Non-asymptotic performance bounds of eigenvalue based detection of signals in non-Gaussian noise

Heimann, Ron; Leshem, Amir; Zehavi, Ephraim; Weiss, A.J.

doi:10.1109/icassp.2016.7472215

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The influence of non-Gaussian (more particularly, sub-Gaussian) distributed noise on the EBD, is explored in [101]. The maximal eigenvalue (ME) λ 1 is regarded as a TS, and an upper bound for the detector's performance, dependent on N RX , N and γ , is derived.…”

Section: Eigenvalue-based Detectionmentioning

confidence: 99%

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.INDEX TERMS 6G, cognitive radio, feature detection, human-centric wireless access, Internet of Things, probabilistic spectrum sensing, wireless communications.

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Section: Eigenvalue-based Detectionmentioning

confidence: 99%

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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“…In this section, the complexity of the OSO and COS schemes are compared. The computational complexity of the OSO scheme depends on two major operations; 1) ordering operation and 2) computation of the test statistic T C,OSO (X) in (23) in Cauchy noise environment and T G,OSO (X) in (24) in Gaussian noise environment. For convenience, we assume that the dispersion parameters {γ k } K k=1 are the same in Cauchy noise environment.…”

Section: Complexity Analysismentioning

confidence: 99%

Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments

et al. 2021

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“…Specifically, a random variable w is said to be σ 2 -sub-Gaussian with variance proxy σ 2 if it has zero mean and its moment generating function (MGF) satisfies E[e sw ] ≤ e σ 2 s 2 /2 for all s ∈ R. Clearly, the classic AWGN model is a special case of our model, in which w ∼ N (0, σ 2 ). A more general common communication model that our model captures is the case where the desired signal is attenuated by the fading channel and received with an additive external bounded interferer plus white Gaussian noise [42].…”

Section: A Transmission Schemementioning

confidence: 99%

Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks

Cohen

Leshem

2018

IEEE Trans. Signal Process.

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We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on a spectrum and energy efficient transmission scheme used to reduce the spectrum usage and energy consumption during the detection task. We propose a Spectrum and Energy Efficient Multiple Access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in SEEMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. SEEMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmission savings and narrowband transmission over MAC. Second, it can be implemented by simple dumb sensors (oblivious to observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish a finite sample analysis and an asymptotic analysis of the error probability with respect to the network size and provide system design conditions to obtain the exponential decay of the error. Specific performance analysis is developed for common noni.i.d. observation scenarios, including local i.i.d. observations, and Markovian correlated observations. Numerical examples demonstrate SEEMA performance.

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Non-asymptotic performance bounds of eigenvalue based detection of signals in non-Gaussian noise

Cited by 5 publications

References 30 publications

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments

Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks

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