Channel States Information based Spectrum Sensing Algorithm in Satellite Cognitive Communication Networks

In the context of the currently developed networks for the realization of future communications, the concept of Cognitive Radio (CR) has a significant place. Its implementation in modern ultra-dense networks (UDN) requires the development of novel and improved adaptive solutions for the main functionalities of a CR system. This technology has a great potential for solving the significant spectrum underutilization issue which has been established as characteristic for the traditional communication networks and hence, the continual and substantive research efforts in the recent years. The principal challenge for CRs is the optimization of spectrum utilization without creating unwanted interference for the incumbent (primary) users. Thus, a significant portion of the research is directed towards the vital spectrum sensing functionality. This paper reviews the potential uses of CR in UDN as well as the main approaches for modeling of spectrum sensing with respect to signal detection. The review focuses on local spectrum sensing techniques and presents the strengths and weaknesses of their mathematical definition. Specific attention is given to the channel models which are considered in the literature and to what kinds of features are extracted from the received signal to achieve accurate detection.

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Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks

Ad Hoc Networks ◽

10.1016/j.adhoc.2016.05.012 ◽

2017 ◽

Vol 58 ◽

pp. 231-238 ◽

Cited By ~ 22

Author(s):

Min Jia ◽

Xin Liu ◽

Zhisheng Yin ◽

Qing Guo ◽

Xuemai Gu

Keyword(s):

Communication Networks ◽

Spectrum Sensing ◽

Cooperative Spectrum Sensing ◽

Cluster Communication

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A Review of Cognitive Radio Spectrum Sensing Methods in Communication Networks

2018 International Conference on Communication and Signal Processing (ICCSP) ◽

10.1109/iccsp.2018.8524489 ◽

2018 ◽

Author(s):

H. B Sandya ◽

K Nagamani ◽

L Shavanthi

Keyword(s):

Cognitive Radio ◽

Communication Networks ◽

Spectrum Sensing ◽

Radio Spectrum

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An Analysis of Cognitive Radio Spectrum Sensing Methods in Communication Networks

2018 International Conference on Communication and Signal Processing (ICCSP) ◽

10.1109/iccsp.2018.8524444 ◽

2018 ◽

Cited By ~ 1

Author(s):

S Hemalatha ◽

E Prabhu

Keyword(s):

Cognitive Radio ◽

Communication Networks ◽

Spectrum Sensing ◽

Radio Spectrum

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Low Energy Consumption Compressed Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Radio Network

Sensors ◽

10.3390/s20051264 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1264 ◽

Cited By ~ 2

Author(s):

Yuan Fang ◽

Lixiang Li ◽

Yixiao Li ◽

Haipeng Peng ◽

Yixian Yang

Keyword(s):

Energy Consumption ◽

Cognitive Radio ◽

Compressed Sensing ◽

Tensor Product ◽

Communication Networks ◽

Spectrum Sensing ◽

Vital Role ◽

Sensor Nodes ◽

Sensing Matrix ◽

Low Dimensional

For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.

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