Double Detector for Sparse Signal Detection From One-Bit Compressed Sensing Measurements

Zayyani, Hadi; Haddadi, Farzan; Korki, Mehdi

doi:10.1109/lsp.2016.2613898

Cited by 34 publications

(19 citation statements)

References 23 publications

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“…where the expectation is equal to P( (y i = y i+1 ) ∧ (y i ′ = y i ′ +1 )|H 0 ) = : i = i ′ Replacing (B) into (22) results in (8).…”

Section: Resultsmentioning

confidence: 99%

“…To avoid the high sampling rate or high implementation complexity in Nyquist systems, sub-Nyquist approaches have gained more attention, in which the sampling rates lower than Nyquist rate is employed to detect spectral opportunities. One of these sub-Nyquist approaches is compressive sensing (CS) [20], [21] for detection of sparse signals [22] or spectrum sensing in cognitive radio framework [23]. However, there are some limitations on CS techniques.…”

Section: Introductionmentioning

confidence: 99%

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One-Bit Spectrum Sensing in Cognitive Radio Sensor Networks

Zayyani

Haddadi

Korki

2019

Circuits Syst Signal Process

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This paper proposes a spectrum sensing algorithm from one bit measurements in a cognitive radio sensor network. A likelihood ratio test (LRT) for the one bit spectrum sensing problem is derived. Different from the one bit spectrum sensing research work in the literature, the signal is assumed to be a discrete random correlated Gaussian process, where the correlation is only available within immediate successive samples of the received signal. The employed model facilitates the design of a powerful detection criteria with measurable analytical performance. One bit spectrum sensing criterion is derived for one sensor which is then generalized to multiple sensors. Performance of the detector is analyzed by obtaining closed-form formulas for the probability of false alarm and the probability of detection. Simulation results corroborate the theoretical findings and confirm the efficacy of the proposed detector in the context of highly correlated signals and large number of sensors.

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“…where the expectation is equal to P( (y i = y i+1 ) ∧ (y i ′ = y i ′ +1 )|H 0 ) = : i = i ′ Replacing (B) into (22) results in (8).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-Bit Spectrum Sensing in Cognitive Radio Sensor Networks

Zayyani

Haddadi

Korki

2019

Circuits Syst Signal Process

Self Cite

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“…Additionally, the GLRT has been leveraged in [6] to detect an unknown deterministic signal through a WSN reporting one-bit quantized measurements via noisy communication channels; this fusion rule has been then extended to cope with multibit measurements in [19]. Recent interesting applications of the GLRT also include distributed detection of arbitrarilypermuted one-bit quantized data [20], sparse signals [21] and one-bit quantized data in a sequential setup [22].…”

Section: B Related Workmentioning

confidence: 99%

“…Future directions will include design of Rao test for alternative, (even) more general and realistic measurement & channel models: (a) sensing models enjoying sparsity [21]; (b) energyefficient censoring sensors [41]; (c) time-correlated reporting channels [42]; (d) design of online precoders c k 's [40]; (e) unknown random signal parameters [43]; (f ) incompletelyspecified noise PDFs.…”

Section: Conclusion and Further Directionsmentioning

confidence: 99%

Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Ciuonzo

Javadi

Mohammadi

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a non-linear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations.

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“…In [36], a detector based on generalized likelihood ratio test (GLRT) is proposed for distributed detection of an unknown scalar deterministic signal with 1-bit data. For the detection of sparse signals, compressed measurements at the local sensors can be quantized into 1-bit data and then sent to the FC [15], [16]. In [15], a 1-bit detector based on GLRT is proposed for distributed detection of sparse deterministic signals.…”

Section: Introductionmentioning

confidence: 99%

Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

Wang

et al. 2019

IEEE Signal Process. Lett.

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Abstract-In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. We show that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization. Simulation results corroborate our theoretical analysis and show the superiority of the proposed Im-1-bit LMPT detector.

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Double Detector for Sparse Signal Detection From One-Bit Compressed Sensing Measurements

Cited by 34 publications

References 23 publications

One-Bit Spectrum Sensing in Cognitive Radio Sensor Networks

One-Bit Spectrum Sensing in Cognitive Radio Sensor Networks

Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios

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