A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations

Bahramnejad, Somayeh; Movahhedinia, Naser

doi:10.1007/s00607-022-01072-7

Cited by 11 publications

(5 citation statements)

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“…Thrane et al [21] further explored this by leveraging satellite imagery with deep learning for signal strength prediction. Bahramnejad and Movahhedinia presented an analytical framework for estimating the reliability of V2V communications within cognitive radio-VANETs, integrating a Markov model and an ANN model for streamlined reliability estimation [22].…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

Muktar,

Fono,

Zongo

2023

Electronics

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In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

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Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

Muktar,

Fono,

Zongo

2023

Electronics

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“…Therefore, the hardware reliability of the receiving drone, the success of the transmission, and the number of transmitters are the factors that define reliable communications. Hence, the reliability of communications (RoC) is written as [15] 1 ( ) 1…”

Section: Reliability Estimation Of Drone Communicationmentioning

confidence: 99%

“…RoTi (t) is the reliability of transmission for the ith drone, and n is the redundant transmission value. RoT can be calculated as [15] ( )…”

Section: Reliability Estimation Of Drone Communicationmentioning

confidence: 99%

“…The motion control system, which is reliant on the distance between the drones and their signal strength, enables the drones to successfully connect and subsequently transmit data at fast speeds [12][13][14]. A dependable and effective cooperative MAC protocol was put up by [15] to increase communication dependability. For extremely reliable multi-hop message distribution under a variety of channel situations, [16] presented a cooperative communication strategy.…”

Section: Introductionmentioning

confidence: 99%

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Reliability estimation for drone communications by using an MLP-based model

Shah

Karabulut

2022

International Advanced Researches and Engineering Journal

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Unmanned aerial vehicles (UAVs) or drones have been widely employed in both military and civilian tasks due to their reliability and low cost. UAVs ad hoc networks also acknowledged as flying ad-hoc networks (FANETs), are multi-UAV systems arranged in an ad hoc manner. In order to maintain consistent and effective communication, reliability is a prime concern in FANETs. This paper presents an analytical framework to estimate the reliability of drones’ communication in FANETs. The proposed system takes into account the reliability of communications in FANETs, including channel fading. The suggested analytical investigation is used to generate a dataset, then an artificial neural network (ANN) based multi-layer perceptron (MLP) model is used to estimate the reliability of drones’ communication. Moreover, to define the best MLP model with hidden layers, the correlation coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are obtained. Moreover, numerical results are presented which verify analytical studies.

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“…Furthermore, the 2011 IEEE 802. 15 (Bahramnejad, 2022). Moreover, the 2018 version describes the Optical Camera Communication (OCC) as the IEEE's rst standard to use OWC in personal area networks (PANs) as the su x well-known as (OWPANs).…”

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confidence: 99%

Car To Car Communication Using RF Cognitive Radio with VLC Common Control Channel

Okasha

Zekry

Newagy

2023

Preprint

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This paper combines the advantages of both cognitive radio (CR) communication and visible light communication (VLC) for car-to-car applications to achieve a higher data rate, low delay, minimum outage probability, minimum cost, and minimum bit error rate (BER). CR technology hops among the existing radio frequency (RF) available channels to increase the RF spectrum usage efficiency and dodge the scarcity limitation. Moreover, using CR as a license-free application will reduce car-to-car communication costs. However, CRs require a common control channel (CCC) to communicate the spectrum availability map within the CR network and to inform the receiver end about the change in the transmitter-end channel. Therefore, the CCC is the bottleneck in the car-to-car CR network. Therefore, we explore the types of CCCs and use each of them to solve this bottleneck issue. In the proposed scheme, we assume alternative CCCs such as licensed overlay, unlicensed CR overlay in the ISM band, underlay CR, and using VLC. A MATLAB simulation for a car-to-car framework is built to demonstrate and compare these types of CCC through the chosen metrics. Our metrics include data rate, delay, outage probability, cost, and bit error rate. The comparison analyses shall include comparing VLC versus the other CCC types. We compare practical systems, not the theoretically achievable system therefore the results might prefer a CCC type according to one metric and prefer another type of CCC due to the second metric. Our results show that VLC can achieve up to 90% of the licensed data rate with a small outage probability of 21.2% while maintaining moderate BER and delay. In addition, VLC presents the minimum cost, and as the introduced combined metric it placed second after the licensed type with a score of 84.2%. In conclusion, with the VLC’s bright future of expansion and growing the car-to-car application, we have proven VLC is worthy of implementation practically in modern cars.

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A reliability estimation framework for cognitive radio V2V communications and an ANN-based model for automating estimations

Cited by 11 publications

References 50 publications

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

Reliability estimation for drone communications by using an MLP-based model

Car To Car Communication Using RF Cognitive Radio with VLC Common Control Channel

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