waashk/dblp huggingface dataset:
outage minimized relay selection with partial channel information. selection relaying is a promising technique for practical implementation of cooperative systems with multiple relays. however, to select the best relay, globe channel knowledge is required at the selecting entity. in this paper, we consider the relay selection problem in dual-hop amplify-and-forward (af) communication systems with partial channel state information (csi). we present relay selection strategies aiming at minimizing the outage probability with different kinds of channel knowledge available, including perfect, statistical and quantized csi. simulation results show that near optimal performance is achievable with a few bits feedback to the selecting entity. thus the signaling overhead of relay selection can be greatly reduced with quantized csi feedback.
a dynamic game model for amplify-and-forward cooperative communications. cooperative wireless communication protocols are designed with the assumption that users always behave in a socially efficient manner. this assumption may not be valid in commercial wireless networks where users may violate rules of cooperation to reap benefits of cooperation at no cost. disobeying the rules of cooperation creates a social-dilemma where well-behaved users exhibit uncertainty about intention of other users. cooperation in social-dilemma is characterized by a non-cooperative nash equilibrium which indicates the difficulty of maintaining a socially optimal cooperation without establishing a mechanism to detect and mitigates effects of misbehavior. in this paper, we formulate interaction of users in cooperative amplify-and-forward as a dynamic game with incomplete information. we show the existence of a perfect bayesian equilibrium.
correlation noise classification based on matching success for transform domain wyner-ziv video coding. distributed source coding strongly depends on the knowledge of statistical dependency between source and side information. in transform domain wyner-ziv video coding (tdwz) this statistical dependency (also known as correlation noise) has been usually modeled by a unique laplacian distribution for each frequency band. in this paper, we propose a method to define different classes of correlation noise for each frequency band based on the accuracy of the side information. with this approach the correlation between source and side information is estimated separately for each frequency band of each class. therefore, the decoder can discriminate blocks in order to estimate the correlation noise of their frequency bands. simulation results show that applying the proposed method improves rate-distortion performance.
fundamental properties of non-negative impulse response filters - theoretical bounds ii. this paper presents several fundamental frequency-domain bounds for a non-negative impulse response (nnir) filter. upper-bounds on power spectral attenuation and power spectral gain in geometrically spaced frequency regions are derived, when power spectral attenuation near frequency zero is limited. by analyzing the tightnesses of these bounds, the relationship between the maximally allowable power attenuation and gain is also treated. all results hold for both continuous and discrete-time domains.
some statistical issues in estimating information in neural spike trains. information theory provides an attractive framework for attacking the neural coding problem. this entails estimating information theoretic quantities from neural spike train data. this paper highlights two issues that may arise: non-parametric entropy estimation and non-stationarity. it gives an overview of these issues and some of the progress that has been made.
one-pass multi-layer rate-distortion optimization for quality scalable video coding. in this paper, a one-pass multi-layer rate-distortion optimization algorithm is proposed for quality scalable video coding. to improve the overall coding efficiency, the mb mode in the base layer is selected not only based on its rate-distortion performance relative to this layer but also according to its impact on the enhancement layer. moreover, the optimization module for residues is also improved to benefit inter-layer prediction. simulations show that the proposed algorithm outperforms the most recent svc reference software. for eight test sequences, a gain of 0.35 db on average and 0.75 db at maximum is achieved at a cost of less than 8% increase of the total coding time.
a fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring. we consider the class of iterative shrinkage-thresholding algorithms (ista) for solving linear inverse problems arising in signal/image processing. this class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. in this paper we present a fast iterative shrinkage-thresholding algorithm (fista) which preserves the computational simplicity of ista, but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of fista.
multi-microphone maximum a posteriori fundamental frequency estimation in the cepstral domain. in this work we derive a new cepstrum based maximum likelihood fundamental frequency estimator that exploits the information of multiple microphones. the new approach results in a maximum search on the sum of the microphone cepstra. we compare the new approach to a maximum search on the cepstrum of the output signal of a delay-and-sum beamformer. we show that the new approach outperforms the beamforming approach for all considered input signal-to-noise ratios. we develop a general framework which includes the cepstral harmonics of the fundamental frequency and extend the approach towards a maximum a posteriori fundamental period tracker that further enhances the results and increases the robustness in noisy environments.
detecting bandlimited audio in broadcast television shows. for tv and radio shows containing narrowband speech, speech-to-text (stt) accuracy on the narrowband audio can be improved by using an acoustic model trained on acoustically matched data. to selectively apply it, one must first be able to accurately detect which audio segments are narrowband. the present paper explores two different bandwidth classi??cation approaches: a traditional gaussian mixture model (gmm) approach and a spline-based classifier that categorizes audio segments based on their power spectra. we focus on shows found in the darpa gale mandarin training and test sets, where the ratio of wideband to narrowband shows is very large. in this setting, the spline-based classifier reduces the number of misclassified wideband segments by up to 95% relative to the gmm-based classi??er for the same number of misclassified narrowband segments.
oscillator phase noise compensation using kalman tracking. phase noise (pn) is a serious challenge in wireless transmission systems as it can cause significant degradation of the system performance. recent publications propose iterative pn compensation algorithms for single or multicarrier systems. in this paper we will present an unscented kalman filter pn tracking algorithm working in time domain, which is independent of the underlying system. furthermore, we propose a reduced complexity tracking algorithm, where we perform an interpolation between the estimated pn samples. simulation results for an ofdm setup show that by using this technique the system performance can be improved significantly.
on-line speaker adaptation on telephony speech data with adaptively trained acoustic models. this paper addresses speaker adaptive acoustic modeling, based on feature space maximum likelihood linear regression, in the context of on-line telephony applications. an adaptive acoustic modeling method, that we previously proved effective in off-line applications, is used to train acoustic models to be used in text-dependent and text-independent on-line adaptation. experiments on telephony speech data indicate that feature space maximum a posteriori linear regression (fmaplr) greatly helps to cope with sparse adaptation data when performing instantaneous and incremental adaptation with both baseline models and speaker adaptively trained models. the use of speaker adaptively trained models in conjunction with fmaplr leads to the best recognition results in both instantaneous and incremental adaptation. the proposed text-independent adaptation approach, exploiting speaker adaptively trained models, is also proven effective.
a criterion for the enhancement of time-frequency masks in missing data recognition. despite their effectiveness for robust speech processing, missing data techniques are vulnerable to errors in the classification of the input speech signal's time-frequency points. a direct method for the removal of these mask errors is through the top-down optimization of the estimated mask, however this requires a measure to evaluate the mask quality without a priori noise knowledge. in this paper we propose the normalized likelihood confidence as such a criterion for robust speaker recognition. in this approach the accuracy with which an estimated mask classifies time-frequency points as corrupt or reliable is related to its likelihood score confidence. this is based on the conceptual effect of binary mask errors on the model likelihood distributions produced by accumulated marginalization densities. experimental results confirm a relationship between the normalized likelihood distance and the accuracy of the time-frequency mask produced by various estimation strategies.
image denoising based on statistical jump regression analysis and local segmentation using normalized cuts. the edge-preserving surface estimation based on statistical jump regression analysis is a powerful approach for image denoising. however, it requires an accessorial corner-preserving technique in which a corner threshold needs to be tuned. in this paper, we suggest a novel procedure based on local segmentation using normalized cuts which can well preserve the edges and corners at the same time without using the corner-preserving technique. extensive experiments show that the proposed approach outperforms the state-of-the-art existing approaches.
efficient gradient f0 tree model for prosody modeling and unit-selection, applied for the embedded us english concatenative tts. modeling of pitch dynamics in addition to absolute pitch modeling is highly desirable for robust pitch curve prediction and unit selection in concatenative tts systems. transition prosody models have been reported to improve consistency and naturalness for pitch-accent and tonal languages, like japanese and mandarin. in the current work we revise a gradient f0 tree model, originally developed for japanese, and adjust it for american english. the resultant model requires few computational resources at a runtime that makes it highly suitable for embedded tts applications. we report encouraging results of applying it for an embedded concatenative tts system for american english.
adaptive filters using modified sliding-block distributed arithmetic with offset binary coding. an efficient way for computing the response of an adaptive digital filter is to use sliding-block distributed arithmetic (sbda). one disadvantage of distributed arithmetic is the amount of memory utilized. by encoding the memory tables in offset binary code (obc), the size of the memory tables is reduced in half. however, the computational workload remains unchanged. by modifying the computational flow, the computational workload can be reduced by almost half at the expense of slightly more memory. this modified sbda structure is called sbda-obc. it has memory requirements 25%–50% lower than sbda depending on the size of the sub-filter. in terms of the computational workload, sbda-obc is most advantageous for large sub-filters and when the filter is split into few subfilters. in this case, the computational workload is reduced almost in half.
efficient merging of multiple audio streams for spatial sound reproduction in directional audio coding. directional audio coding (dirac) is an efficient technique to capture and reproduce spatial sound. the analysis step outputs a mono dirac stream, comprising an omnidirectional microphone pressure signal and side information, i.e., direction of arrival and diffuseness of the sound field expressed in time-frequency domain. this contribution proposes a method to merge two or more mono dirac streams for a joint playback at the reproduction side. this problem arises in applications such as immersive spatial audio teleconferencing. with respect to a trivial direct merging, the proposed method is more efficient as it does not require the synthesis step. from this follows the benefit that the loudspeaker setup at the reproduction side does not have to be known in advance. simulations and informal listening tests confirm the absence of any artifacts and that the proposed method is practically indistinguishable from the ideal merging.
optimum ambiguity-free isotropic antenna arrays. based on the crb of the 2d-doa estimation problem, we prove a condition on the sensor coordinates of a planar array to be ambiguity-free and isotropic. a systematic search of such antenna arrays is conducted leading to the identification of all possible ambiguity-free isotropic arrays. in particular, we select the arrays that outperform the popular uniform circular array (uca). it is shown that these arrays allow to enhance the doa estimation by as much as 25%, in comparison with uca. as the number of sensors increases, the best isotropic array tends towards the non-intuitive v-shape.
low complexity essentially maximum likelihood decoding of perfect space-time block codes. perfect space-time block codes (stbcs) were first introduced by oggier et al. to have full rate, full diversity and non-vanishing determinant. a maximum likelihood decoder based on the sphere decoder has been used for efficient decoding of perfect stbcs. however the worst-case complexity for the sphere decoder is an exhaustive search. in this paper we present a reduced complexity algorithm for 3 × 3 perfect stbc which gives essentially maximum likelihood (ml) performance and which can be extended to other perfect stbc. the algorithm is based on the conditional maximization of the likelihood function with respect to one of the set of signal points given another. there are a number of choices for which signal points to condition on and the underlying structure of the code guarantees that one of the choices is good with high probability. furthermore, the approach can be integrated with the sphere decoding algorithm with worst case complexity corresponding exactly to that of our algorithm.
weighted harmonic mean sinr maximization for the mimo downlink. leakage-based methods which are based on the harmonic mean signal-to-interference ratio (sir) maximization deliver a good tradeoff between the sum rate and bit-error rate (ber). in this paper, we present the joint design of linear transmit and receive beamformers that maximize the weighted harmonic mean signal-to-interference-and-noise ratio (sinr) for the downlink of the multiuser mimo channel. by exploiting the uplink-downlink sinr duality, the non-convex optimization problem is transformed into a series of simpler problems which can be solved by geometric programming. when the minimum mean square error (mmse) receive beamformers are employed, it is shown that the harmonic mean sinr maximization becomes a close approximation to the sum mse minimization problem in the high sinr region. the simulations show that the proposed scheme outperforms other leakage-based schemes in terms of harmonic mean sinr, sum mse, ber and sum rate.
a mimo-ofdm digital baseband receiver design with adaptive equalization technique for ieee 802.16 wman. in this paper, a 2×2 mimo-ofdm digital baseband receiver for ieee 802.16 wman-ofdm phy is presented. the inner receiver design includes the timing and carrier frequency synchronization, the channel estimation and the mimo detection with adaptive equalization technique. in order to enhance the robustness of the system, the blms algorithm is derived to track the channel variation for the alamouti-scheme stbc feq. the simulation results demonstrate that the mimo receiver with adaptive equalization technique has superior ser performance over frequency selective fading channel.
efficacy of a constantly adaptive language modeling technique for web-scale applications. in this paper, we describe calm, a method for building statistical language models for the web. calm addresses several unique challenges dealing with the web contents. first, calm does not rely on the whole corpus to be available to build the language model. instead, we design calm to progressively adapt itself as web chunks are made available by the crawler. second, given the dynamic and dramatic changes in the web contents, calm is designed to quickly enrich its lexicon and n-grams as new vocabulary and phrases are discovered. to reduce the amount of heuristics and human interventions typically needed for model adaptation, we derive an information theoretical formula for calm to facilitate the optimal adaptation in the maximum a posteriori (map) sense. testing against a collection of web chunks where new vocabulary and phrases are dominant, we show calm can achieve comparable and satisfactory model measured in perplexity. we also show calm is robust against over training and the initial condition, suggesting that any assumptions made in obtaining the initial model can gradually see their impacts diminished as calm runs its full course and adapt to more data.
tables for practical wyner-ziv coding of laplacian sources. many practical coding scenarios deal with sources with transform coefficients that are well modeled as laplacians. for the wyner-ziv coding problem for such sources when correlated side-information is available at the decoder, the side-information is modeled as obtained by independent additive laplacian or gaussian innovation on the source. this paper deals with the optimal choice of encoding parameters for practical wyner-ziv coding in such scenarios, using the same quantizer family as in the regular codec to cover a range of rate-distortion trade-offs, given the variances of the source and innovation. using our prior analysis of a general encoding model based on multi-level coset codes combining source and channel coding, we present comprehensive tables with optimal encoding parameters. these tables can be readily incorporated into a practical codec to read off the encoding parameters.
h.264/svc temporal bit allocation with dependent distortion model. the bit allocation problem for hierarchical b-pictures in h.264/svc is studied with a gop-based dependent distortion model in this work. inter-dependency between temporal layers of h.264/svc is often neglected because of the complexity involved, which often leads to poorer rate control performance. to address this shortcoming, we propose a distortion model that takes inter-dependency into consideration while preserving the low complexity of the encoding process. it is demonstrated by experimental results that the new distortion model results in a highly efficient bit allocation scheme, which outperforms the rate control algorithm in the jsvm 9.12 reference codec by a significant margin.
distributed distance estimation for manifold learning and dimensionality reduction. given a network of n nodes with the i-th sensor's observation xi ∈ rm, the matrix containing all euclidean distances among measurements ||xi − xj || ∀i, j ∈ {1,…, n} is a useful description of the data. while reconstructing a distance matrix has wide range of applications, we are particularly interested in the manifold reconstruction and its dimensionality reduction for data fusion and query. to make this map available to the all of the nodes in the network, we propose a fully decentralized consensus gossiping algorithm which is based on local neighbor communications, and does not require the existence of a central entity. the main advantage of our solution is that it is insensitive to changes in the network topology and it is fully decentralized. we describe the proposed algorithm in detail, study its complexity in terms of the number of inter-node radio transmissions and showcase its performance numerically.
distributed estimation using binary data transmitted over fading channels. we study the parametric distributed estimation problem using a wireless sensor network (wsn) where each sensor observes an unknown scalar parameter, quantizes its observation and sends its quantized observation to a fusion center via fading and noisy communication channels. we propose to incorporate channel statistics rather than the instantaneous channel state information (csi) into the maximum likelihood (ml) formulation and show that the resulting likelihood function is strictly log-concave almost surely with a change of variable provided that at least one of the communication channels between the sensors and the fusion center has nonzero capacity. we also investigate the effects of channel layer on the sensor threshold design and show that the threshold design problem is coupled with the channel layer and the sensor signal-to-noise ratio (snr) only for nonsymmetric channels. our formulation is very general in the sense that no assumptions are made about the physical layer in terms of the modulation schemes and the reception techniques.
tdoa estimation for cyclostationary sources: new correlations-based bounds and estimators. we consider the problem of time difference of arrival (tdoa) estimation for cyclostationary signals in additive white gaussian noise. classical approaches to the problem either ignore the cyclostationarity and use ordinary cross-correlations, or exploit the cyclostationarity by using cyclic cross-correlations, or combine these approaches into a multicycle approach. despite contradicting claims in the literature regarding the performance-ranking of these approaches, there has been almost no analytical comparative performance study. we propose to regard the estimated (ordinary or cyclic) correlations as the “front-end” data, and based on their asymptotically gaussian distribution, to compute the asymptotic cramér-rao bounds (crb) for the various combinations (ordinary/single-cycle/multi-cycle). using our cyclic-correlations-based crb (termed “crbcrb”), we can bound the performance of any (unbiased) estimator which exploits a given set of correlations. moreover, we propose an approximate maximum likelihood estimator (with respect to the correlations), and show that it attains our crbcrb asymptotically in simulations, outperforming the competitors.
a hybrid method for deconvolution of bernoulli-gaussian processes. we investigate a hybrid method which improves the quality of state inference and parameter estimation in blind deconvolution of a sparse source modeled by a bernoulli-gaussian process. in this problem, when both the signal and the filter are jointly estimated, the true posterior is typically highly multimodal. therefore, when not properly initialized, standard stochastic inference methods, (mcem, sem or saem), tend to get stuck and suffer from poor convergence. in our approach, we first relax the bernoulli-gaussian prior model by a student-t model. our simulations suggest that deterministic inference in the relaxed model is not only efficient, but also provides a very good initialization for the bernoulli-gaussian model. we provide simulation studies that compare the results obtained with and without our initialization method for several combinations of state inference and parameter estimation methods used for the bernoulli-gaussian model.