A Hybrid Cognitive Radio System: A Combination of Underlay and Overlay Approaches

Oh, Jinhyung; Choi, Wan

doi:10.1109/vetecf.2010.5594302

Cited by 72 publications

(56 citation statements)

References 14 publications

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“…124 GSM channels were considered with 62 channels each for uplink and downlink. The improvement of the optimized ANN model over the unoptimize model is computed using (3).…”

Section: Resultsmentioning

confidence: 99%

“…Advanced Cognitive Radio (CR) or Adaptive Spectrum Sharing (ASS) is one of the ways to optimize our wireless communications technologies for high data rates whilst maintaining user desired quality of service (QoS) requirements. CR is a radio equipped with the capability of awareness, perception, adaptation and learning of its radio frequency (RF) environment [3]; through which many of the digital signal processing that were traditionally done in hardware are implemented via software. Irrespective of the definition of CR, it has the followings basic characteristics: observation, adaptability and intelligence.…”

Section: Introductionmentioning

confidence: 99%

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Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

Iliya

Goodyer

Góngora

et al. 2014

2014 14th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN's through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach

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“…124 GSM channels were considered with 62 channels each for uplink and downlink. The improvement of the optimized ANN model over the unoptimize model is computed using (3).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

Iliya

Goodyer

Góngora

et al. 2014

2014 14th UK Workshop on Computational Intelligence (UKCI)

View full text Add to dashboard Cite

show abstract

“…The feedback of this study is based on the codes, on the basis of the fixed number of so called limited feedback, limited feedback user preset time and user base, every time all determine the code in this [6] quantization scheme, this scheme adopts the random code, by literature [7] can know, random code this quantification can achieve optimal system performance. In the option code standards, this article adopts the way of string distance, choose the shortest distance, the specific criteria, such as type (11) …”

Section: Feedback Schemementioning

confidence: 99%

An Beamforming Algorithm on Limited Feedback MIMO two-Way Relay Cognitive Networks

Zhang¹,

Wang²

2013

IJSIP

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“…Inspired by the inherent benefits of the overlay and underlay spectrum access, hybrid overlay-underlay CRNs have been proposed in [6,7]. Specifically, Oh and Choi [6] derived the optimal switching rate between spectrum access modes in order to maximize throughput of a hybrid overlay-underlay CRN.…”

Section: Introductionmentioning

confidence: 99%

Hybrid spectrum access with relay assisting both primary and secondary networks under imperfect spectrum sensing

Chu

Zepernick

Phan

2016

J Wireless Com Network

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This paper proposes a novel hybrid interweave-underlay spectrum access for a cognitive amplify-and-forward relay network where the relay forwards the signals of both the primary and secondary networks. In particular, the secondary network (SN) opportunistically operates in interweave spectrum access mode when the primary network (PN) is sensed to be inactive and switches to underlay spectrum access mode if the SN detects that the PN is active. A continuous-time Markov chain approach is utilized to model the state transitions of the system. This enables us to obtain the probability of each state in the Markov chain. Based on these probabilities and taking into account the impact of imperfect spectrum sensing of the SN, the probability of each operation mode of the hybrid scheme is obtained. To assess the performance of the PN and SN, we derive analytical expressions for the outage probability, outage capacity, and symbol error rate over Nakagami-m fading channels. Furthermore, we present comparisons between the performance of underlay cognitive cooperative radio networks (CCRNs) and the performance of the considered hybrid interweave-underlay CCRN in order to reveal the advantages of the proposed hybrid spectrum access scheme. Eventually, with the assistance of the secondary relay, performance improvements for the PN are illustrated by means of selected numerical results.

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A Hybrid Cognitive Radio System: A Combination of Underlay and Overlay Approaches

Cited by 72 publications

References 14 publications

Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks

An Beamforming Algorithm on Limited Feedback MIMO two-Way Relay Cognitive Networks

Hybrid spectrum access with relay assisting both primary and secondary networks under imperfect spectrum sensing

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