Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis

Pradhan, Pyari Mohan; Panda, Ganapati

doi:10.1007/s11277-016-3645-6

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…Also, the approximations ( 53) and ( 59), derived from Fisher and Cochran studies, respectively, produce good results in agreement with the exact results. On the contrary, the GA (30) underestimates the minimum number of required active sensors, while the arcsine approximation (36) overestimates the number of active sensors. Therefore, the approximations based on the incomplete beta distribution (Fisher, Cochran, and Carter) are more accurate than the approximations based on the binomial distribution (GA, arcsine, and Camp-Poulson).…”

Section: Numerical Results: Validation and Discussionmentioning

confidence: 93%

“…Herein, we focus on a centralized approach wherein an FC collects the reports of the SUs. In addition, the fusion operation can be performed in several different ways [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]: this paper mainly focuses on hard reporting (HR) , where the SUs report their hard decisions to the FC. Anyway, we also consider a soft reporting (SR) case, where the SUs report their soft decisions to the FC, which aggregates these values using equal-gain combining (EGC).…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

Rugini,

Banelli

2024

Sensors

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This paper presents a performance analysis of centralized spectrum sensing based on compressed measurements. We assume cooperative sensing, where unlicensed users individually perform compressed sensing and send their results to a fusion center, which makes the final decision about the presence or absence of a licensed user signal. Several cooperation schemes are considered, such as and-rule, or-rule, majority voting, soft equal-gain combining (EGC). The proposed analysis provides simplified closed-form expressions that calculate the required number of sensors, the required number of samples, the required compression ratio, and the required signal-to-noise ratio (SNR) as a function of the probability of detection and the probability of the false alarm of the fusion center and of the sensors. The resulting expressions are derived by exploiting some accurate approximations of the test statistics of the fusion center and of the sensors, equipped with energy detectors. The obtained results are useful, especially for a low number of sensors and low sample sizes, where conventional closed-form expressions based on the central limit theorem (CLT) fail to provide accurate approximations. The proposed analysis also allows the self-computation of the performance of each sensor and of the fusion center with reduced complexity.

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Section: Numerical Results: Validation and Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

Rugini,

Banelli

2024

Sensors

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“…The information combining schemes used by the FC or SUs can be categorized as either hard or soft combining schemes. The most common hard combining algorithms are AND combining, OR combining, M-outof-N combining, and quantized hard combining, while soft combining algorithms include selection combining, maximal ratio combining, equal gain combining, and square law combining [83]. The soft combining approach provides the best detection performance, but at the cost of additional control channel overhead.…”

Section: Spectrum Sharingmentioning

confidence: 99%

Machine Learning in NextG Networks via Generative Adversarial Networks

Ayanoglu,

Davaslioglu,

Sagduyu

2022

Preprint

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Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semisupervised data. Third, they facilitate increased resolution. Fourth, they enable the recovery of corrupted bits in the spectrum. The paper provides the basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)-iii) above, and future research directions. As a use case of GAN for nextG communications, we show that a GAN can be effectively applied for anomaly detection in signal classification (e.g., user authentication) outperforming another state-of-the-art ML technique such as an autoencoder.

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“…Clustering formation and optimization process is a non-linear multi-objective optimization problem [21]. Therefore, in the actual optimization process, heuristic optimization algorithm is often used to solve multi-objective optimization problems.…”

Section: Clustering Generation and Optimization Modelmentioning

confidence: 99%

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

Bin

Sun

2020

Sensors

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The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway systems is complex, the construction cost of wired monitoring systems is high, and it is difficult to achieve full coverage in the operation area of harsh environments, so wireless sensor networks are suitable for the status monitoring of railway systems. Energy resources of nodes are the basis of ensuring the lifecycle of wireless sensor networks, but severely restrict the sustainability of wireless sensor networks. A construction method of special wireless sensor networks for railway status monitoring, and an optimal energy resources allocation method of wireless sensor networks for intelligent railway systems are proposed in this paper. Through cluster head selection and rotating probability model, clustering generation and optimization model, and partial coverage model, the energy consumption of nodes can be minimized and balanced. The result of simulation experiment proved that the lifetime of wireless sensor networks can be maximized by the optimal energy resources allocation method based on clustering optimization and partial coverage model, based on polynomial time algorithm.

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Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis

Cited by 11 publications

References 56 publications

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

Performance Analysis of Centralized Cooperative Schemes for Compressed Sensing

Machine Learning in NextG Networks via Generative Adversarial Networks

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

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