Calculation of detection and false alarm probabilities in spectrum pooling systems

Hillenbrand, J.; Weiss, T.; Jondral, Friedrich K.

doi:10.1109/lcomm.2005.1413630

Cited by 124 publications

(53 citation statements)

References 3 publications

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“…It is known from [15] that, for a target false alarm probability , an increase in the sensing time maximizes probability of detection .̂is defined as the detection probability with maximum sensing time and is determined bŷ…”

Section: Casementioning

confidence: 99%

Maximizing Throughput with Wireless Spectrum Sensing Network Assisted Cognitive Radios

Ahmad

Yang

Lee

2015

International Journal of Distributed Sensor Networks

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In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic manner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference to the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then transmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN), eliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate sensing time from the SUs frame, hence increasing the transmission time, which maximizes the achievable throughput. Additionally, the sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a targeted high probability of detection. A low probability of false alarm increases the spectrum utilization, improving the achievable throughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden and exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the proposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that of conventional CRN.

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Section: Casementioning

confidence: 99%

Maximizing Throughput with Wireless Spectrum Sensing Network Assisted Cognitive Radios

Ahmad

Yang

Lee

2015

International Journal of Distributed Sensor Networks

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“…Furthermore, cooperative sensing can also decrease the spectrum sensing time [42]. But the challenges of cooperative sensing mainly include: the network architecture of centralized or distributed cooperation sensing [43]; detection fusion including decision fusion or data fusion [44,45]; and cooperative node selection. Furthermore, researches in [46,47] have improved the cooperation sensing performance from the space diversity and the abnormality detection perspectives.…”

Section: Spectrum Sensingmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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Facing the challenges on how to improve spectrum efficiency and how to realize heterogeneous network convergence in future wireless networks, a cognitive radio network (CRN) is proposed as one of the solutions. This has become a major research topic in recent years and it is timely to give an overview of the development of CRN and to summarize key issues and technologies. The fundamental concepts of CRN, including the cognitive cycle model, the network architecture, and the cognitive ability and intelligent decision functions, are introduced in detail based on recent advances. Key issues for each topic, followed with recent research on theory and method, are then classified and the industrialization developments of CRN testbeds based on TD-LTE cellular system and standards are briefly presented. Finally, conclusions are reached on the perspectives and directions of future development.cognitive radio networks, network architecture, cognitive ability, spectrum sensing, intelligent decision and dynamic spectrum allocation, end-to-end performance Driven by the requirements of ubiquitous wireless access and personalized wireless applications [1], worldwide, numerous wireless technologies are invading our lives, such as evolutionary telecommunication technologies, wideband wireless access and short distance wireless technologies. Furthermore, the concept of the internet of things (IoT) has become widespread based on the development of wireless sensor networks. With the explosive development of wireless technologies, several challenges must be addressed. The electromagnetic spectrum, whose use is licensed and managed by government, has been scarce for allocation to ubiquitous wireless applications. However, contrary to the physical scarcity of the available spectrum, the report published by the federal communication commission (FCC) shows that over 60 % of the licensed spectrum below 6 GHz remains underused [2,3]. The measurement results in Figure 1 taken in Beijing over a one month period, also indicate low spectrum use consistent with the results released by the FCC. Therefore, improvement of spectrum efficiency is the first challenge to be examined. On the other hand, the end-to-end performance of wireless networks is considered to be the performance indicator for ubiquitous and personalized wireless applications [4], such as end-to-end throughput, end-to-end delay and routing scheme, which take into account all network elements in a data transmission flow across different layers for the global optimization from the network aspect. Because the property of heterogeneity causes low radio resource use and mutual interference among heterogeneous networks, the other big challenge is that heterogeneous network convergence is needed to improve the end-to-end performance of wireless networks.Cognitive radio (CR), proposed by Mitola in 1999 [5,6], is considered a feasible solution for improving spectrum efficiency. Furthermore, a cognitive radio network (CRN), which is defined as a wireless network with the capabilities

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“…Cooperative detection techniques, including Likelihood ratio criterion approaches (Bayesian test [5], Neyman Pearson Test [6], etc), correspond in fact to a suitable weighting of SUs decisions. However, the proposed weights expressions supposes that the local detection and false alarm probabilities of each SU are known.…”

Section: Formulation Of the Online Learningmentioning

confidence: 99%

A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing

Nasr

Cherif

2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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Abstract-1 In cognitive radio systems, the accuracy of spectrum sensing depends on the received primary signal strength at the secondary user (SU). In fact, a single node sensing would be compromised if the received signal strength is not high enough to be detected by this node. In this paper, we propose a cooperative decision fusion rule based on adaptive linear combiner. The weights which correspond to confidence levels affected to SUs, are determined adaptively using the Normalized Least Mean Squares (NLMS) and the Recursive Mean Squares (RLS) algorithms. The proposed algorithms combine the SUs decisions with the adaptive confidence levels to track the surrounding environment. Simulation results show a high adaptability of the proposed scheme, as the operating conditions change. Furthermore, the proposed algorithms do not necessitate a prior knowledge about the PU features and are very efficient compared to conventional decision fusion techniques.

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Calculation of detection and false alarm probabilities in spectrum pooling systems

Cited by 124 publications

References 3 publications

Maximizing Throughput with Wireless Spectrum Sensing Network Assisted Cognitive Radios

Maximizing Throughput with Wireless Spectrum Sensing Network Assisted Cognitive Radios

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing

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