Published videos of ICASSP 2011 talks

Yesterday I received an email informing that the videos of ICASSP 2011 talks have been made available. In the website you can watch the videos, slides and text of the speeches. Additionally you can share comments with other visitors (haven't tried this yet). Here the list of posts in Spectral Holes covering this year's ICASSP:

• ICASSP's technical program overview
• Compressive Sampling and Sparse Reconstruction at ICASSP 2011
• Spectrum sensing
• Cooperative spectrum sensing and resource allocation
• More hints on compressed sensing at ICASSP 2011

More hints on compressed sensing at ICASSP 2011

The last of this series of posts about ICASSP 2011 is about compressed sensing. Here's a bunch of compressed sensing related papers from different sessions:

"THE VALUE OF REDUNDANT MEASUREMENT IN COMPRESSED SENSING"; Victoria Kostina, Princeton University, US; Marco Duarte, Duke University, United States; Sina Jafarpour, Princeton University, US; Robert Calderbank, Duke University, US

The conference room was completely full during the presentation of this paper, in my opinion because of its evocative title. However the scenario studied in this work is much more particular as one could think from the title. The authors study the performance of a particular family of measurement matrices (weakly democratic) under the assumption that the CS quantizer has the choice to reject some measurements from the ones initially acquired. An overall bit budget for quantization is divided between (i) a set of bits to encode the set of indices for the rejected measurements, and (ii) the remaining bits that encode the values of the preserved measurements. Under these assumptions the paper concludes that throwing away certain measurements improves recovery SNR, i.e. it is better to have certain measurements quantized over a finer grid than a lot of coarse measurements.

"COMPRESSIVE SENSING MEETS GAME THEORY"; Sina Jafarpour, Princeton University, US; Volkan Cevher, Ecole Polytechnique Fédérale de Lausanne, Switzerland; Robert Schapire, Princeton University, US

Another paper with an evocative title. This work proposes a new reconstruction algorithm (MUSE) from compressed measurements corrupted with noise. This algorithm, which presents guarantees on the infinity-norm of the reconstruction error, can be formulated as a two-player game (and hence the title), which is equivalent to an alternating optimization scheme.


An interesting work on the fundamentals of estimation of parameters with underlying sparsity:

"PERFORMANCE BOUNDS FOR SPARSE PARAMETRIC COVARIANCE ESTIMATION IN GAUSSIAN MODELS"; Alexander Jung, Vienna University of Technology, Austria; Sebastian Schmutzhard, University of Vienna, Austria; Franz Hlawatsch, Vienna University of Technology, Austria; Alfred O. Hero III, University of Michigan, US

This paper studies the performance bounds on the problem of estimating the covariance matrix of a Gaussian random vector under the assumption that this covariance matrix can be modeled as a sparse expansion of known "basis matrices". The authors have derived lower bounds on the variance of both biased and unbiased estimators for a certain family of covariance matrices of interest. The analysis shows that in the low SNR regime the sparsity does not help, while in the high SNR regime the performance of an oracle estimator can be achieved. Between these two extreme cases the bound presents a transition phase polynomical in the SNR. This work studies a more involved scenario than the one by Ben-Haim et al. in their last year paper.


ICASSP will soon have to create a special session just for Yonina C. Eldar. This year she figures as coauthor of six papers. I won't go over all of them, just a couple of hints:

"SHANNON MEETS NYQUIST: CAPACITY LIMITS OF SAMPLED ANALOG CHANNELS"; Yuxin Chen, Stanford University, United States; Yonina C. Eldar, Technion / Israel Institute of Technology, Israel; Andrea Goldsmith, Stanford University, US

This preliminary work explores how capacity is affected by a sampling mechanisms below the channel's Nyquist rate. Under the Gaussianity assumption, the problem is formulated as a joint optimization over the input distribution and the receiver filter for a given set of uniform samplers. In the case of having a single receiver chain it is shown that the optimal transmission strategy avoids aliasing at the output, i.e. receiver filter not always corresponding to the classical matched filter. On the other hand, for multiple receiver chains sufficient conditions to obtain Nyquist capacity at a total rate equal to Landau rate are derived. My opinion is that the journal version of this work ("Shannon meets Nyquist: capacity limits of sampled analog channels", Y. Chen, Y. C. Eldar, and A. J. Goldsmith, in preparation) will give the first steps towards a rigorous analysis of the capacity achievable by compressed sampling schemes.

"SUB-NYQUIST SAMPLING OF SHORT PULSES"; Ewa Matusiak, University of Vienna, Austria; Yonina C. Eldar, Technion, Israel

This work applies the ideas of the modulated wideband converter to the time domain. Here it is assumed that the transmitted signal is composed by a series of narrow pulses with unknown shapes and unknown delays. In order to reduce the required sampling rate the proposed approach exploits the sparsity of the signal in the "Gabor frames" domain yielding to a similar result as in the case of the modulated wideband converter.

Cooperative spectrum sensing and resource allocation at ICASSP 2011

After spending the weekend in Vienna, I continue today the series of posts devoted to ICASSP 2011. Here I will present some works related to cognitive radio from a couple of sessions. In the session SPCOM-L4 (Cooperative Spectrum Sensing) we could find different collaborative schemes (several of them relying in compressed sensing) for primary user monitoring/detection:

"BASIS PURSUIT FOR SPECTRUM CARTOGRAPHY"; Juan Andrés Bazerque, Gonzalo Mateos, Georgios B. Giannakis, University of Minnesota, US

This paper proposes a sparsity-aware spline-based method for field (RF power in space and frequency) estimation from a set of measurements provided by a set of sensors distributed on the region under investigation. The authors propose the adoption of an overcomplete set of basis functions, together with a sparsity-promoting regularization term, which endows the estimator with the ability to select a few of these bases that “better” explain the data. The algorithm results into a group-Lasso estimator of the spline basis expansion coefficients. Results from empirical measurements are provided.

"BEP WALLS FOR COLLABORATIVE SPECTRUM SENSING"; Sachin Chaudhari, Jarmo Lunden, Visa Koivunen, Aalto University, Finland

This works investigates the performance limitation of collaborative spectrum sensing in cognitive radios with imperfect reporting channels. The authors study the problem of hard decision based cooperative sensing, in which each secondary node sends a one-bit binary decision over a binary channel with errors and the fusion center applies a K-out-of-N fusion rule, trying to find a similar result to the SNR walls under noise uncertainty. If the bit error rate of the reporting channel is above a wall value, the authors show that constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality and sensing time. My point here is that if sensing time is allowed to grow without bounds one could also allow better error correction codes, couldn't one?

Update: Sachin Chaudhari commented that indeed their work emphasizes the need of using correction codes in the transmissions to the fusion center. Here his comment:

"Please note that the constraints on the cooperative detection performance cannot be met at the fusion center irrespective of the received signal quality 'or' the sensing time. So even for the case when the sensing time is short and the SNRs on the listening channel are very good, the performance constraints cannot be met. The whole point of the paper was to show that you may need the error correction codes while using the counting rules."

"DECENTRALIZED SUPPORT DETECTION OF MULTIPLE MEASUREMENT VECTORS WITH JOINT SPARSITY"; Qing Ling, University of Science and Technology of China, China; Zhi Tian, Michigan Technological University, US

This paper considers the problem of finding sparse solutions from multiple measurement vectors with joint sparsity. To this end the authors propose a decentralized row-based Lasso (DR-Lasso) algorithm in which a penalty term is introduced to enforce joint sparsity of the solution. In order to exchange information between neighbors the algorithm relies in a consensus based iterative procedure.

"COOPERATIVE SPECTRUM SENSING BASED ON MATRIX RANK MINIMIZATION"; Yue Wang, Beijing University of Posts and Telecommunications, China; Zhi Tian, Michigan Technological University, US; Chunyan Feng, Beijing University of Posts and Telecommunications, China

This paper develops a new cooperative spectrum sensing technique based on matrix rank minimization. A nuclear norm minimization problem is formulated to jointly identify the nonzero support of the monitored wide spectrum (featuring possibly multiple primary signals).

The session SPCOM-L3 (Resource Allocation and Game Theory) featured some works with different approaches to resource allocation and to scheduling of the transmission/sensing instants. In general they study the problem from a network level perspective, for example by assuming slotted transmissions:

"NON-CONVEX UTILITY MAXIMIZATION IN GAUSSIAN MISO BROADCAST AND INTERFERENCE CHANNELS"; Marco Rossi, New Jersey Institute of Technology, US; Antonia Maria Tulino, Bell Laboratories (Alcatel-Lucent), US; Osvaldo Simeone, Alexander M. Haimovich, New Jersey Institute of Technology, US

In this work two algorithms are proposed to sum-rate (and other utilities) maximization for multiantenna broadcast and interference channels (non-convex problem). The first finds the global optimum by performing a suitably designed branch-and-bound method (as you can imagine fairly complex). The second approach is a suboptimal iterative algorithm that converges to a stationary point fulfilling the KKT conditions, however, its final performance depends on the initial parameters and hence does not guarantee global optimality.

"STOCHASTIC ANALYSIS OF TWO-TIER NETWORKS: EFFECT OF SPECTRUM ALLOCATION"; Wang Chi Cheung, Tony Quee Seng Quek, Agency of Science, Technology And Research, Singapore; Marios Kountouris, Supélec, France

This work proposes a two-tier network modeling a macrocell/femtocells scenario. The scheme reserves a certain amount of resources for exclusive use of macrocell, a different set of resources for exclusive use of femtocells, and the remaining are left open for both tiers, hence generating cochannel interference.

"DISTRIBUTED MULTIACCESS IN HIERARCHICAL COGNITIVE RADIO NETWORKS"; Shiyao Chen, Lang Tong, Cornell University, US

This work presents a high level approach to the problem of scheduling the transmission/sensing instants among different secondary users under a global primary-user collision constraint. If secondary users are assumed to sense/transmit in a single channel each time slot the optimum policy under certain conditions is shown to be a round-robin policy in which each secondary user cycles through all the channels in the band.

Spectrum sensing: ICASSP 2011

As I promised at the end of the post about the session on compressed sensing and sparse reconstruction, here my impressions on the papers presented in my session (SPCOM-L2: Spectrum Sensing for Cognitive Radio). This year's ICASSP presentations have been recorded in video and after the talk I was asked to sign a consent form for the recorded material. Then I assume that it (when allowed by the authors) will be made publicly available in the future. Nice!

"DETECTION DIVERSITY OF MULTIANTENNA SPECTRUM SENSORS"; Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, University of Vigo, Spain; Ashish Pandharipande, Philips Research, Netherlands

This paper applies to multiantenna spectrum sensing the concept of diversity order first proposed by Daher and Adve in the radar community. This diversity order corresponds to the slope of the average probability of detection vs. SNR curve at the point at which the average probability of detection equals 0.5, and tightly characterizes the minimum operational SNR at which a sensing scheme begins to work "well" and how fast this happens.

"THE NON-BAYESIAN RESTLESS MULTI-ARMED BANDIT: A CASE OF NEAR-LOGARITHMIC REGRET"; Wenhan Dai, Tsinghua University, China; Yi Gai, Bhaskar Krishnamachari, University of Southern California, US; Qing Zhao, University of California, US

Not so much into the topic. The idea is quite similar to the one presented by the authors last year's ICASSP: to gain knowledge of an underlying stochastic process by scheduling the sensing and transmissions. Here the abstract:

"In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are N arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activate K ≥ 1 arms at each time in order to maximize the expected total reward obtained over multiple plays. RMAB is a challenging problem that is known to be PSPACE-hard in general. We consider in this work the even harder non-Bayesian RMAB, in which the parameters of the Markov chain are assumed to be unknown a priori. We develop an original approach to this problem that is applicable when the corresponding Bayesian problem has the structure that, depending on the known parameter values, the optimal solution is one of a prescribed finite set of policies. In such settings, we propose to learn the optimal policy for the non-Bayesian RMAB by employing a suitable meta-policy which treats each policy from this finite set as an arm in a different non-Bayesian multi-armed bandit problem for which a single-arm selection policy is optimal. We demonstrate this approach by developing a novel sensing policy for opportunistic spectrum access over unknown dynamic channels. We prove that our policy achieves near-logarithmic regret (the difference in expected reward compared to a model-aware genie), which leads to the same average reward that can be achieved by the optimal policy under a known model. This is the first such result in the literature for a non-Bayesian RMAB."

"ON AUTOCORRELATION-BASED MULTIANTENNA SPECTRUM SENSING FOR COGNITIVE RADIOS IN UNKNOWN NOISE"; Jitendra Tugnait, Auburn University, US

This work presents a spectrum sensing scheme for spatially rank-1 primary signals in spatially uncorrelated noise with unequal noise variances across antennas. Unlike in the paper we presented at CIP'10 the method is not based on the GLRT. The proposed method uses the properties of the autocorrelation function of the received signal. Additionally, an asymptotic analysis of the statistic distribution under both hypothesis is provided. At the end of the talk J. Tugnait presented an sketch of the extension of the algorithm to consider spatial correlation between the noise process observed at each of the antennas, similarly to the work by Stoica and Cedervall in ”Detection tests for array processing in unknown correlated noise fields,” 1997, IEEE Trans. Signal Process.


"MULTIANTENNA DETECTION UNDER NOISE UNCERTAINTY AND PRIMARY USER'S SPATIAL STRUCTURE"; David Ramirez, University of Cantabria, Spain; Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, University of Vigo, Spain; Javier Vía, Ignacio Santamaría, University of Cantabria, Spain

The model considered in this work is the same as the one of the previous paper except for the fact that now primary user's signal may present a spatial rank larger than 1 and which is assumed known at the receiver. Hence, assuming a generic diagonal noise covariance matrix, the authors propose a GLRT based detection scheme. Although asymptotic in the low SNR regime, the proposed detector offers good performance even for moderate SNR values. This work is part of a journal paper accepted for publication in IEEE Trans. Signal Process.

"TONE DETECTION OF NON-UNIFORMLY UNDERSAMPLED SIGNALS WITH FREQUENCY EXCISION"; André Bourdoux, Sofie Pollin, Antoine Dejonghe, Liesbet Van der Perre, IMEC, Belgium

In this work the authors perform narrowband signal detection from a set of compressed measurements using a modified basis pursuit algorithm. At first I couldn't get the point of this algorithm. However now I think I understand it: the basis pursuit is applied in the spectral domain so that there exists an important leakeage of the signal power that increases the noise floor. Once the largest frequency component is identified, the modied algorithm estimates its corresponding phase before subtracting it in the original domain. Hence in the next iteration the noise floor has been reduced noticeably. A pity I haven't though about the "small detail" of the phases last year.

"A UNIFIED FRAMEWORK FOR GLRT-BASED SPECTRUM SENSING OF SIGNALS WITH COVARIANCE MATRICES WITH KNOWN EIGENVALUE MULTIPLICITIES"; Erik Axell, Erik G. Larsson, Linköping University, Sweden

The last paper of the session also focuses on multiantenna spectrum sensing. The authors compute the GLRT for a general model that comprises several practical scenarios as a special case, namely spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities (all other parameters are assumed unknown and need to be estimated). Nice presentation.

Compressive Sampling and Sparse Reconstruction: ICASSP 2011

In first place I want to make a short comment about the Xampling seminar by Yonina Eldar and Moshe Mishali. Some colleagues pointed out that the seminar's start was too basic, intended for people not familiar at all with compressed sensing. In my opinion Yonina presented a nice framework (the subspace union model) where several problems involving sparsity fit in. In this sense the first part of the presentation was quite general. However, at the end there was not much time left for commenting the main hardware implementation issues before the welcome reception. A pity.

Today's compressed sensing oral session (SPCOM-L1) was intense:

"EIGENSPACE SPARSITY FOR COMPRESSION AND DENOISING"; Ioannis Schizas, Georgios B. Giannakis, University of Minnesota, US

This paper exploits sparsity in the eigenspace of signal covariance matrices for both compression and denoising. To this end it presents an iterative algorithm which starting from the standard PCA solution converges to a sparse PCA. In this sparse PCA solution some of the principal components are zeroed, hence the compression. The noise reduction comes from the fact that if data are noisy the proposed sparsity-aware eigenspace estimator recovers a subset of the unknown signal subspace basis support, hence reducing the variability of the signal. Nice presentation by Giannakis.

"BASIS PURSUIT IN SENSOR NETWORKS"; João Mota, Carnegie Mellon University / Institute of Systems and Robotics, US; João Xavier, Pedro Aguiar, Institute of Systems and Robotics, Portugal; Markus Püschel, ETH Zürich, Switzerland

This paper exploits some of the structure of the Basis Pursuit (BP) algorithm in order to derive a distributed algorithm in a sensor network based on the dual of the original optimization problem. The novelty of this work is to use an optimal first-order method to solve an augmented Lagrangian reformulation.

"ESTIMATING SPARSE MIMO CHANNELS HAVING COMMON SUPPORT"; Yann Barbotin, Ali Hormati, Ecole Polytechnique Fédérale de Lausanne, Switzerland; Sundeep Rangan, Polytechnic Institute of New York University, United States; Martin Vetterli, Ecole Polytechnique Fédérale de Lausanne, Switzerland

This work proposes an algorithm to estimate multipath channels with Sparse Common Support based on Finite Rate of Innovation sampling. This algorithm presents two main advantages: on the one hand it exploits sparsity in domain of "received paths", and on the other it uses that in practical setups the support of this sparse signal can be considered equal accross multiple antennas. This last points closes the gap to the Cramer-Rao lower bound in the medium SNR regime (the mismatch of the model given the existing distance between the receiver antennas is exhacerbated in the high SNR regime).

"APPLYING CSISZAR'S I-DIVERGENCE TO BLIND SPARSE CHANNEL ESTIMATION"; Feng Wan, Urbashi Mitra, University of Southern California, US

In this work the authors propose a semi-blind, iterative, sparse channel estimation method based on minimizing Csiszar’s I-divergence. The proposed semi-blind method accurately estimates the significant tap locations of a sparse channel, and their corresponding magnitudes. The phase ambiguity of each of the channel contributions must be estimated through the use of additional pilots.

"COMPRESSIVE TRACKING OF DOUBLY SELECTIVE CHANNELS IN MULTICARRIER SYSTEMS BASED ON SEQUENTIAL DELAY-DOPPLER SPARSITY"; Daniel Eiwen, University of Vienna, Austria; Georg Tauböck, Franz Hlawatsch, Vienna University of Technology, Austria; Hans Georg Feichtinger, University of Vienna, Austria

The idea behind this work is that the channel support in a practical system changes slowly. Then the domain "differences in the support between one time instant and the next" presents a certain sparsity which can be exploited for channel estimation. To this end the authors employ a modified version of the orthogonal matching pursuit.

"ADDITIVE CHARACTER SEQUENCES WITH SMALL ALPHABETS FOR COMPRESSED SENSING MATRICES"; Nam Yul Yu, Lakehead University, Canada

This work proposes a deterministic construction of the compression matrix via additive character sequences. While this construction shows good properties in terms of recovery performance, it does not introduce further structure for efficient reconstruction.

This afternoon I gave my talk in the spectrum sensing session, but this will have to wait until I have some time.

Cognitive Radio at ICASSP 2011

Today I've been checking ICASSP's technical program looking for sessions related to cognitive radio and compressive sensing. Compared to last year's ICASSP, we can see an important reduction in the number of cognitive radio related papers while compressive sensing continues its rising trend. Here a quick selection (to guide me through the conference).

SPCOM-L2: Spectrum Sensing for Cognitive Radio

• DETECTION DIVERSITY OF MULTIANTENNA SPECTRUM SENSORS
  Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, Ashish Pandharipande
  
• THE NON-BAYESIAN RESTLESS MULTI-ARMED BANDIT: A CASE OF NEAR-LOGARITHMIC REGRET
  Wenhan Dai, Yi Gai, Bhaskar Krishnamachari, Qing Zhao
  
• ON AUTOCORRELATION-BASED MULTIANTENNA SPECTRUM SENSING FOR COGNITIVE RADIOS IN UNKNOWN NOISE
  Jitendra Tugnait
  
• MULTIANTENNA DETECTION UNDER NOISE UNCERTAINTY AND PRIMARY USER'S SPATIAL STRUCTURE
  David Ramirez, Gonzalo Vazquez-Vilar, Roberto Lopez-Valcarce, Javier Vía, Ignacio Santamaría
• TONE DETECTION OF NON-UNIFORMLY UNDERSAMPLED SIGNALS WITH FREQUENCY EXCISION
  André Bourdoux, Sofie Pollin, Antoine Dejonghe, Liesbet Van der Perre
  
• A UNIFIED FRAMEWORK FOR GLRT-BASED SPECTRUM SENSING OF SIGNALS WITH COVARIANCE MATRICES WITH KNOWN EIGENVALUE MULTIPLICITIES
  Erik Axell, Erik G. Larsson

SPCOM-P3: Resource Allocation and Game Theory

• OPTIMAL TRANSMISSION STRATEGIES FOR CHANNEL CAPTURE MITIGATION IN COGNITIVE RADIO NETWORKS
  Yingxi Liu, Nikhil Kundargi, Ahmed Tewfik
  
• RESOURCE ALLOCATION FOR OFDMA COGNITIVE RADIOS UNDER CHANNEL UNCERTAINTY
  Seung-Jun Kim, Nasim Soltani, Georgios B. Giannakis
  
• POWER ALLOCATION OPTIMIZATION IN OFDM-BASED COGNITIVE RADIOS BASED ON SENSING INFORMATION
  Xiaoge Huang, Baltasar Beferull-Lozano
  
• STOCHASTIC RESOURCE ALLOCATION FOR COGNITIVE RADIO NETWORKS BASED ON IMPERFECT STATE INFORMATION
  Antonio Marques, Georgios B. Giannakis, Luis Lopez-Ramos
  
• DYNAMIC SPECTRUM MANAGEMENT IN DSL WITH ASYNCHRONOUS CROSSTALK
  Rodrigo Moraes, Paschalis Tsiaflakis
  
• DESIGN OF DIGITAL PREDISTORTERS FOR WIDEBAND POWER AMPLIFIERS IN COMMUNICATION SYSTEMS WITH DYNAMIC SPECTRUM ALLOCATION
  Sungho Choi, Eui-Rim Jeong, Yong Hoon Lee
  
• GAME-THEORETIC RESOURCE ALLOCATION IN RELAY-ASSISTED DS/CDMA SYSTEMS WITH SUCCESSIVE INTERFERENCE CANCELLATION
  Alessio Zappone, Eduard Jorswieck
  
• OPTIMAL RADIO ACCESS IN FEMTOCELL NETWORKS BASED ON MARKOV MODELING OF INTERFERERS' ACTIVITY
  Sergio Barbarossa, Alessandro Carfagna, Stefania Sardellitti, Marco Omilipo, Loreto Pescosolido
  
• CONVERGENCE OF THE ITERATIVE WATER-FILLING ALGORITHM WITH SEQUENTIAL UPDATES IN SPECTRUM SHARING SCENARIOS
  Bhavani Shankar M. R, Peter von Wrycza, Mats Bengtsson, Björn Ottersten
  
• RATE CONTROL FOR PSD LIMITED MULTIPLE ACCESS SYSTEMS THROUGH LINEAR PROGRAMMING
  Amir Leshem, Ephraim Zehavi

SPCOM-L3: Resource Allocation and Game Theory

• NON-CONVEX UTILITY MAXIMIZATION IN GAUSSIAN MISO BROADCAST AND INTERFERENCE CHANNELS
  Marco Rossi, Antonia Maria Tulino, Osvaldo Simeone, Alexander M. Haimovich
  
• STOCHASTIC ANALYSIS OF TWO-TIER NETWORKS: EFFECT OF SPECTRUM ALLOCATION
  Wang Chi Cheung, Tony Quee Seng Quek, Marios Kountouris
  
• DISTRIBUTED MULTIACCESS IN HIERARCHICAL COGNITIVE RADIO NETWORKS
  Shiyao Chen, Lang Tong
• NEW RESULTS ON ADAPTIVE COMPUTATIONAL RESOURCE ALLOCATION IN SOFT MIMO DETECTION
  Mirsad Cirkic, Daniel Persson, Erik G. Larsson
  
• JOINT BANDWIDTH AND POWER ALLOCATION IN COGNITIVE RADIO NETWORKS UNDER FADING CHANNELS
  Xiaowen Gong, Sergiy Vorobyov, Chintha Tellambura
  
• CLT FOR EIGEN-INFERENCE METHODS IN COGNITIVE RADIOS
  Jianfeng Yao, Romain Couillet, Jamal Najim, Eric Moulines, Mérouane Debbah
  

SPCOM-L4: Cooperative Spectrum Sensing

• BEP WALLS FOR COLLABORATIVE SPECTRUM SENSING
  Sachin Chaudhari, Jarmo Lunden,Visa Koivunen
  
• COOPERATIVE SENSING WITH SEQUENTIAL ORDERED TRANSMISSIONS TO SECONDARY FUSION CENTER
  Laila Hesham, Ahmed Sultan, Mohammed Nafie, Fadel Digham
  
• BASIS PURSUIT FOR SPECTRUM CARTOGRAPHY
  Juan Andrés Bazerque, Gonzalo Mateos, Georgios B. Giannakis
  
• DECENTRALIZED SUPPORT DETECTION OF MULTIPLE MEASUREMENT VECTORS WITH JOINT SPARSITY
  Qing Ling, Zhi Tian
  
• COOPERATIVE SPECTRUM SENSING BASED ON MATRIX RANK MINIMIZATION
  Yue Wang, Zhi Tian, Chunyan Feng
  
• COOPERATIVE SENSING IN COGNITIVE NETWORKS UNDER MALICIOUS ATTACK
  Mai Abdelhakim, Lei Zhang, Jian Ren, Tongtong Li
  

SPCOM-L1: Compressive Sampling and Sparse Reconstruction

• EIGENSPACE SPARSITY FOR COMPRESSION AND DENOISING
  Ioannis Schizas, Georgios B. Giannakis
  
• BASIS PURSUIT IN SENSOR NETWORKS
  João Mota, João Xavier, Pedro Aguiar, Markus Püschel
  
• ESTIMATING SPARSE MIMO CHANNELS HAVING COMMON SUPPORT
  Yann Barbotin, Ali Hormati, Sundeep Rangan, Martin Vetterli
  
• COMPRESSIVE TRACKING OF DOUBLY SELECTIVE CHANNELS IN MULTICARRIER SYSTEMS BASED ON SEQUENTIAL DELAY-DOPPLER SPARSITY
  Daniel Eiwen, Georg Tauböck, Franz Hlawatsch, Hans Georg Feichtinger
  
• ADDITIVE CHARACTER SEQUENCES WITH SMALL ALPHABETS FOR COMPRESSED SENSING MATRICES
  Nam Yul Yu

SPTM-P3/4: Compressive Sensing and Sparsity I and II

• ROBUST NONPARAMETRIC REGRESSION BY CONTROLLING SPARSITY
  Gonzalo Mateos, Georgios B. Giannakis
  
• COMPRESSIVE POWER SPECTRAL DENSITY ESTIMATION
  Michael Lexa, Michael Davies, Janosch Nikolic, John Thompson
  
• ADAPTIVE COMPRESSIVE SENSING AND PROCESSING FOR RADAR TRACKING
  Ioannis Kyriakides
  
• SPARSE VARIABLE REDUCED RANK REGRESSION VIA STIEFEL OPTIMIZATION
  Magnus Ulfarsson, Victor Solo
  
• THE ROTATIONAL LASSO
  Alexander Lorbert, Peter Ramadge
  
• CAUSAL SIGNAL RECOVERY FROM U-INVARIANT SAMPLES
  Tomer Michaeli, Yonina C. Eldar, Volker Pohl
  
• ESTIMATION AND DYNAMIC UPDATING OF TIME-VARYING SIGNALS WITH SPARSE VARIATIONS
  M. Salman Asif, Adam Charles, Justin Romberg, Christopher Rozell
  
• RECOVERY OF SPARSE PERTURBATIONS IN LEAST SQUARES PROBLEMS
  Mert Pilanci, Orhan Arikan
  
• COMPRESSIVE SAMPLING WITH A SUCCESSIVE APPROXIMATION ADC ARCHITECTURE
  Chenchi Luo, James McClellan
  
• ITERATIVE REWEIGHTED ALGORITHMS FOR SPARSE SIGNAL RECOVERY WITH TEMPORALLY CORRELATED SOURCE VECTORS
  Zhilin Zhang, Bhaskar D. Rao
  
• USING THE KERNEL TRICK IN COMPRESSIVE SENSING: ACCURATE SIGNAL RECOVERY FROM FEWER MEASUREMENTS
  Hanchao Q, Shannon Hughes
  
• SUB-NYQUIST SAMPLING OF SHORT PULSES
  Ewa Matusiak, Yonina C. Eldar
  

SPTM-P6: Sparsity, Sampling and Reconstruction

SPTM-L4/7: Compressed Sensing: Theory and Methods I and II

• THE VALUE OF REDUNDANT MEASUREMENT IN COMPRESSED SENSING
  Victoria Kostina, Marco Duarte, Sina Jafarpour, Robert Calderbank
  

SPTM-P11: Sampling and Reconstruction

SS-L11: Compressed Sensing and Sparse Representation of Signals

• BEATING NYQUIST THROUGH CORRELATIONS: A CONSTRAINED RANDOM DEMODULATOR FOR SAMPLING OF SPARSE BANDLIMITED SIGNALS
  Andrew Harms, Waheed U. Bajwa, Robert Calderbank
  
• SPARSE SPECTRAL FACTORIZATION: UNICITY AND RECONSTRUCTION ALGORITHMS
  Yue Lu, Martin Vetterli
  
• RAND PPM : A LOW POWER COMPRESSIVE SAMPLING ANALOG TO DIGITAL CONVERTER
  Praveen Yenduri, Anna Gilbert, Michael Flynn, Shahrzad Naraghi