Distributed linear blind source separation over wireless sensor networks with arbitrary connectivity patterns

Alavi, S. R Mir; Kleijn, W. Bastiaan

doi:10.1109/icassp.2016.7472262

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…. ,s n (T ))], Pr(s n (t − 1) = 0|s n ) = 1 − Pr(s n (t − 1) = 1|s n ) is obtained from (13), and the posterior joint probabilities in (15) and (16) ,…”

Section: B Unknown Model Parametersmentioning

confidence: 99%

“…, apply the forward-backward algorithm to calculate the probabilities α t and β t and hence the posterior probabilities (13), for n = 1, . .…”

Section: B Unknown Model Parametersmentioning

confidence: 99%

“…The need for BSS arises in non-collaborative applications in which the signals and the channels through which they are received at a terminal are both unknown. ICA has been widely applied to solve BSS problems in wireless networks [13][14][15][16], since it yields a useful decomposition with only scaling, and permutation ambiguities [17]. To achieve signal separation, ICA relies on the statistical independence and on the non-Gaussian distribution of the components of the mixture.…”

Section: Introductionmentioning

confidence: 99%

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Blind Sparse Estimation of Intermittent Sources Over Unknown Fading Channels

Dong

Simeone

Haimovich

et al. 2019

IEEE Trans. Veh. Technol.

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Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on the output produced by the former. Both stages are designed so as to account for the sparsity and memory of the sources. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm and Expectation Maximization (EM) algorithm are leveraged for PSF. It is shown that the proposed algorithm can enhance the detection and the estimation performance of the sources, and that it is robust to the sparsity level. Index TermsBlind source separation, Wireless networks, Dictionary learning, Intermittent and sparse sources, Hidden Markov model I. INTRODUCTIONBlind source separation (BSS) refers to the separation of a set of source signals from a set of mixed signals, without resorting to any a priori information about the source signals or the mixing process [1]. BSS exploits only the information carried by the received signals themselves, hence the term blind. BSS has numerous applications in speech recognition [2,3], image extraction [4,5], and surveillance [6,7]. Different metrics are used to evaluate the performance of BSS methods depending on the applications. For example, signal-to-interference ratio is used in [2] for speech separation, and a performance index is introduced in [4] for image feature extraction. Based on these metrics, many approaches have been proposed to solve BSS problems, such as independent component analysis (ICA) [8], principal component analysis (PCA) [9], and singular value decomposition (SVD) [10].This paper addresses BSS in wireless networks. We are specifically interested in the set-up illustrated in Fig. 1, in which a fusion center observes a number of radio sources via noisy sensor measurements over unknown channels. The system may model an Internet-of-Things (IoT) system, such as LoRa, Sigfox, or Narrow Band-IoT (NB-IoT) [11,12].In wireless networks involving multiple terminals operating over flat fading channels, the signals received at a terminal are linear mixtures of the signals emitted by the transmitting terminals. The need for BSS arises in non-collaborative applications in which the signals and the channels through which they are received at a terminal are both unknown. ICA has been widely applied to solve BSS problems in wireless networks [13][14][15][16], since it yields a useful decomposition with only scaling, and permutation ambiguities [17]. To achieve signal separation, ICA relies on the statistical independence and on the non-...

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“…. ,s n (T ))], Pr(s n (t − 1) = 0|s n ) = 1 − Pr(s n (t − 1) = 1|s n ) is obtained from (13), and the posterior joint probabilities in (15) and (16) ,…”

Section: B Unknown Model Parametersmentioning

confidence: 99%

“…, apply the forward-backward algorithm to calculate the probabilities α t and β t and hence the posterior probabilities (13), for n = 1, . .…”

Section: B Unknown Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind Sparse Estimation of Intermittent Sources Over Unknown Fading Channels

Dong

Simeone

Haimovich

et al. 2019

IEEE Trans. Veh. Technol.

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show abstract

“…In publication I [5], we applied the scheme 2 on BSS algorithms that optimize the mixing matrices. In the rest of this chapter we will see that applying the scheme 2 on BSS algorithms that optimize the de-mixing matrices requires lower transmission power compared to the mixing matrix based BSS algorithms.…”

Section: Introductionmentioning

confidence: 99%

Distributed Processing of Blind Source Separation

Alavi¹

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<p>Communication is performed by transmitting signals through a medium. It is common that signals originating from different sources are mixed in the transport medium. The operation of separating source signals without prior information about the sources is referred to as blind source separation (BSS). Blind source separation for wireless sensor networks has recently received attention because of low cost and the easy coverage of large areas. Distributed processing is attractive as it is scalable and consumes low power. Existing distributed BSS algorithms either require a fully connected pattern of connectivity, to ensure the good performance, or require a high computational load at each sensor node, to enhance the scalability. This motivates us to develop distributed BSS algorithms that can be implemented over any arbitrary graph with fully shared computations and with good performance. This thesis presents three studies on distributed algorithms. The first two studies are on existing distributed algorithms that are used in linearly constrained convex optimization problems, which are common in signal processing and machine learning. The studies are aimed at improving the algorithms in terms of computational complexity, communication cost, processors coordination and scalability. This makes them more suitable for implementation on sensor networks, thus forming a basis for the development of distributed BSS algorithms on sensor networks in our third study. In the first study, we consider constrained problems in which the constraint includes a weighted sum of all the decision variables. By formulating a constrained dual problem associated to the original constrained problem, we were able to develop a distributed algorithm that can be run both synchronously and asynchronously on any arbitrary graph with lower communication cost than traditional distributed algorithms. In the second study, we consider constrained problems in which the constraint is separable. By making use of the augmented Lagrangian function and splitting the dual variable (Lagrange multiplier) associated to each partial constraint, we were able to develop a distributed fully asynchronous algorithm with lower computational complexity than traditional distributed algorithms. The simplicity of the algorithm is the consequence of approximating the constraint on the equality of the decoupled dual variables. We also provide a measure of the inaccuracy in such an approximation on the optimal value of the primal objective function. Finally, in the third study, we investigate distributed processing solutions for BSS on sensor networks. We propose two distributed processing schemes for BSS that we refer to as scheme 1 and scheme 2. In scheme 1, each sensor node estimates one specific source signal while in scheme 2, by formulating a consensus optimization problem, each sensor node estimates all source signals in a fully shared computation manner. Our proposed algorithms carry the following features: low computational complexity, low power consumption, low data transmission rate, scalability and excellent performance over arbitrary graphs. Although all of our proposed algorithms share the aforementioned properties, each of them is superior in one or some of the features compared to the others. Comparative experimental results show that among all our proposed distributed BSS algorithms, a variant of scheme 1 performs best when all features are considered. This is achieved by making use of the concept of pairwise mutual information along with adding a sparsity assumption on the parameters of the model that is used in BSS.</p>

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A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions

Luo

Zhu

2018

IEEE Access

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Distributed linear blind source separation over wireless sensor networks with arbitrary connectivity patterns

Cited by 8 publications

References 16 publications

Blind Sparse Estimation of Intermittent Sources Over Unknown Fading Channels

Blind Sparse Estimation of Intermittent Sources Over Unknown Fading Channels

Distributed Processing of Blind Source Separation

A Comprehensive Survey on Blind Source Separation for Wireless Adaptive Processing: Principles, Perspectives, Challenges and New Research Directions

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