Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks

Feng, Yuqing; Xu, Wenjun; Zhang, Zhi; Wang, Feng‐Yu

doi:10.3390/s22072620

Cited by 4 publications

(6 citation statements)

References 41 publications

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“…Continuous hidden Markov model. [16] 3D positioning and power allocation. Joint optimization solution with adapted particle swarm optimization.…”

Section: Research Topic On Uav-based Sensing Modeling Approach Refere...mentioning

confidence: 99%

“…The model leverages a dataset to distinguish between voice and data communication, aiding spectrum prediction. A novel approach to boosting spectrum utilization in cognitive UAV networks is mentioned in [ 16 ]. This approach integrates a continuous hidden Markov model with a unique signal-to-noise ratio (SNR) estimation method within a CSS scheme.…”

Section: Introductionmentioning

confidence: 99%

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UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

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Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks.

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“…Continuous hidden Markov model. [16] 3D positioning and power allocation. Joint optimization solution with adapted particle swarm optimization.…”

Section: Research Topic On Uav-based Sensing Modeling Approach Refere...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Then, an HMM is a probabilistic time series model, doubly stochastic, which includes the transition of hidden states and emitting observations [90]. The hidden state transition, which follows Markov chains, is the actual state within the system, mapped by observable states, which are directly observed and have a correlation with the hidden states [90][91][92][93].…”

Section: Hidden Markov Models (Hmms)mentioning

confidence: 99%

“…There are three problems that need to be solved to use an HMM in an integrity assessment and health-status prediction of equipment [83,93]:…”

Section: Hidden Markov Models (Hmms)mentioning

confidence: 99%

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

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The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

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“…Ref. [3] proposed a cooperative spectrum sensing scheme for cognitive UAV networks based on a CHMM (Continuous Hidden Markov Model) with a novel SNR (Signal-to-Noise Ratio) estimation method.…”

mentioning

confidence: 99%