An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks

Surampudi, Atchutananda; Kalimuthu, K.

doi:10.1109/iicip.2016.7975334

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Matching filter detection is the optimal signal detection algorithm when all the information of the PU is known. However, the disadvantages of this method are also very obvious, because it requires prior knowledge of the PU that include the packet format and sequence, and the modulation type [6,7]. The calculation of cyclic eigenvalue detection is relatively complicated, so it cannot be detected in real time; therefore, rapid detection is not possible [8].…”

Section: Related Workmentioning

confidence: 99%

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

Zhang

Wang

et al. 2019

J Wireless Com Network

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To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

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Section: Related Workmentioning

confidence: 99%

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

Zhang

Wang

et al. 2019

J Wireless Com Network

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“…The accuracy, as well as detection of this approach, was better, but the system performance was affected due to the dynamic noise. Surampudi and Kalimuthu (2016) employed adaptive threshold-based detection by implementing artificial neural networks. This approach had better spectral sensing performance, but the PU detection was affected in noisy field.…”

Section: Motivationmentioning

confidence: 99%

A hybrid model for energy efficient spectrum sensing in cognitive radio

Bhowmik

Malathi

2019

IJICC

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Purpose Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR. Design/methodology/approach This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs. Findings The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s. Research implications The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on. Originality/value A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

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“…Uplink signatures from the trapped lives, can be received and downlink broadcast can be done. The sensing of uplink signatures can be done efficiently by using machine learning as in [16] for cognitive radio networks. All the balloons will be interconnected wirelessly and linked to a fusion centre (FC) which is in-turn The balloons can be assumed to be arranged deterministically or as a Poisson point process ψ, stabilized at a height h (black circular dots).…”

Section: A How Does the Libnet Work?mentioning

confidence: 99%

Can balloons produce Li-Fi? A disaster management perspective

Surampudi

Chapalgaonkar

Paventhan

2018

2018 Global LIFI Congress (GLC)

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Natural calamities and disasters disrupt the conventional communication setups and the wireless bandwidth becomes constrained. A safe and cost effective solution for communication and data access in such scenarios is long needed. Light-Fidelity (Li-Fi) which promises wireless access of data at high speeds using visible light can be a good option. Visible light being safe to use for wireless access in such affected environments, also provides illumination. Importantly, when a Li-Fi unit is attached to an air balloon and a network of such Li-Fi balloons are coordinated to form a Li-Fi balloon network, data can be accessed anytime and anywhere required and hence many lives can be tracked and saved. We propose this idea of a Li-Fi balloon and give an overview of it's design using the Philips Li-Fi hardware. Further, we propose the concept of a balloon network and coin it with an acronym, the LiBNet. We consider the balloons to be arranged as a homogenous Poisson point process in the LiBNet and we derive the mean co-channel interference for such an arrangement.

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An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks

Cited by 17 publications

References 10 publications

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

A hybrid model for energy efficient spectrum sensing in cognitive radio

Can balloons produce Li-Fi? A disaster management perspective

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