A Test Methodology for Evaluating Cognitive Radio Systems

Thompson, Jared J.; Hopkinson, Kenneth M.; Silvius, Mark D.

doi:10.1109/twc.2015.2452268

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…A Cognitive Radio Test Methodology (CRATM) that infers the device cognition based on the PU and SU performances is presented in [26]. The cognition is defined according to the SU capacity of improving its transmission rate and reducing the interference to the PU (inferred from the PU throughput).…”

Section: Related Workmentioning

confidence: 99%

A Conformance Testing Methodology and System for Cognitive Radios

Silva

Balieiro

Mendoça

et al. 2021

Wireless Communications and Mobile Computing

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The fifth generation (5G) of mobile networks has started its operation in some countries and is aimed at meeting demands beyond the current system capabilities such as the huge amount of connected devices from IoT applications (e.g., smart cities), explosive growth of high-speed mobile data traffic (e.g., ultrahigh definition video streaming), and ultrareliable and low latency communication (e.g., autonomous vehicle). To attend to these needs, the electromagnetic spectrum must be made available, but the static spectrum allocation policy has caused a spectrum shortage and impaired the employment/expansion of the wireless systems. To overcome this issue, the dynamic spectrum access (DSA) has been promoted in 5G/6G networks, which is enabled by the cognitive radio (CR) technology. Although diverse mechanisms have been developed to tackle the challenges that emerge in different CR layers/functionalities, a standardized testing methodology and system for CR is still immature. Existing standards or methodologies and systems for CR only focus on the definition of network technologies (e.g., IEEE 802.22 and IEEE 802.11af), performance evaluation of CR algorithms/mechanisms, or definition of the device cognition level via performance results or psychometric approaches, not covering systems/methodologies to verify if the device meets the CR capabilities and regulatory policies, neglecting the conformance testing. In this respect, this paper proposes a flexible methodology and system for CR conformance testing under two perspectives, functionalities and limits. We instantiate it by using the Universal Software Radio Peripheral (USRP) software-defined radio platform and present a proof-of-concept with a conformance metric. The results show the feasibility of our proposal.

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Section: Related Workmentioning

confidence: 99%

A Conformance Testing Methodology and System for Cognitive Radios

Silva

Balieiro

Mendoça

et al. 2021

Wireless Communications and Mobile Computing

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“…However, there are various studies on evaluating the performance of CRs. A cognitive radio test methodology to test a CR system is presented in [31]. The effect of cognitive engine on both SU and PU performance is measured and evaluated.…”

Section: B Related Work On Cr Intelligencementioning

confidence: 99%

Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios

Dabaghchian¹,

Alipour-Fanid²,

Liu³

et al. 2017

Preprint

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Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognitive capabilities and intelligence is largely open. Deriving the intelligence measure of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different cognitive capabilities. In this paper, for the first time, we propose a data-driven methodology to quantitatively measure the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance data on different metrics. Then we apply factor analysis on the performance data to identify and quantize the intelligence factors and cognitive capabilities of the CR. More specifically, we present a case study consisting of 144 different types of CRs. The CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, processing speed, and algorithmic complexity. Five intelligence factors are identified for the CRs through our data analysis. We show that these factors comply well with the nature of the tested CRs, which validates the proposed intelligence measure methodology.

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