Outage probability estimation technique based on a Bayesian model for cognitive radio networks

Elderini, Tarek; Kaabouch, Naima; Reyes, Hector

doi:10.1109/ccwc.2017.7868355

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Probabilistic techniques have been applied to networking questions of control and estimation, contemporary examples in the literature include: identification of optimal node placement in order to provide sufficient network coverage during the design phase [8], estimation of throughput and backlog in node-dense environments with latency-restricted traffic [9], and estimation of outages under different network conditions [10]. These approaches tend to be used when exhaustive solutions become computationally intractable or when there is insufficient information to use first-principles models.…”

Section: Qos In Industrial Wsnmentioning

confidence: 99%

Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Cameron

Naeem

2020

2020 16th IEEE International Conference on Factory Communication Systems (WFCS)

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Industry 4.0 applications rely upon timely and accurate data about plant and process within a production site. Whilst modern facilities tend to have this capability as a matter of course, older equipment may lack network connectivity. A lack of data-gathering capability represents a significant barrier-to-entry when undertaking any data-driven investigation or improvement programs. Wireless sensor networks (WSNs) can be used as a flexible and low-disruption technique to acquire data at the point of interest, however the data stream is often lossy when deployed in harsh conditions without costly adaptations to the environment. This paper introduces the F-QoS metric which is able to classify the quality of the data stream from a WSN (using only packet reception timestamps), at user-defined sampling rates with a constraint placed upon the maximum amount of missing data. The resulting classifications can be used in an offline fashion to select periods of high-quality data for modelling, or, in an online manner to assess the realtime performance of a WSN. The F-QoS metric is applied to a LoRaWAN network in a large commercial bakery with a low-disruption installation-the network links are strained by large metal obstructions and the endpoints are installed inside metal cabinets. Each node transmits on a 10s cycle, and the analysis shows that >70% of the data is suitable for sampling at a 30s rate. The results indicate that LoRaWAN is capable of data acquisition in an unadapted and challenging environment, with the recommendation that the raw sample rate should be triple the desired final sample rate.

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Section: Qos In Industrial Wsnmentioning

confidence: 99%

Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Cameron

Naeem

2020

2020 16th IEEE International Conference on Factory Communication Systems (WFCS)

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“…Various reconstruction models have been proposed by the researchers to estimate the signal from the sparse representation like greedy approach, combinatorial approach, thresholding approach, convex optimization approach, and nonconvex approach Bayesian approach. [24][25][26][27][28][29] Despite of being the global optimization technique and robust to noise, convex approach and nonconvex approach were difficult to implement for a data of large size because of the complexity and computation time. Greedy algorithm, on the other hand, was faster method and based upon the correlation based iterative method.…”

Section: Bayesian Compressive Sensingmentioning

confidence: 99%

Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

Nigam¹,

Pawar²,

Sharma³

2020

Int J Communication

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SummaryThis paper presents the implementation of a modified version of Bayesian relevance vector machine (RVM)‐based compressive sensing method on cognitive radio network with wavelet transform for spectrum hole detection. Bayesian compressive sensing is used in this work to deal with the complexity and uncertainty of the process. The dependency of the Bayesian compressive sensing on the knowledge of noise levels in the measurement has been relaxed through the proposed Bayesian RVM‐based compressive sensing algorithm. This technique recovers the wideband signals even with fewer measurements maintaining considerably good accuracy and speed. Wavelet transform is used in this paper to enable the detection of primary user (PU) even in the low regulated transmission from unlicensed user. The advantage of this approach lies in the fact that it enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation study is performed to evaluate the efficacy of the proposed technique over the cognitive radio environment. The performance of the proposed technique is compared with the conventional Bayesian approach on the basis of recovery error, recovery time and covariance to verify its superiority.

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“…A communication channel is in outage when SINR falls under certain threshold [42–46], as seen in

P_{outage} = P_{r} ()(, SINR \leq β)

where

β

is an assigned threshold.…”

Section: Channel Quality Indicator Metricsmentioning

confidence: 99%

“…Based on the same concept of the definition of outage probability, Elderini et al [46] proposed a Bayesian network that is composed of a graphical model and conditional probability distributions. As shown in Fig.…”

Section: Channel Quality Indicator Metricsmentioning

confidence: 99%

Channel quality estimation metrics in cognitive radio networks: a survey

Elderini¹,

Kaabouch²,

Reyes

2017

IET Communications

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Outage probability estimation technique based on a Bayesian model for cognitive radio networks

Cited by 7 publications

References 25 publications

Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

Channel quality estimation metrics in cognitive radio networks: a survey

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