OpenRaiser/Intern-Atlas huggingface dataset:
Performance analysis of a cross-layer collaborative beamforming approach in the presence of channel and phase errors
Venue: Conference ICASSP 2008
Year: 2008

Collaborative beamforming enables nodes in a wireless network to transmit a common message over long distances in an energy efficient fashion. However, the process of making available the same message to all collaborating nodes introduces delays. The authors recently proposed a MAC-PHY cross-layer scheme that enables collaborative beamforming with significantly reduced collaboration overhead. The method requires knowledge of node locations and internode channel coefficients. In this paper, the performance of that approach is studied analytically in terms of average beampattern and symbol error probability (SEP) under realistic conditions, i.e., when imperfect channel estimates are used and when there are phase errors in the contributions of the collaborating nodes at the receiver.
A first step to convolutive sparse representation
Venue: Conference ICASSP 2008
Year: 2008

In this paper an extension of the sparse decomposition problem is considered and an algorithm for solving it is presented. In this extension, it is known that one of the shifted versions of a signal s (not necessarily the original signal itself) has a sparse representation on an overcomplete dictionary, and we are looking for the sparsest representation among the representations of all the shifted versions of s. Then, the proposed algorithm finds simultaneously the amount of the required shift, and the sparse representation. Experimental results emphasize on the performance of our algorithm.
Auction-based resource allocation for multi-relay asynchronous cooperative networks
Venue: Conference ICASSP 2008
Year: 2008

Resource allocation is considered for cooperative transmissions in multiple-relay wireless networks. Two auction mechanisms, SNR auctions and power auctions, are proposed to distributively coordinate the allocation of power among multiple relays. In the SNR auction, a user chooses the relay with the lowest weighted price. In the power auction, a user may choose to use multiple relays simultaneously, depending on the network topology and the relays' prices. Sufficient conditions for the existence (in both auctions) and uniqueness (in the SNR auction) of the Nash equilibrium are given. The fairness of the SNR auction and efficiency of the power auction are further discussed. It is also proven that users can achieve the unique Nash equilibrium distributively via best response updates in a completely asynchronous manner.
Optimal medium access control in cognitive radios: A sequential design approach
Venue: Conference ICASSP 2008
Year: 2008

The design of medium access control protocols for a cognitive user wishing to opportunistically exploit frequency bands within parts of the radio spectrum having multiple bands is considered. In the scenario under consideration, the availability probability of each channel is unknown a priori to the cognitive user. Hence efficient medium access strategies must strike a balance between exploring the availability of channels and exploiting the opportunities identified thus far. Using a sequential design approach, an optimal medium access strategy is derived. To avoid the prohibitive computational complexity of this optimal strategy, a low complexity asymptotically optimal strategy is also developed. The proposed strategy does not require any prior statistical knowledge about the traffic pattern on the different channels.
Spatial-spectral joint detection for wideband spectrum sensing in cognitive radio networks
Venue: Conference ICASSP 2008
Year: 2008

Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. Since individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing, this paper proposes a cooperative wideband spectrum sensing scheme, referred to as spatial-spectral joint detection, which is based on a linear combination of the local statistics from spatially distributed multiple cognitive radios. The cooperative sensing problem is formulated into an optimization problem, for which suboptimal but efficient solutions can be obtained through mathematical transformation under practical conditions.
Large deviations analysis for the detection of 2D hidden Gauss-Markov random fields using sensor networks
Venue: Conference ICASSP 2008
Year: 2008

The detection of hidden two-dimensional Gauss-Markov random fields using sensor networks is considered. Under a conditional autoregressive model, the error exponent for the Neyman-Pearson detector satisfying a fixed level constraint is obtained using the large deviations principle. For a symmetric first order autoregressive model, the error exponent is given explicitly in terms of the SNR and an edge dependence factor (field correlation). The behavior of the error exponent as a function of correlation strength is seen to divide into two regions depending on the value of the SNR. At high SNR, uncorrelated observations maximize the error exponent for a given SNR, whereas there is non-zero optimal correlation at low SNR. Based on the error exponent, the energy efficiency (defined as the ratio of the total information gathered to the total energy required) of ad hoc sensor network for detection is examined for two sensor deployment models: an infinite area model and and infinite density model. For a fixed sensor density, the energy efficiency diminishes to zero at rate O(area^{-1/2}) as the area is increased. On the other hand, non-zero efficiency is possible for increasing density depending on the behavior of the physical correlation as a function of the link length.
MIMO decoding based on stochastic reconstruction from multiple projections
Venue: Conference ICASSP 2009
Year: 2009

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a-priori to be bounded integer valued, or they come from a finite set of values on an arbitrary finite lattice. In this case finding the closest vector becomes NP-Hard problem. In this paper we propose a novel algorithm, the Tomographic Least Squares Decoder (TLSD), that not only solves the ILS problem, better than other sub-optimal techniques, but also is capable of providing the a-posteriori probability distribution for each element in the solution vector. The algorithm is based on reconstruction of the vector from multiple two-dimensional projections. The projections are carefully chosen to provide low computational complexity. Unlike other iterative techniques, such as the belief propagation, the proposed algorithm has ensured convergence. We also provide simulated experiments comparing the algorithm to other sub-optimal algorithms.
Beacon-assisted spectrum access with cooperative cognitive transmitter and receiver
Venue: Conference ICASSP 2009
Year: 2009

Spectrum access is an important function of cognitive radios for detecting and utilizing spectrum holes without interfering with the legacy systems. In this paper we propose novel cooperative communication models and show how deploying such cooperations between a pair of secondary transmitter and receiver assists them in identifying spectrum opportunities more reliably. These cooperations are facilitated by dynamically and opportunistically assigning one of the secondary users as a relay to assist the other one which results in more efficient spectrum hole detection. Also, we investigate the impact of erroneous detection of spectrum holes and thereof missing communication opportunities on the capacity of the secondary channel. The capacity of the secondary users with interference-avoiding spectrum access is affected by 1) how effectively the availability of vacant spectrum is sensed by the secondary transmitter-receiver pair, and 2) how correlated are the perceptions of the secondary transmitter-receiver pair about network spectral activity. We show that both factors are improved by using the proposed cooperative protocols. One of the proposed protocols requires explicit information exchange in the network. Such information exchange in practice is prone to wireless channel errors (i.e., is imperfect) and costs bandwidth loss. We analyze the effects of such imperfect information exchange on the capacity as well as the effect of bandwidth cost on the achievable throughput. The protocols are also extended to multiuser secondary networks.
Analyzing Least Squares and Kalman Filtered Compressed Sensing
Venue: Conference ICASSP 2009
Year: 2009

In recent work, we studied the problem of causally reconstructing time sequences of spatially sparse signals, with unknown and slow time-varying sparsity patterns, from a limited number of linear "incoherent" measurements. We proposed a solution called Kalman Filtered Compressed Sensing (KF-CS). The key idea is to run a reduced order KF only for the current signal's estimated nonzero coefficients' set, while performing CS on the Kalman filtering error to estimate new additions, if any, to the set. KF may be replaced by Least Squares (LS) estimation and we call the resulting algorithm LS-CS. In this work, (a) we bound the error in performing CS on the LS error and (b) we obtain the conditions under which the KF-CS (or LS-CS) estimate converges to that of a genie-aided KF (or LS), i.e. the KF (or LS) which knows the true nonzero sets.
Linear transformations and Restricted Isometry Property
Venue: Conference ICASSP 2009
Year: 2009

The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in compressed sensing theory. It says that if a sampling matrix satisfies the RIP of certain order proportional to the sparsity of the signal, then the original signal can be reconstructed even if the sampling matrix provides a sample vector which is much smaller in size than the original signal. This short note addresses the problem of how a linear transformation will affect the RIP. This problem arises from the consideration of extending the sensing matrix and the use of compressed sensing in different bases. As an application, the result is applied to the redundant dictionary setting in compressed sensing.
Compressive sensing for MIMO radar
Venue: Conference ICASSP 2009
Year: 2009

Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing.
Distributed detection over fading macs with multiple antennas at the fusion center
Venue: Conference ICASSP 2010
Year: 2010

A distributed detection problem over fading Gaussian multiple-access channels is considered. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme. The fusion center has multiple antennas with different channel models considered between the sensors and the fusion center, and different cases of channel state information are assumed at the sensors. The performance is evaluated in terms of the error exponent for each of these cases, where the effect of multiple antennas at the fusion center is studied. It is shown that for zero-mean channels between the sensors and the fusion center when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the fusion center. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the fusion center is shown to be no more than a factor of (8/pi) for Rayleigh fading channels between the sensors and the fusion center, independent of the number of antennas at the fusion center, or correlation among noise samples across sensors. Scaling laws for such gains are also provided when both sensors and antennas are increased simultaneously. Simple practical schemes and a numerical method using semidefinite relaxation techniques are presented that utilize the limited possible gains available. Simulations are used to establish the accuracy of the results.
Static Address Generation Easing: a design methodology for parallel interleaver architectures
Venue: Conference ICASSP 2010
Year: 2010

For high throughput applications, turbo-like iterative decoders are implemented with parallel architectures. However, to be efficient parallel architectures require to avoid collision accesses i.e. concurrent read/write accesses should not target the same memory block. This consideration applies to the two main classes of turbo-like codes which are Low Density Parity Check (LDPC) and Turbo-Codes. In this paper we propose a methodology which finds a collision-free mapping of the variables in the memory banks and which optimizes the resulting interleaving architecture. Finally, we show through a pedagogical example the interest of our approach compared to state-of-the-art techniques.
SET: An algorithm for consistent matrix completion
Venue: Conference ICASSP 2010
Year: 2010

A new algorithm, termed subspace evolution and transfer (SET), is proposed for solving the consistent matrix completion problem. In this setting, one is given a subset of the entries of a low-rank matrix, and asked to find one low-rank matrix consistent with the given observations. We show that this problem can be solved by searching for a column space that matches the observations. The corresponding algorithm consists of two parts -- subspace evolution and subspace transfer. In the evolution part, we use a line search procedure to refine the column space. However, line search is not guaranteed to converge, as there may exist barriers along the search path that prevent the algorithm from reaching a global optimum. To address this problem, in the transfer part, we design mechanisms to detect barriers and transfer the estimated column space from one side of the barrier to the another. The SET algorithm exhibits excellent empirical performance for very low-rank matrices.
On unbiased estimation of sparse vectors corrupted by Gaussian noise
Venue: Conference ICASSP 2010
Year: 2010

We consider the estimation of a sparse parameter vector from measurements corrupted by white Gaussian noise. Our focus is on unbiased estimation as a setting under which the difficulty of the problem can be quantified analytically. We show that there are infinitely many unbiased estimators but none of them has uniformly minimum mean-squared error. We then provide lower and upper bounds on the Barankin bound, which describes the performance achievable by unbiased estimators. These bounds are used to predict the threshold region of practical estimators.
Collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks
Venue: Conference ICASSP 2010
Year: 2010

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%.
Poisoned feedback: The impact of malicious users in closed-loop multiuser mimo systems
Venue: Conference ICASSP 2010
Year: 2010

Accurate channel state information (CSI) at the transmitter is critical for maximizing spectral efficiency on the downlink of multi-antenna networks. In this work we analyze a novel form of physical layer attacks on such closed-loop wireless networks. Specifically, this paper considers the impact of deliberately inaccurate feedback by malicious users in a multiuser multicast system. Numerical results demonstrate the significant degradation in performance of closed-loop transmission schemes due to intentional feedback of false CSI by adversarial users.
Step-frequency radar with compressive sampling (SFR-CS)
Venue: Conference ICASSP 2010
Year: 2010

Step-frequency radar (SFR) is a high resolution radar approach, where multiple pulses are transmitted at different frequencies, covering a wide spectrum. The obtained resolution directly depends on the total bandwidth used, or equivalently, the number of transmitted pulses. This paper proposes a novel SFR system, namely SFR with compressive sampling (SFRCS), that achieves the same resolution as a conventional SFR, while using significantly reduced bandwidth, or equivalently, transmitting significantly fewer pulses. This bandwidth reduction is accomplished by employing compressive sampling ideas and exploiting the sparseness of targets in the range velocity space.
Robust beamforming for amplify-and-forward MIMO relay systems based on quadratic matrix programming
Venue: Conference ICASSP 2010
Year: 2010

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).
A general proof of convergence for adaptive distributed beamforming schemes
Venue: Conference ICASSP 2011
Year: 2011

This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework. Once reformulated as local random search algorithms, it is proved that under two sufficient conditions: a) the objective function of the algorithm is continuous and all its local maxima are global maxima, and b) the origin is an interior point within the range of the considered transformation of the random perturbation, the corresponding adaptive distributed beamforming schemes converge both in probability and in mean. This proof of convergence is general since it can be applied to analyze randomized adaptive distributed beamforming schemes with any type of objective functions and probability measures as long as both the sufficient conditions are satisfied. Further, this framework can be generalized to analyze an asynchronous scheme where distributed transmitters can only update their beamforming coefficients asynchronously. Simulation results are also provided to validate our analyses.
Searching in one billion vectors: Re-rank with source coding
Venue: Conference ICASSP 2011
Year: 2011

Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.
Performance bounds for sparse parametric covariance estimation in Gaussian models
Venue: Conference ICASSP 2011
Year: 2011

We consider estimation of a sparse parameter vector that determines the covariance matrix of a Gaussian random vector via a sparse expansion into known "basis matrices". Using the theory of reproducing kernel Hilbert spaces, we derive lower bounds on the variance of estimators with a given mean function. This includes unbiased estimation as a special case. We also present a numerical comparison of our lower bounds with the variance of two standard estimators (hard-thresholding estimator and maximum likelihood estimator).
A robust artificial noise aided transmit design for MISO secrecy
Venue: Conference ICASSP 2011
Year: 2011

This paper considers an artificial noise (AN) aided secrecy rate maximization (SRM) problem for a multi-input single-output (MISO) channel overheard by multiple single-antenna eavesdroppers. We assume that the transmitter has perfect knowledge about the channel to the desired user but imperfect knowledge about the channels to the eavesdroppers. Therefore, the resultant SRM problem is formulated in the way that we maximize the worst-case secrecy rate by jointly designing the signal covariance ${\bf W}$ and the AN covariance ${\bf Σ}$. However, such a worst-case SRM problem turns out to be hard to optimize, since it is nonconvex in ${\bf W}$ and ${\bf Σ}$ jointly. Moreover, it falls into the class of semi-infinite optimization problems. Through a careful reformulation, we show that the worst-case SRM problem can be handled by performing a one-dimensional line search in which a sequence of semidefinite programs (SDPs) are involved. Moreover, we also show that the optimal ${\bf W}$ admits a rank-one structure, implying that transmit beamforming is secrecy rate optimal under the considered scenario. Simulation results are provided to demonstrate the robustness and effectiveness of the proposed design compared to a non-robust AN design.
Rényi information measures for spectral change detection
Venue: Conference ICASSP 2011
Year: 2011

Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time- frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the Rényi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.