Andreas Barthelme, M. Joham, Rainer Strobel, W. Utschick
{"title":"Zero-forcing precoding using generalized inverses for G.fast DSL systems","authors":"Andreas Barthelme, M. Joham, Rainer Strobel, W. Utschick","doi":"10.1109/ACSSC.2017.8335652","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335652","url":null,"abstract":"Previous work on precoding for G.fast systems focused on linear and non-linear precoding techniques to maximize the overall throughput. It has been shown that the existing algorithms almost achieve the sum capacity. However, the rate region does no longer feature a rectangular shape as for VDSL systems, that motivates the utilization of operating points suboptimal w.r.t. sum rate to meet the individual users demands. In these points, the gap between existing zero-forcing techniques and the corresponding weighted sum capacity is significantly larger. Therefore, we propose to improve the linear zero-forcing precoding by employing generalized inverses, for which we present methods that enable us to cope with the large system size of G.fast systems. Our simulations show that our proposed precoding scheme outperforms linear ZF solutions based on the Moore-Penrose pseudoinverse significantly in scenarios with greatly varying user demands.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124174717","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}
{"title":"An efficient software implementation of correctly rounded operations extending FMA: A + b + c and a × b + c × d","authors":"C. Lauter","doi":"10.1109/ACSSC.2017.8335379","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335379","url":null,"abstract":"In its 2008 revision, the IEEE754 Standard for Floating-Point Arithmetic added the Fused-Multiply-And-Add (FMA) operation, computing a × b + c without intermediate rounding. This operation enables faster scalar products and doubled-precision arithmetic. The IEEE754 Standard is again undergoing revision. We propose an efficient software implementation of two additional operations: Fused-Multiply-Twice-And-Add, a × b + c × d and Fused-Add-Add a + b + c. Our implementation guarantees correct rounding in all rounding modes and IEEE754 compliant signaling. Although intended for reference purposes, with a 94 resp. 104 cycle latency, our software implementations are pretty fast.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121347917","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}
{"title":"Frequency invariant beamforming for arbitrary planar arrays","authors":"A. Medda, Arjun Patel","doi":"10.1109/ACSSC.2017.8335527","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335527","url":null,"abstract":"In this paper, we present the adaptation to the least-square solution for frequency invariant beamforming to arbitrary planar arrays. This formulation is easily steerable thanks to the decoupling of spatial and spectral constraints. This is achieved with a basis of spherical harmonics polynomials, which parametrize the array response in a term dependent on frequency and a term dependent on the array geometry. Furthermore, the parametrization of the array response Bessel functions decouples the frequency term, which becomes independent of the geometry of the planar array, and allows steering in azimuth and elevation independently. The planar array response is approximated using virtual sensor modeling, which transform any two dimensional planar array in a virtual linear sensors, where azimuth and elevation are separable and can be steered independently.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114627814","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}
{"title":"Fast and compact Kronecker-structured dictionary learning for classification and representation","authors":"Ishan Jindal, M. Nokleby","doi":"10.1109/ACSSC.2017.8335166","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335166","url":null,"abstract":"In this paper, we present a computationally fast, storage-efficient approach, termed Kronecker-Structured Learning of Discriminative Dictionaries (K-SLD2), for learning a Kronecker-structured, overcomplete dictionary for the classification and representation of multidimensional signals like images, medical tomographic data, and videos. We evaluate the performance of K-SLD2 on several datasets, including Extended YaleB and the UCI EEG database. The use of Kronecker-structured dictionaries improves the classification performance over state-of-the-art dictionary-based methods when the number of training samples is small, at it is competitive with methods employing SIFT features even without feature extraction. Furthermore, Kronecker-structured dictionaries offer a more compact representation of signal classes, packing in more atoms with no more than 5% of the storage requirements of existing subspace models.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128350763","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}
{"title":"Algorithms for asynchronous coded caching","authors":"H. Ghasemi, A. Ramamoorthy","doi":"10.1109/ACSSC.2017.8335419","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335419","url":null,"abstract":"The original formulation of the coded caching problem assumes that the file requests from the users are synchronized, i.e., they arrive at the server at the same time. Several subsequent contributions work under the same assumption. Furthermore, the majority of prior work does not consider a scenario where users have deadlines. In our previous work we formulated the asynchronous coded caching problem where user requests arrive at different times. Furthermore, the users have specified deadlines. We proposed a linear program for obtaining its optimal solution. However, the size of the LP (number of constraints and variables) grows rather quickly with the number of users and cache sizes. In this work, we explore a dual decomposition based approach for solving the LP under consideration. We demonstrate that the dual function can be evaluated by equivalently solving a number of minimum cost network flow algorithms. Minimum cost network flow algorithms have been the subject of much investigation and current solvers routinely solve instances with millions of nodes in minutes. Our proposed approach leverages these fast solvers and allows us to solve several large scale instances of the asynchronous coded caching problem with manageable time and memory complexity.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658013","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}
Xinyu Kang, P. Fryzlewicz, C. Chu, M. Kramer, E. Kolaczyk
{"title":"Multiscale network analysis through tail-greedy bottom-up approximation, with applications in neuroscience","authors":"Xinyu Kang, P. Fryzlewicz, C. Chu, M. Kramer, E. Kolaczyk","doi":"10.1109/ACSSC.2017.8335617","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335617","url":null,"abstract":"We propose the TGUH (Tail-Greedy Unbalanced Haar) transform for networks, which results in an orthonormal, adaptive decomposition of the network adjacency matrix into Haar-wavelet like components. The ‘tail-greediness’ of the algorithm — indicating multiple greedy steps are taken in a single pass through the data — enables both fast computation and consistent estimation of network signals. We focus on development of our multiscale network decomposition and a corresponding method for network signal denoising. Moreover, we establish consistency of our resulting denoising methodology, present numerical simulations illustrating compression, and illustrate through application to signals on diffusion tensor imaging (DTI) networks.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130800545","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}
{"title":"Identifying coverage holes: Where to densify?","authors":"Rebal Jurdi, J. Andrews, David Parsons, R. Heath","doi":"10.1109/ACSSC.2017.8335588","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335588","url":null,"abstract":"We develop a methodology to identify coverage holes in cellular markets, given demographic and socio-economic information of the population, detailed knowledge of the current macro-cellular infrastructure, and the spectrum capabilities of a cellular operator. Coverage holes are defined as areas where users contending for macro BS resources outnumber those who eventually get serviced with minimum acceptable average rates. We develop a method that begins with filtering out users who connect to the Internet through Wi-Fi or to their data networks through small cell BSs. Since the rate observed by a user is a function of their own SINR as well as that of other connected users, we generate an SINR heat map and use a fair scheduling algorithm that allows us to determine the effective load on a BS as a number of users. By comparing the cell load to the number of users with acceptable rates, we obtain a coverage map that identifies holes that should be targeted by further densification. We observe that more cells will be in major deficit ten years from now, with up to 25% of an operator's cells requiring more than four additional micro cell BSs to fill the gap. Finally, we give a list of extensions of this work.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131048903","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}
{"title":"Beyond consensus and synchrony in decentralized online optimization using saddle point method","authors":"A. S. Bedi, Alec Koppel, K. Rajawat","doi":"10.1109/ACSSC.2017.8335186","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335186","url":null,"abstract":"We consider online learning problems in multiagent systems comprised of distinct subsets of agents operating without a common time-scale. Each individual in the network is charged with minimizing the global regret, which is a sum of the instantaneous sub-optimality of each agent's actions with respect to a fixed global clairvoyant actor with access to all costs across the network for all time up to a time-horizon T. Since agents are not assumed to be of the same type, the hypothesis that all agents seek a common action is violated, and thus we instead introduce a notion of network discrepancy as a measure of how well agents coordinate their behavior while retaining distinct local behavior. Moreover, agents are not assumed to receive the sequentially arriving costs on a common time index, and thus seek to learn in an asynchronous manner. A variant of the Arrow-Hurwicz saddle point algorithm is proposed to control the growth of global regret and network discrepancy. This algorithm uses Lagrange multipliers to penalize the discrepancies between agents and leads to an implementation that relies on local operations and exchange of variables between neighbors. Decisions made with this method lead to regret whose order is O(√T) and network discrepancy O(T3/4). Empirical evaluation is conducted on an asynchronously operating sensor network estimating a spatially correlated random field.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130184605","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}
Jordi Borras, Josep Font-Segura, J. Riba, G. Vázquez
{"title":"Dimension spreading for coherent opportunistic communications","authors":"Jordi Borras, Josep Font-Segura, J. Riba, G. Vázquez","doi":"10.1109/ACSSC.2017.8335703","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335703","url":null,"abstract":"The waveform optimization problem for opportunistic communications is addressed, based on sensing the second-order statistics of the existing transmissions. We propose a minimum-norm waveform optimization that exhibits robustness to the worst-case subspace mismatch, minimizes the spectral overlapping with the existing transmissions, is rotationally invariant, and has maximally white spectrum. The derived solution can be seen as a different kind of signal dimension-based spreading. In addition, the effects of the residual interference caused to the existing transmissions are studied. Numerical results are provided to assess the performance of the proposed solution in the frequency domain for the asymptotic case. The level of induced interference is compared to traditional null space techniques.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125023006","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}
{"title":"Feature based order recognition of continuous-phase FSK using principal component analysis","authors":"Ambaw B. Ambaw, M. Doroslovački","doi":"10.1109/ACSSC.2017.8335157","DOIUrl":"https://doi.org/10.1109/ACSSC.2017.8335157","url":null,"abstract":"Principal component analysis (PCA) is a technique that performs a linear transformation on the input space to align directions of maximum variation with the directions of the axises. In this paper, we study the feasibility of principal component analysis based order recognition of continuous phase FSK. The approximate entropy (ApEn) of the received signal, ApEn of the phase of the received signal, and ApEn of the instantaneous frequency of the received signal are used as a set of distinguishing features. The work aims in devising an unsupervised learning algorithm under noisy, carrier frequency offset and fast fading channel conditions. The instantaneous frequency is shaped by using root raised cosine pulses. Performance of principal component based method is compared to stacked autoencoder (SAE) based approach which is more computationally complex technique that can model relatively complicated relationships and non-linearities. For fair comparison both the PCA and SAE based methods use approximate entropy features. The benefit of employing PCA is that after PCA transformations the computation cost can really be decreased a lot. Also in both methods, no a priori information is required about carrier phase, symbol rate and carrier amplitude. The PCA based method shows higher accuracy than the SAE method.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423887","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}