"title":"Joint user scheduling and power optimization in full-duplex cells with successive interference cancellation","authors":"Shahram Shahsavari, David Ramírez, E. Erkip","doi":"10.1109/ACSSC.2017.8335520","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335520","url":null,"abstract":"This paper considers a cellular system with a full-duplex base station and half-duplex users. The base station can activate one user in uplink or downlink (half-duplex mode), or two different users one in each direction simultaneously (full-duplex mode). Simultaneous transmissions in uplink and downlink causes self-interference at the base station and uplink-to-downlink interference at the downlink user. Although uplink-to-downlink interference is typically treated as noise, it is shown that successive interference decoding and cancellation (SIC mode) can lead to significant improvement in network utility, especially when user distribution is concentrated around a few hotspots. The proposed temporal fair user scheduling algorithm and corresponding power optimization utilizes full-duplex and SIC modes as well as half-duplex transmissions based on their impact on network utility. Simulation results reveal that the proposed strategy can achieve up to 95% average cell throughput improvement in typical indoor scenarios with respect to a conventional network in which the base station is half-duplex.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857702","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: 5

Deep neural network architectures for modulation classification 调制分类的深度神经网络结构

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-12-01 DOI: 10.1109/ACSSC.2017.8335483

Xiaoyu Liu, Diyu Yang, A. E. Gamal

{"title":"Deep neural network architectures for modulation classification","authors":"Xiaoyu Liu, Diyu Yang, A. E. Gamal","doi":"10.1109/ACSSC.2017.8335483","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335483","url":null,"abstract":"In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real wireless channel, and uses 10 different modulation types. Further, a convolutional neural network (CNN) architecture was developed and shown to deliver performance that exceeds that of expert-based approaches. Here, we follow the framework of [1] and find deep neural network architectures that deliver higher accuracy than the state of the art. We tested the architecture of [1] and found it to achieve an accuracy of approximately 75% of correctly recognizing the modulation type. We first tune the CNN architecture of [1] and find a design with four convolutional layers and two dense layers that gives an accuracy of approximately 83.8% at high SNR. We then develop architectures based on the recently introduced ideas of Residual Networks (ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR accuracies of approximately 83.5% and 86.6%, respectively. Finally, we introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to achieve an accuracy of approximately 88.5% at high SNR.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121107925","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: 133

Towards provably invisible network flow fingerprints 朝向可证明不可见的网络流指纹

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-11-28 DOI: 10.1109/ACSSC.2017.8335179

Ramin Soltani, D. Goeckel, D. Towsley, A. Houmansadr

{"title":"Towards provably invisible network flow fingerprints","authors":"Ramin Soltani, D. Goeckel, D. Towsley, A. Houmansadr","doi":"10.1109/ACSSC.2017.8335179","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335179","url":null,"abstract":"Network traffic analysis reveals important information even when messages are encrypted. We consider active traffic analysis via flow fingerprinting by invisibly embedding information into packet timings of flows. In particular, assume Alice wishes to embed fingerprints into flows of a set of network input links, whose packet timings are modeled by Poisson processes, without being detected by a watchful adversary Willie. Bob, who receives the set of fingerprinted flows after they pass through the network modeled as a collection of independent and parallel M/M/1 queues, wishes to extract Alice's embedded fingerprints to infer the connection between input and output links of the network. We consider two scenarios: 1) Alice embeds fingerprints in all of the flows; 2) Alice embeds fingerprints in each flow independently with probability p. Assuming that the flow rates are equal, we calculate the maximum number of flows in which Alice can invisibly embed fingerprints while having those fingerprints successfully decoded by Bob. Then, we extend the construction and analysis to the case where flow rates are distinct, and discuss the extension of the network model.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123306012","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: 13

Seeded graph matching: Efficient algorithms and theoretical guarantees 种子图匹配:高效算法和理论保证

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-11-28 DOI: 10.1109/ACSSC.2017.8335178

Farhad Shirani, S. Garg, E. Erkip

引用次数: 48

Efficient and robust classification of seismic data using nonlinear support vector machines 基于非线性支持向量机的地震数据高效鲁棒分类

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-11-20 DOI: 10.1109/ACSSC.2017.8335156

K. Hickmann, J. Hyman, G. Srinivasan

{"title":"Efficient and robust classification of seismic data using nonlinear support vector machines","authors":"K. Hickmann, J. Hyman, G. Srinivasan","doi":"10.1109/ACSSC.2017.8335156","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335156","url":null,"abstract":"We characterize the robustness and scalability of nonlinear Support Vector Machines (SVM) combined with kernel Principal Component Analysis (kPCA) for the classification of nonlinearly correlated data within the context of geo-structure identification using seismic data. Classification through pattern recognition using supervised learning algorithms such as SVM is popular in many fields. However, the suitability of such methods for classifying seismic data is severely hampered by assumptions of linearity (linear SVM), which affects accuracy, or computational limitations with increases in data dimension (nonlinear SVM). We propose an alternate approach to overcome this limitation, performing nonlinear SVM in a reduced dimensional space determined using kPCA. The utility of the method is demonstrated by characterizing the geologic structure using synthetically generated seismograms. We observe that our method produced a more efficient and robust classifier for seismic data than standard nonlinear SVM. Optimal SVM performance occurs when a subspace that makes up only 10% of the entire feature space is used for the training set. We also observe a greater than five times speedup in computational time between the optimal performance and standard nonlinear SVM. The results indicate that performing kPCA dimension reduction prior to classification can significantly increase performance and robustness.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262597","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

Interleaver design for deep neural networks 深度神经网络的交织器设计

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-11-18 DOI: 10.1109/ACSSC.2017.8335713

Sourya Dey, P. Beerel, K. Chugg

引用次数: 6

Minimum energy quantized neural networks 最小能量量化神经网络

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-11-01 DOI: 10.1109/ACSSC.2017.8335699

Bert Moons, Koen Goetschalckx, Nick Van Berckelaer, M. Verhelst

引用次数: 95

Hyper-threaded multiplier for HECC 用于HECC的超线程倍增器

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-29 DOI: 10.1109/ACSSC.2017.8335378

Gabriel Gallin, A. Tisserand

引用次数: 7

Polar coding for the large hadron collider: Challenges in code concatenation 大型强子对撞机的极性编码:编码拼接中的挑战

2017 51st Asilomar Conference on Signals, Systems, and Computers Pub Date : 2017-10-29 DOI: 10.1109/ACSSC.2017.8335654

Alexios Balatsoukas-Stimming, T. Podzorny, J. Uythoven

引用次数: 1