Simultaneous Estimation of Primary and Cross-Channel Gains for Underlay Cognitive Radios

Yao, Junteng; Jin, Ming; Guo, Qinghua; Li, Youming

doi:10.1109/access.2018.2843792

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…both PUs and secondary users are transmitting concurrently in the same spectrum band provided the interference caused by secondary users to PUs is below some threshold [27]. The underlay CRNs have to gain more recognition due to its high efficiency and requiring no information of the primary signal [28]. In underlay CRN, for secure transmission, A 's transmit power

P_{a v}

should be kept below the peak interference power threshold.…”

Section: System Modelmentioning

confidence: 99%

Secure transmission using optimal antenna selection for MIMO underlay CRN with multiple primary users

Thakur

Singh

2020

IET Communications

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P_{a v}

should be kept below the peak interference power threshold.…”

Section: System Modelmentioning

confidence: 99%

Secure transmission using optimal antenna selection for MIMO underlay CRN with multiple primary users

Thakur

Singh

2020

IET Communications

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“…It leads to extensive studies on the underlay paradigm using a wide range of advanced techniques (e.g., power and admission control [12], energy harvesting [13], and, full-duplex [14]). The above works have considered the interference gap between a secondary TX and its paired RX to be negligible, which become rather invalid in 5G supporting stricter requirements than before.…”

Section: A Prior Workmentioning

confidence: 99%

“…Lastly, the fact that a non-zero lower bound of OP exists encourages the TX to access the spectrum with a certain probability, even though very large numbers of I are sensed. 14…”

Section: Rx Txmentioning

confidence: 99%

Sense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks

Kim

Cha

Kim

et al. 2019

IEEE Trans. Wireless Commun.

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Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is important to explore whether spectrum access opportunities are available, but conventional CR based on transmitter (TX) sensing cannot be used to this end because the paired receiver (RX) may experience different levels of interference, according to the extent of their separation, blockages, and beam directions. To address this problem, this paper proposes a novel form of medium access control (MAC) termed sense-andpredict (SaP), whereby each secondary TX predicts the interference level at the RX based on the sensed interference at the TX; this can be quantified in terms of a spatial interference correlation between the two locations. Using stochastic geometry, the spatial interference correlation can be expressed in the form of a conditional coverage probability, such that the signal-to-interference ratio (SIR) at the RX is no less than a predetermined threshold given the sensed interference at the TX, defined as an opportunistic probability (OP). The secondary TX randomly accesses the spectrum depending on OP. We optimize the SaP framework to maximize the area spectral efficiencies (ASEs) of secondary networks while guaranteeing the service quality of the primary networks. Testbed experiments using USRP and MATLAB simulations show that SaP affords higher ASEs compared with CR without prediction.

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“…Also, CR approach is utilized for solving the spectrum scarcity problem in modern wireless communications. Enhancing the spectrum efficiency is the major principle for the development of CR methods (Akyildiz et al , 2008; Wang and Liu, 2011; Yao et al , 2018). In spectrum sensing (SS), sensing delay becomes more popular parameter in spectrum sensing approaches.…”

Section: Introductionmentioning

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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Simultaneous Estimation of Primary and Cross-Channel Gains for Underlay Cognitive Radios

Cited by 8 publications

References 21 publications

Secure transmission using optimal antenna selection for MIMO underlay CRN with multiple primary users

Secure transmission using optimal antenna selection for MIMO underlay CRN with multiple primary users

Sense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks

A hybrid model for energy efficient spectrum sensing in cognitive radio

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