Pub Date : 2017-01-30DOI: 10.1109/ITA.2017.8023467
O. Rioul
We present a simple proof of the entropy-power inequality using an optimal transportation argument which takes the form of a simple change of variables. The same argument yields a reverse inequality involving a conditional differential entropy which has its own interest. It can also be generalized in various ways. The equality case is easily captured by this method and the proof is formally identical in one and several dimensions.
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Pub Date : 2017-01-12DOI: 10.1109/ITA.2017.8023462
D. Needell, T. Woolf
Asynchronous parallel computing and sparse recovery are two areas that have received recent interest. Asynchronous algorithms are often studied to solve optimization problems where the cost function takes the form Σi=1Mƒi(x), with a common assumption that each ƒi is sparse; that is, each ƒi acts only on a small number of components of x ∈ ℝn. Sparse recovery problems, such as compressed sensing, can be formulated as optimization problems, however, the cost functions ƒi are dense with respect to the components of x, and instead the signal x is assumed to be sparse, meaning that it has only s non-zeros where s ≪ n. Here we address how one may use an asynchronous parallel architecture when the cost functions ƒi are not sparse in x, but rather the signal x is sparse. We propose an asynchronous parallel approach to sparse recovery via a stochastic greedy algorithm, where multiple processors asynchronously update a vector in shared memory containing information on the estimated signal support. We include numerical simulations that illustrate the potential benefits of our proposed asynchronous method.
{"title":"An asynchronous parallel approach to sparse recovery","authors":"D. Needell, T. Woolf","doi":"10.1109/ITA.2017.8023462","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023462","url":null,"abstract":"Asynchronous parallel computing and sparse recovery are two areas that have received recent interest. Asynchronous algorithms are often studied to solve optimization problems where the cost function takes the form Σ<inf>i=1</inf><sup>M</sup>ƒ<inf>i</inf>(x), with a common assumption that each ƒ<inf>i</inf> is sparse; that is, each ƒ<inf>i</inf> acts only on a small number of components of x ∈ ℝ<sup>n</sup>. Sparse recovery problems, such as compressed sensing, can be formulated as optimization problems, however, the cost functions ƒ<inf>i</inf> are dense with respect to the components of x, and instead the signal x is assumed to be sparse, meaning that it has only s non-zeros where s ≪ n. Here we address how one may use an asynchronous parallel architecture when the cost functions ƒ<inf>i</inf> are not sparse in x, but rather the signal x is sparse. We propose an asynchronous parallel approach to sparse recovery via a stochastic greedy algorithm, where multiple processors asynchronously update a vector in shared memory containing information on the estimated signal support. We include numerical simulations that illustrate the potential benefits of our proposed asynchronous method.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124970009","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}
Pub Date : 2016-10-29DOI: 10.1109/ITA.2017.8023484
Waqas bin Abbas, Felipe Gómez-Cuba, M. Zorzi
In future mmWave wireless system, fully digital receivers may have an excessive power consumption at the Analog to Digital Converters (ADC), even if lower resolution ADCs are employed. We propose to optimize the ADC resolution exploiting the sparse propagation in mmWave. We identify and assign more bits to antennas that capture stronger incoming signals, and allocate fewer bits to the antennas that see mostly noise. In order to facilitate a potential practical implementation, we constrain the allocation problem so the number of bits assigned to each antenna can take only one of two values, blow or bhigh. Compared to a reference fixed-resolution mmWave system with bref bits (blow ≤ bref ≤ bhigh), and depending on the margin between the two options given to the algorithm, (blow, bhigh), our results show that 2-level receivers with a low margin (e.g., (4, 6)) can achieve moderate power saving (5-20%) consistent across any received unquantized SNR value, whereas 2-level receivers with a wide margin (e.g., (1, 8)) can achieve a large power saving (80%) only at high SNR, while consuming more power than the reference at low SNR. Combining bit allocation with antenna selection techniques, we create a 3-level system (e.g., 0, 4, 8) that can outperform the former scenario when the given resolution options are carefully chosen.
{"title":"Bit allocation for increased power efficiency in 5G receivers with variable-resolution ADCs","authors":"Waqas bin Abbas, Felipe Gómez-Cuba, M. Zorzi","doi":"10.1109/ITA.2017.8023484","DOIUrl":"https://doi.org/10.1109/ITA.2017.8023484","url":null,"abstract":"In future mmWave wireless system, fully digital receivers may have an excessive power consumption at the Analog to Digital Converters (ADC), even if lower resolution ADCs are employed. We propose to optimize the ADC resolution exploiting the sparse propagation in mmWave. We identify and assign more bits to antennas that capture stronger incoming signals, and allocate fewer bits to the antennas that see mostly noise. In order to facilitate a potential practical implementation, we constrain the allocation problem so the number of bits assigned to each antenna can take only one of two values, blow or bhigh. Compared to a reference fixed-resolution mmWave system with bref bits (blow ≤ bref ≤ bhigh), and depending on the margin between the two options given to the algorithm, (blow, bhigh), our results show that 2-level receivers with a low margin (e.g., (4, 6)) can achieve moderate power saving (5-20%) consistent across any received unquantized SNR value, whereas 2-level receivers with a wide margin (e.g., (1, 8)) can achieve a large power saving (80%) only at high SNR, while consuming more power than the reference at low SNR. Combining bit allocation with antenna selection techniques, we create a 3-level system (e.g., 0, 4, 8) that can outperform the former scenario when the given resolution options are carefully chosen.","PeriodicalId":305510,"journal":{"name":"2017 Information Theory and Applications Workshop (ITA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114328785","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}
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