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Journal articles on the topic 'Networks with underlay cognitive nodes'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Hussain, Syed Imtiaz, Mohamed M. Abdallah, Mohamed-Slim Alouini, Khalid Qaraqe, and Mazen Hasna. "Relay selection in underlay cognitive networks with fixed transmission power nodes." Transactions on Emerging Telecommunications Technologies 24, no. 7-8 (2013): 734–47. http://dx.doi.org/10.1002/ett.2691.

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2

Abdulghafoor, Omar B., Mahamod Ismail, Rosdiadee Nordin, and Musbah Shaat. "Fast and Distributed Power Control Algorithm in Underlay Cognitive Radio Networks." Journal of Communications 9, no. 8 (2014): 634–43. https://doi.org/10.5281/zenodo.56435.

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The power allocation in an underlay cognitive radio network rises up several challenges due to rapid utilization of the available spectrum hole. In this paper, we propose a non-cooperative power-game algorithm to solve the power allocation problem in an underlay cognitive radio network with two main objectives: (i) to provide good quality of service to cognitive radio nodes, and (ii) to protect the transmission of primary users from the interference generated by nearby cognitive radio. These objectives have been assured by including the following constraints: transmit power on each cognitive n

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3

Alhamad, Raed, and Hatem Boujemaa. "Intelligent Reflecting Surfaces with Adaptive Transmit Power for Underlay Cognitive Radio Networks." Wireless Communications and Mobile Computing 2022 (August 17, 2022): 1–7. http://dx.doi.org/10.1155/2022/2787466.

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In this article, we study the performance of intelligent reflecting surfaces (IRS) with adaptive transmit power (ATP). The power of secondary source is adapted to have a low interference at primary destination. IRS with ATP offers 14, 20, 26, 32, and 38 dB gains versus the absence of IRS for N = 8,16,32,64 , and 128 reflectors. Rayleigh channels are studied with arbitrary positions of primary and secondary nodes.

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4

Toan, Hoang Van, and Vo Nguyen Quoc Bao. "Performance Analysis of Cognitive Two-Way Networks with Interference from Primary User over Nakagami-m Fading Channels." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 1 (2017): 29. http://dx.doi.org/10.31130/jst.2017.35.

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A cognitive underlay two-way relay network taking into account interference links from primary transmitter to secondary receivers over Nakagami-m fading channels is analyzed in this article. In this model, a secondary system including two terminal nodes exchanges data through a decode-and-forward (DF) relay node. Under the underlay approach, all secondary transmitter must adjust transmit power to protect the primary communications. We derive the exact and asymptotic closed-form expression for the secondary system outage probability over Nakagami-m fading channels showing the system diversity.

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Phu, Tran Tin, Ha Duy-Hung, Minh Quang Pham, Thanh Binh Nguyen, and Luong Nhat Nguyen. "Performance of multi-hop cognitive MIMO relaying networks with joint constraint of intercept probability and limited interference." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 44~50. https://doi.org/10.12928/TELKOMNIKA.v19i1.18006.

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In this paper, we propose a multi-hop multiple input multiple output (MIMO) decode-and-forward relaying protocol in cognitive radio networks. In this protocol, a multi-antenna secondary source attempts to send its data to a multi antenna secondary destination with assistance of multiple intermediate multi antenna nodes, in presence of a multi-antenna secondary eavesdropper. A primary network includes a primary transmitter and a primary receiver which are equipped with multiple antennas, and use transmit antenna selection (TAS) and selection combining (SC) to communicate with each other. Operat

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6

Han, Xu, Lei Xue, Ying Xu, and Zunyang Liu. "A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data." Sensors 20, no. 8 (2020): 2245. http://dx.doi.org/10.3390/s20082245.

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In the underlay cognitive radio networks, the radio environment maps (REMs) estimation is the main challenge in sensing the idle wireless spectrum resources. Traditional deep learning-based algorithms estimate the REMs on the basis of the high-quality, large-scale complete training images. However, collecting the complete radio environment images is time-consuming and requires a numerous number of power spectrum sensing nodes. For this reason, we propose a generative adversarial networks-based pixel regression framework (PRF) for underlay cognitive radio networks. The PRF algorithm relaxes the

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7

Kozić, Nadica, Vesna Blagojević, Aleksandra Cvetković, and Predrag Ivaniš. "Performance Analysis of Wirelessly Powered Cognitive Radio Network with Statistical CSI and Random Mobility." Sensors 23, no. 9 (2023): 4518. http://dx.doi.org/10.3390/s23094518.

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The relentless expansion of communications services and applications in 5G networks and their further projected growth bring the challenge of necessary spectrum scarcity, a challenge which might be overcome using the concept of cognitive radio. Furthermore, an extremely high number of low-power devices are introduced by the concept of the Internet of Things (IoT), which also requires efficient energy usage and practically applicable device powering. Motivated by these facts, in this paper, we analyze a wirelessly powered underlay cognitive system based on a realistic case in which statistical

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8

Bakht, Khush, Furqan Jameel, Zain Ali, et al. "Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks." Wireless Communications and Mobile Computing 2019 (November 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/2472783.

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The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment soluti

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9

Tran Tin, Phu, Dang The Hung, Tan Nguyen, Tran Duy, and Miroslav Voznak. "Secrecy Performance Enhancement for Underlay Cognitive Radio Networks Employing Cooperative Multi-Hop Transmission with and without Presence of Hardware Impairments." Entropy 21, no. 2 (2019): 217. http://dx.doi.org/10.3390/e21020217.

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In this paper, we consider a cooperative multi-hop secured transmission protocol to underlay cognitive radio networks. In the proposed protocol, a secondary source attempts to transmit its data to a secondary destination with the assistance of multiple secondary relays. In addition, there exists a secondary eavesdropper who tries to overhear the source data. Under a maximum interference level required by a primary user, the secondary source and relay nodes must adjust their transmit power. We first formulate effective signal-to-interference-plus-noise ratio (SINR) as well as secrecy capacity u

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10

K, Shashi Raj, K. Siddesh G, Mallikarjunaswamy S, and Vivek Raj K. "Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs." Indian Journal of Science and Technology 13, no. 41 (2020): 4332–50. https://doi.org/10.17485/IJST/v13i41.687.

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Abstract <strong>Background/Objectives</strong>: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. <strong>Methods</strong>: The proposed Interference Resil

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11

Pravin M, Et al. "Distribution of Heterogeneous Traffic Resources for 5G Cognitive Radio Networks based on Cooperative Learning and QOE." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 570–75. http://dx.doi.org/10.17762/ijritcc.v11i9.8845.

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In addition to offering faster speeds and lower latency, 5G networks provide enhanced availability, significantly higher capacity, improved stability, and superior connectivity. The Mean Opinion Score (MOS), which quantifies the subjective quality of the user experience, has emerged as a widely accepted metric for assessing the quality of various types of network traffic. As we move towards the 5G era, measuring end-user quality has become increasingly important in the evolution of wireless communications. This paper presents a resource allocation approach for 5G cognitive radio networks that

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12

Minh Nam, Pham, Ha Duy Hung, Lam-Thanh Tu, Pham Viet Tuan, Tran Trung Duy, and Tan Hanh. "Outage Performance of Interference Cancellation-Aided Two-Way Relaying Cognitive Network with Primary TAS/SC Communication and Secondary Partial Relay Selection." Electronics 11, no. 22 (2022): 3645. http://dx.doi.org/10.3390/electronics11223645.

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In this paper, we propose a two-way relaying scheme using digital network coding in an underlay cognitive radio network. In the proposed scheme, the transmit antenna selection and selection techniques are combined using a primary transmitter and a primary receiver, respectively. In the secondary network, two source nodes that cannot directly communicate attempt to exchange their data with each other. As a result, the relaying technique using partial relay selection is applied to assist the data exchange. Particularly, at the first time slot, the selected secondary relay applies an interference

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13

Huynh, Tan-Phuoc, Pham Ngoc Son, and Miroslav Voznak. "Secrecy Performance of Underlay Cooperative Cognitive Network Using Non-Orthogonal Multiple Access with Opportunistic Relay Selection." Symmetry 11, no. 3 (2019): 385. http://dx.doi.org/10.3390/sym11030385.

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In this paper, an underlay cooperative cognitive network using a non-orthogonal multiple access (UCCN-NOMA) system is investigated, in which the intermediate multiple relays help to decode and forward two signals x 1 and x 2 from a source node to two users D1 and D2, respectively, under wiretapping of an eavesdropper (E). We study the best relay selection strategies by three types of relay selection criteria: the first and second best relay selection is based on the maximum channel gain of the links R i -D 1 , R i -D2, respectively; the third one is to ensure a minimum value of the channel gai

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14

Madole, James W., Mijke Rhemtulla, Andrew D. Grotzinger, Elliot M. Tucker-Drob, and K. Paige Harden. "Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence." Clinical Psychological Science 7, no. 4 (2019): 701–18. http://dx.doi.org/10.1177/2167702619842466.

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Comorbidity is pervasive across psychopathological symptoms, diagnoses, and domains. Network analysis is a method for investigating symptom-level associations that underlie comorbidity, particularly through bridge symptoms connecting diagnostic syndromes. We applied network analyses of comorbidity to data from a population-based sample of adolescents ( N = 849). We implemented a method for assessing nonparametric moderation of psychopathology networks to evaluate differences in network structure across levels of intelligence and emotional control. Symptoms generally clustered by clinical diagn

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15

Fadel, Eleonora, Heinz Boeker, Matti Gaertner, et al. "Differential Alterations in Resting State Functional Connectivity Associated with Depressive Symptoms and Early Life Adversity." Brain Sciences 11, no. 5 (2021): 591. http://dx.doi.org/10.3390/brainsci11050591.

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Depression and early life adversity (ELA) are associated with aberrant resting state functional connectivity (FC) of the default mode (DMN), salience (SN), and central executive networks (CEN). However, the specific and differential associations of depression and ELA with FC of these networks remain unclear. Applying a dimensional approach, here we analyzed associations of FC between major nodes of the DMN, SN, and CEN with severity of depressive symptoms and ELA defined as childhood abuse and neglect in a sample of 83 healthy and depressed subjects. Depressive symptoms were linked to increase

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16

Clusella, Pau, Gustavo Deco, Morten L. Kringelbach, Giulio Ruffini, and Jordi Garcia-Ojalvo. "Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks." PLOS Computational Biology 19, no. 4 (2023): e1010781. http://dx.doi.org/10.1371/journal.pcbi.1010781.

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Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 bra

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17

Timsit, Youri, and Sergeant-Perthuis Grégoire. "Towards the Idea of Molecular Brains." International Journal of Molecular Sciences 22, no. 21 (2021): 11868. http://dx.doi.org/10.3390/ijms222111868.

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How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the e

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18

Sankar, *Anjali, Xilin Shen, Lejla Colic, et al. "PREDICTING DEPRESSED AND ELEVATED MOOD SYMPTOMATOLOGY IN BIPOLAR DISORDER USING BRAIN FUNCTIONAL CONNECTOMES." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i15. https://doi.org/10.1093/ijnp/pyae059.027.

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Abstract Background Depressed and elevated mood symptom severity underlie the suffering associated with bipolar disorder and are among the strongest clinical predictors of functional impairment and disability in the disorder. Furthermore, mood symptoms contribute to the high risk for suicide in the disorder. Elucidation of the brain functional disturbances that contribute to mood symptoms in individuals with bipolar disorder could advance our understanding of the pathophysiology of the disorder and enable the development of more targeted therapeutic approaches. Aims and Objectives The study is

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19

Le, Tuan Anh, and Keivan Navaie. "Downlink Beamforming in Underlay Cognitive Cellular Networks." IEEE Transactions on Communications 62, no. 7 (2014): 2212–23. http://dx.doi.org/10.1109/tcomm.2014.2323051.

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20

Mohamed, Elalem. "Effective Capacity Analysis for Cognitive Networks under 𝐐𝐨𝐒 Satisfaction". Journal of Information Sciences and Computing Technologies 5, № 3 (2016): 498–518. https://doi.org/10.5281/zenodo.3968427.

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Spectrum sensing and dynamic spectrum access (DSA) techniques in cognitive radio networks (CRN) have been extensively investigated since last decade. Recently, satisfaction of quality-of-service (QoS) demands for secondary users (SU) has attracted great attention. The SU can not only discover the transmission opportunities, but also cognitively adapts the dynamic spectrum access strategies to its own QoS requirement and the environment variations. In this paper, we study how the delay QoS requirement affects the strategy on network performance. We first treat the delay-QoS in interference cons

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21

Sultan, Kiran, Ijaz Mansoor Qureshi, Aqdas Naveed Malik, and Muhammad Zubair. "Performance Analysis of Relay Subset Selection for Amplify-and-Forward Cognitive Relay Networks." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/548082.

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Cooperative communication is regarded as a key technology in wireless networks, including cognitive radio networks (CRNs), which increases the diversity order of the signal to combat the unfavorable effects of the fading channels, by allowing distributed terminals to collaborate through sophisticated signal processing. Underlay CRNs have strict interference constraints towards the secondary users (SUs) active in the frequency band of the primary users (PUs), which limits their transmit power and their coverage area. Relay selection offers a potential solution to the challenges faced by underla

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22

Kompella, Sastry, Gam D. Nguyen, Clement Kam, Jeffrey E. Wieselthier, and Anthony Ephremides. "Cooperation in Cognitive Underlay Networks: Stable Throughput Tradeoffs." IEEE/ACM Transactions on Networking 22, no. 6 (2014): 1756–68. http://dx.doi.org/10.1109/tnet.2013.2284788.

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23

Lameiro, Christian, Wolfgang Utschick, and Ignacio Santamaría. "Spatial interference shaping for underlay MIMO cognitive networks." Signal Processing 134 (May 2017): 174–84. http://dx.doi.org/10.1016/j.sigpro.2016.12.012.

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24

Mahmood, Nurul H., Ferkan Yilmaz, Geir E. Oien, and Mohamed-Slim Alouini. "On Hybrid Cooperation in Underlay Cognitive Radio Networks." IEEE Transactions on Wireless Communications 12, no. 9 (2013): 4422–33. http://dx.doi.org/10.1109/twc.2013.081413.121516.

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25

Kusaladharma, Sachitha, and Chintha Tellambura. "Aggregate Interference Analysis for Underlay Cognitive Radio Networks." IEEE Wireless Communications Letters 1, no. 6 (2012): 641–44. http://dx.doi.org/10.1109/wcl.2012.091312.120600.

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26

Touati, Sami, Hatem Boujemaa, and Nazha Abed. "Interference Aware Routing Protocols for Underlay Cognitive Radio Networks." International Journal of Communications 15 (April 23, 2021): 6–9. http://dx.doi.org/10.46300/9107.2021.15.2.

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In this paper, optimal and sub-optimal routing protocols are proposed for cognitive radio networks. We first investigate optimal routing that consists in searching among all paths, the one that minimizes the end-to-end outage while verifying interference constraint to primary receiver. A sub-optimal one-hop routing is proposed where the best relay is selected in each hop. The last routing protocol consists in decomposing the network in many sub-networks composed of K hops. Then the best route is determined in each of these sub-networks so that the K hops-outage probability is minimized. The pr

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27

Claudino, Lucas, and Taufik Abrão. "Linear MMSE Channel Estimation for Underlay Cognitive Radio Networks." Journal of Computer and Communications 10, no. 08 (2022): 1–17. http://dx.doi.org/10.4236/jcc.2022.108001.

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28

RONG, Mei, and Shihua ZHU. "Hybrid Overlay/Underlay Spectrum Sharing in Cognitive Radio Networks." IEICE Transactions on Communications E94-B, no. 9 (2011): 2672–76. http://dx.doi.org/10.1587/transcom.e94.b.2672.

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29

Zheng, Kechen, Xiao-Yang Liu, Xiaoying Liu, and Yihua Zhu. "Hybrid Overlay-Underlay Cognitive Radio Networks With Energy Harvesting." IEEE Transactions on Communications 67, no. 7 (2019): 4669–82. http://dx.doi.org/10.1109/tcomm.2019.2912605.

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30

Wang, Qiu, Hong-Ning Dai, Orestis Georgiou, Zhiguo Shi, and Wei Zhang. "Connectivity of Underlay Cognitive Radio Networks With Directional Antennas." IEEE Transactions on Vehicular Technology 67, no. 8 (2018): 7003–17. http://dx.doi.org/10.1109/tvt.2018.2825379.

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31

Lameiro, Christian, Ignacio Santamaria, Peter J. Schreier, and Wolfgang Utschick. "Maximally Improper Signaling in Underlay MIMO Cognitive Radio Networks." IEEE Transactions on Signal Processing 67, no. 24 (2019): 6241–55. http://dx.doi.org/10.1109/tsp.2019.2953665.

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32

Nguyen, Nam-Phong, Tu Lam Thanh, Trung Q. Duong, and A. Nallanathan. "Secure communications in cognitive underlay networks over Nakagami-mchannel." Physical Communication 25 (December 2017): 610–18. http://dx.doi.org/10.1016/j.phycom.2016.05.003.

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33

Le, Long, and Ekram Hossain. "Resource allocation for spectrum underlay in cognitive radio networks." IEEE Transactions on Wireless Communications 7, no. 12 (2008): 5306–15. http://dx.doi.org/10.1109/t-wc.2008.070890.

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34

El Tanab, Manal, and Walaa Hamouda. "Resource Allocation for Underlay Cognitive Radio Networks: A Survey." IEEE Communications Surveys & Tutorials 19, no. 2 (2017): 1249–76. http://dx.doi.org/10.1109/comst.2016.2631079.

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35

El Tanab, Manal, Walaa Hamouda, and Yasmine Fahmy. "Distributed opportunistic scheduling for MIMO underlay cognitive radio networks." Wireless Communications and Mobile Computing 16, no. 15 (2016): 2212–24. http://dx.doi.org/10.1002/wcm.2677.

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36

Sawant, Rupali, and Shikha Nema. "Outage Analysis in Underlay OFDMA Based Cooperative Cognitive Radio Networks." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (2020): 625–33. http://dx.doi.org/10.2174/2210327910666191218125527.

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Background: Efficient resource allocation in Cooperative Cognitive Radio Network (CCRN) is necessary in order to meet the challenges in future wireless networks. With proper resource allocation, the Quality of Service (QoS) comprising of outage probability and data rate are evaluated in this paper and sufficiently improved with proper subcarrier allocation. Objective: Another important parameter is Signal to Interference Ratio (SIR) which should be above a threshold called minimum protection ratio to maintain the required QoS. Results: The network considered is Orthogonal Frequency Division Mu

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37

SON, Pham Ngoc, and Hyung Yun KONG. "Exact Outage Analysis of Energy Harvesting Underlay Cooperative Cognitive Networks." IEICE Transactions on Communications E98.B, no. 4 (2015): 661–72. http://dx.doi.org/10.1587/transcom.e98.b.661.

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38

Chen, Yunli, Zhengguang Zheng, Yibin Hou, and Yong Li. "Energy Efficient Design for OFDM-Based Underlay Cognitive Radio Networks." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/431878.

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Conventional designs on OFDM-based underlay cognitive radio (CR) networks mainly focus on interference avoidance and spectral efficiency (SE) improvement. As green radio becomes increasingly important, this paper investigates energy efficient power allocation. Our aim is to maximize energy efficiency (EE), subject to the constraints on the total transmit power, the peak interference power, and the minimum data rate requirement. We first analyze the relationship between SE and EE and solve this optimization problem with the help of bisection search technique. However, the accuracy of the power

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39

Huang, Haiyan, Norman C. Beaulieu, Zan Li, and Jiangbo Si. "On the Performance of Underlay Cognitive Multisource Multirelay Cooperative Networks." IEEE Communications Letters 19, no. 4 (2015): 605–8. http://dx.doi.org/10.1109/lcomm.2015.2398421.

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Luo, Liping, Ping Zhang, Guangchi Zhang, and Jiayin Qin. "Outage Performance for Cognitive Relay Networks with Underlay Spectrum Sharing." IEEE Communications Letters 15, no. 7 (2011): 710–12. http://dx.doi.org/10.1109/lcomm.2011.051011.110426.

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Ma, Bojiang, Man Hon Cheung, Vincent W. S. Wong, and Jianwei Huang. "Hybrid Overlay/Underlay Cognitive Femtocell Networks: A Game Theoretic Approach." IEEE Transactions on Wireless Communications 14, no. 6 (2015): 3259–70. http://dx.doi.org/10.1109/twc.2015.2403363.

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42

Zheng, Meng, Chi Xu, Wei Liang, and Haibin Yu. "Harvesting‐throughput tradeoff for RF‐powered underlay cognitive radio networks." Electronics Letters 52, no. 10 (2016): 881–83. http://dx.doi.org/10.1049/el.2015.4418.

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Son, Pham Ngoc, Tran Trung Duy, and Khuong Ho-Van. "SIC-Coding Schemes for Underlay Two-Way Relaying Cognitive Networks." Wireless Communications and Mobile Computing 2020 (August 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/8860551.

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In this paper, we propose an underlay two-way relaying scheme with the successive interference cancellation (SIC) solution in which two secondary sources transmit simultaneously their data to each other through secondary relays. The proposed scheme is operated in only two time slots and under an interference constraint of a primary receiver, denoted as the UTW-2TS scheme. In the UTW-2TS scheme, the secondary relays employ the SIC operation to decode successively the data from received broadcast signals and then encode these data by two techniques: digital network coding (DNC) enforced by XOR o

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44

Arzykulov, Sultangali, Galymzhan Nauryzbayev, Theodoros A. Tsiftsis, and Behrouz Maham. "Performance Analysis of Underlay Cognitive Radio Nonorthogonal Multiple Access Networks." IEEE Transactions on Vehicular Technology 68, no. 9 (2019): 9318–22. http://dx.doi.org/10.1109/tvt.2019.2930553.

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45

Gupta, Nikhil, and Aditya K. Jagannatham. "Transceiver Optimization for Unicast/Multicast MIMO Cognitive Overlay/Underlay Networks." IEEE Signal Processing Letters 22, no. 10 (2015): 1556–60. http://dx.doi.org/10.1109/lsp.2015.2413940.

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46

Lee, Sunyoung, Trung Q. Duong, Daniel Benevides daCosta, Dac-Binh Ha, and Sang Quang Nguyen. "Underlay cognitive radio networks with cooperative non-orthogonal multiple access." IET Communications 12, no. 3 (2018): 359–66. http://dx.doi.org/10.1049/iet-com.2017.0559.

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47

Chu, Thi My Chinh, Hoc Phan, and Hans-Jurgen Zepernick. "Hybrid Interweave-Underlay Spectrum Access for Cognitive Cooperative Radio Networks." IEEE Transactions on Communications 62, no. 7 (2014): 2183–97. http://dx.doi.org/10.1109/tcomm.2014.2325041.

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48

Tran, Hung, Georges Kaddoum, and François Gagnon. "Power allocation for cognitive underlay networks with spectrum band selection." Physical Communication 21 (December 2016): 41–48. http://dx.doi.org/10.1016/j.phycom.2016.09.004.

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49

Ho-Van, Khuong, and Thiem Do-Dac. "Security Performance of Underlay Cognitive Relaying Networks with Energy Harvesting." Wireless Personal Communications 110, no. 2 (2019): 829–46. http://dx.doi.org/10.1007/s11277-019-06758-4.

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50

Tang, Ningkai, Shiwen Mao, and Sastry Kompella. "On power control in full duplex underlay cognitive radio networks." Ad Hoc Networks 37 (February 2016): 183–94. http://dx.doi.org/10.1016/j.adhoc.2015.08.018.

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