Power management for spectrum sharing in cognitive radio communication system: a comprehensive survey

References

• Federal Communications Commission. Notice of proposed rulemaking and order: Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. ET Docket No. 03-108, Feb 2002.
   Google Scholar
• Mitola J, Maguire GQJ. Cognitive radio: making software radios more personal. IEEE Pers Commun Mag. 1999 Aug;6(4):13–18.
   Google Scholar
• Xin C, Paul P, Song M, et al. On dynamic spectrum allocation in geo-location spectrum sharing systems. IEEE Trans Mob Comput. 2019 Apr;18(4):923–933.
   Web of Science ®Google Scholar
• Jeon J, Ford RD, Ratnam VV, et al. Coordinated dynamic spectrum sharing for 5G and beyond cellular networks. IEEE Access. 2019 Aug;7:111592–111604.
   Google Scholar
• Akyildiz IF, Lee W-Y, Vuran MC, et al. Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Networks. 2006 Sep;50(13):2127–2159.
   Web of Science ®Google Scholar
• Thakur P, Singh G, Satashia SN. Spectrum sharing in cognitive radio communication system using power constraints: a technical review. Perspect Sci. 2016 Sep;8:651–653.
   Google Scholar
• Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials. 2009;11(1):116–130.
   Web of Science ®Google Scholar
• Akyildiz IF, Lee WY, Varun MC, et al. A survey on spectrum management in cognitive radio networks. IEEE Commun Mag. 2008 Apr;46(4):40–48.
   Web of Science ®Google Scholar
• Akyildiz IF, Lo BF, Balakrishnan R. Cooperative spectrum sensing in cognitive radio networks: a survey. Phys Commun. 2011 Mar;4(1):40–62.
   Web of Science ®Google Scholar
• Cristian I, Moh S, Chung I, et al. Spectrum mobility in cognitive radio networks. IEEE Commun Mag. 2012 Jun;50(6):114–121.
   Web of Science ®Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques. Phys Commun. 2017 Sep;24:1–8.
   Google Scholar
• Kumar K, Prakash A, Tripathi R. Spectrum handoff in cognitive radio networks: a classification and comprehensive survey. J Network Comput Appl. 2016 Feb;61:161–188.
   Web of Science ®Google Scholar
• Dudley SM, Headley WC, Lichtman M, et al. Practical issues for spectrum management with cognitive radios. IEEE Proc. 2014 Mar;102(3):242–264.
   Web of Science ®Google Scholar
• President’s Council of Advisors on Science and Technology, Report to the President realizing the full potential of government-held spectrum to spur economic growth; Jul 2012.
   Google Scholar
• European Parliament, Decision No 243/1012/EU of the European Parliament and the Council of 14 March 2012 establishing a multiannual radio spectrum policy programme, Strasbourg, France, 243/2012/EU; 2012.
   Google Scholar
• Buddhikot MM. Understanding dynamic spectrum access: Models, taxonomy and challenges. Proceedings of the 2nd IEEE International Symposium on New Frontiers of Dynamic Spectrum Access Networks; 2007. p. 649–663.
   Google Scholar
• U.S. Federal Communications Commission, Enabling innovative small cell use in 3.5 GHz band NPRM & order; 2012.
   Google Scholar
• Wang B, Liu KJR. Advances in cognitive radio networks: a survey. IEEE J Sel Areas Commun. 2011 Feb;11(1):5–23.
   Google Scholar
• Xu Y, Zhao X, Liang Y-C. Robust power control and beamforming in cognitive in radio networks: a survey. IEEE Commun Surv Tutorials. 2015;17(4):1834–1857.
   Web of Science ®Google Scholar
• Naeem M, Anpalagan A, Jaseemuddin M, et al. Resource allocation techniques in cooperative cognitive radio networks. IEEE Commun Survey Tutorials. 2014;16(2):729–744.
   Web of Science ®Google Scholar
• Ahmed E, Gani A, Abolfazli S, et al. Channel assignment algorithms in cognitive radio networks: taxonomy, open issues, and challenges. IEEE Commun Surv Tutorials. 2016;18(1):795–823.
   Web of Science ®Google Scholar
• Tanab ME, Hamouda W. Resource allocation for underlay cognitive radio networks: a survey. IEEE Commun Survey Tutorials. 2017;19(2):1249–1276.
   Web of Science ®Google Scholar
• Hu F, Chen B, Zhu K. Full spectrum sharing in cognitive radio networks toward 5G: a survey. IEEE Access. 2018 Feb;6:15754–15776.
   Google Scholar
• Kour H, Jha RK, Jainb S. A comprehensive survey on spectrum sharing: architecture, energy efficiency and security issues. J Networking Comput Appl. 2018 Feb;103(1):29–57.
   Google Scholar
• Sharma SK, Bogale TE, Le LB, et al. Dynamic spectrum sharing in 5G wireless networks with full-duplex technology: recent advances and research challenges. IEEE Commun Tutorials. 2018;20(1):674–707.
   Web of Science ®Google Scholar
• Hossain E, Niyato D, Han Z. Dynamic spectrum access and management in cognitive radio networks. New York: Cambridge University Press; 2009.
   Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Spectrum monitoring in heterogeneous cognitive radio network: How to cooperate? IET Commun. 2018 Oct;12(17):2110–2118.
   Web of Science ®Google Scholar
• Thakur P, Singh G. Energy and spectral efficient SMC-MAC protocol in distributed cognitive radio networks. IET Commun. 2019 Jun:2110–2118. doi:10.1049/iet-com.2019.0212.
   Web of Science ®Google Scholar
• Zheng H, Cao L. Device-centric spectrum management. Proceedings of the IEEE Symposium on New Frontiers of Dynamic Spectrum Access Networks (DySPAN), Baltimore (MD); Nov 2005. p. 56–65.
   Google Scholar
• Sharma SK, Bhogle TE, Le LB, et al. Cognitive radio techniques under practical imperfections: a survey. IEEE Commun Surveys Tutorials. 2015;17(4):1858–1884.
   Web of Science ®Google Scholar
• Wyglinski AM, Nekovee M, Hou T. Cognitive radio communications and networks. Boston (MA): Academic Press; 2010.
   Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure. Digital Commun Netw. 2018 Nov;4(4):287–295.
   Web of Science ®Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques. Wireless Netw. 2018 Aug;24(6):2005–2015.
   Web of Science ®Google Scholar
• Ban TW, Choi W, Jung BC, et al. Multi-user diversity in a spectrum sharing system. IEEE Trans Wireless Commun. 2009 Jan;8(1):102–106.
   Web of Science ®Google Scholar
• Establishment of an interference temperature metric to quantify and manage interference and to expand available unlicensed operation in certain fixed, mobile and satellite frequency bands. Federal Communication Commission (FCC), Washington (DC); ET Docket 0.-289; 2003.
   Google Scholar
• Goldsmith A, Jafar SA, Maric I, et al. Breaking spectrum gridlock with cognitive radios: a n information theoretic perspective. Proc IEEE. 2009 May;97(5):894–914.
   Web of Science ®Google Scholar
• Costa M. Writing on dirty paper (corresp.). IEEE Trans Inf Theory. 1983 May;29(3):439–441.
   Web of Science ®Google Scholar
• Gel’fand SI, Pinsker MS. Problems of control and information theory. Coding Channel Random Parameters. 1980;9(1):19–31.
   Google Scholar
• Sharma SK, Chatzinotas S, Ottersten B. A hybrid cognitive transceiver architecture: sensing throughput tradeoff. Proc. Cognitive Radio Wireless Networks and Communications (CROWNCOM), Oulu, Finland; Jun 2014. p. 143–149.
   Google Scholar
• Jiang X, Wang KK, Zang Y, et al. On hybrid overlay-underlay dynamic spectrum access: double-threshold energy detection and Markov model. IEEE Trans Veh Technol. 2013 Apr;62(8):4078–4083.
   Web of Science ®Google Scholar
• Chu TMC, Phan H, Japernick H-J. Hybrid interweave-underlay spectrum access for cognitive radio networks. IEEE Trans Commun. 2014 Jul;62(7):2183–2197.
   Web of Science ®Google Scholar
• Serrano RB, Thombaben R, Jorswieck E, et al. Comparison of underlay and overlay spectrum sharing strategies in MISO cognitive channels. Proceedings of the Cognitive Radio Oriented Wireless Networks And Communications (CROWNCOM), Stockholm; Jun 2012. p. 224–229.
   Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Frame structures for hybrid spectrum accessing strategy in cognitive radio communication system. Proceedings of the IEEE International Conference on Contemporary Computing (IC-3), India; Aug 2016. p. 1–6.
   Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Advanced frame structures for hybrid spectrum accessing strategy in cognitive radio communication system. IEEE Commun Lett. 2017;21(1):410–413.
   Google Scholar
• Chengquan A, Yang L. A matching game algorithm for spectrum allocation based on POMDP model. Proceedings of the IEEE International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Wuhan, China; Sep 2011. p. 1–3.
   Google Scholar
• Niyato D, Hossain E. A game-theoretic approach to competitive spectrum sharing in cognitive radio Networks. Proceedings of the Wireless Communications and Networking Conference (WCNC), Kowloon, Hong-Kong; Mar 2007. p. 16–20.
   Google Scholar
• Sharma S, Teneketzis D. A game theoretic approach to decentralized optimal power allocation for cellular networks. Telecommun Syst. 2011 Jun;47:65–80.
   Web of Science ®Google Scholar
• Li H, Gou Q, Tang T, et al. Distributed resource allocation for cognitive radio network with imperfect spectrum sensing. Proceedings of the 78th Vehicular Technology (VTC fall), Las Vegas (NV); Sep 2013. p. 1–6.
   Google Scholar
• Ektin R, Parekh A, Tse D. Spectrum sharing for unlicensed bands. IEEE J Sel Areas Commun. 2007 Apr;25(3):517–528.
   Web of Science ®Google Scholar
• Zhou P, Yuan W, Liu W, et al. Joint power and rate control in cognitive radio networks: A game-theoretical approach. Proceedings of the IEEE International Conference on Communications, Beijing; May 2008. p. 3926–3301.
   Google Scholar
• Attar A, Mohammad Reza N, Hamid AA. Cognitive radio game for secondary spectrum access problem. IEEE Trans Wireless Commun. 2009 Apr;8(4):2121–2131.
   Web of Science ®Google Scholar
• Ni Q, Zarakovitis CC. Nash bargaining game theoretic scheduling for joint channel and power allocation in cognitive radio systems. IEEE J Sel Areas Commun. 2012 Jan;30(1):70–81.
   Web of Science ®Google Scholar
• Liu Y, Dong L. Spectrum sharing in MIMO cognitive radio networks based on cooperative game theory. IEEE Trans Wireless Commun. 2014 Sep;13(9):4807–4820.
   Web of Science ®Google Scholar
• Zhang H, Jiang C, Beaulieu N, et al. Resource allocation for cognitive small cell networks: a cooperative bargaining game theoretic approach. IEEE Trans Wireless Commun. 2015 Jun;14(6):3481–3493.
   Web of Science ®Google Scholar
• Lin F, Zhou X, Lu X, et al. Novel pre-pushing scheme for peer-assisted streaming network based on multi-leader multi-follower stackelberg model. Wireless Pers Commun. 2015 Jan;80(1):289–301.
   Web of Science ®Google Scholar
• Shafigh AS, Mertikopoulos P, Glisic S, et al. Semi-cognitive radio networks: a novel dynamic spectrum sharing mechanism. IEEE Trans Cognitive Commun Networking. 2017 Mar;3(1):97–111.
   Web of Science ®Google Scholar
• Roy A, Midya S, Majumder K, et al. Optimized secondary user selection for quality of service enhancement of two-tier multi-user cognitive radio network: a game theoretic approach. Comput Networks. 2017 Aug;123(4):1–18.
   Google Scholar
• Ni Q, Zhu R, Wu Z, et al. Spectrum allocation based on game theory in cognitive radio networks. J Networks. 2013 Mar;8(3):712–722.
   Google Scholar
• Li D, Xu Y, Wang X, et al. Coalitional game theoretic approach for secondary spectrum access in cooperative cognitive radio networks. IEEE Trans Wireless Commun. 2011 Mar;10(3):844–856.
   Web of Science ®Google Scholar
• Wang H, Gao L, Gan X, et al. Cooperative spectrum sharing in cognitive radio networks: a game-theoretic approach. Proceedings of the International Conference on Communications (ICC), Cape Town; May 2010. p. 1–5.
   Google Scholar
• Omidvar N, Khalaj BH. A game theoretic approach for power allocation in the downlink of cognitive radio networks. Proceedings of the IEEE Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Kyoto; Jun 2012. p. 158-162.
   Google Scholar
• Huang J, Berry R, Honig ML. Auction-based spectrum sharing. Mob Networks Appl. 2006 Jun;11(3):405–418.
   Web of Science ®Google Scholar
• Wang B, Han Z, Liu KJ. Distributed relay selection and power control for multiuser cooperative communication networks using buyer/seller game. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Barcelona; May 2007. p. 544–552.
   Google Scholar
• Zou J, Xiong H, Wang D, et al. Optimal power allocation for hybrid overlay/underlay spectrum sharing in multiband cognitive radio networks. IEEE Trans Veh Technol. 2013 May;62(4):1827–1837.
   Web of Science ®Google Scholar
• Yu H, Gao L, Li Z, et al. Pricing for uplink power control in cognitive radio networks. IEEE Trans Veh Technol. 2010 Jan;59(4):1769–1778.
   Web of Science ®Google Scholar
• Kang X, Zhang R, Motani M. Price-based resource allocation for spectrum-sharing femtocell networks: a stackelberg game approach. IEEE J Sel Areas Commun. 2012;30(3):538–549.
   Web of Science ®Google Scholar
• Wang ZQ, Jiang LG, He C. A novel price-based power control algorithm in cognitive radio networks. IEEE Commun Lett. 2013 Jan;17(1):43–46.
   Web of Science ®Google Scholar
• Ahmed F, Tirkkonen O, Dowhuszko AA, et al. Distributed power allocation in cognitive radio networks under network power constraint,” Proceedings of the International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Oulu, Finland; Jun. 2014. p. 1–6.
   Google Scholar
• Oro SD, Mertikopoulos P, Moustakas AL, et al. Interference-based pricing for opportunistic multicarrier cognitive radio systems. IEEE Trans Wireless Commun. 2015 Dec;14(12):6536–6539.
   Web of Science ®Google Scholar
• Darsena D, Gelli G, Verde F. An opportunistic spectrum access scheme for multicarrier cognitive sensor networks. IEEE Sessors J. 2017 Apr;17(8):2596–2606.
   Web of Science ®Google Scholar
• Liu Z, Hao L, Xia Y, et al. Price bargaining based on the Stackelberg game in two-tier orthogonal frequency division multiple access femtocell networks. IET Communication. 2015;9(1):133–145.
   Web of Science ®Google Scholar
• Zeng B, Zhang C, Hu P, et al. Spectrum sharing based on a Bertrand game in cognitive radio sensor networks. Sensors. 2017 Jan;17(1):1–27.
   Web of Science ®Google Scholar
• Krishna V. Auction theory. London: Academic Press; 2002.
   Google Scholar
• Dramitinos M, Stamoulis G, Courcoubetis C. Auction-based resource reservation in 2.5/3G networks. Mob Networks Appl. 2004 Dec;9(6):557–566.
   Web of Science ®Google Scholar
• Huang J, Han Z, Chiang M, et al. Auction-based resource allocation for cooperative communications. IEEE J Sel Areas Commun. 2008 Sep;26(7):1226–1237.
   Web of Science ®Google Scholar
• Guo J, Gu S, Wang X, et al. Subchannel and power allocation in OFDMA-based cognitive radio networks. Proceedings of the IEEE International Conference on Communications (ICC), Cape Town; May 2010. p. 1–5.
   Google Scholar
• Mao J, Gao J, Liu Y, et al. Power allocation over fading cognitive MIMO channels: an ergodic capacity perspective. IEEE Trans Veh Technol. 2012 Mar;61(3):1162–1173.
   Web of Science ®Google Scholar
• Kang X, Zang R, Liang Y-C, et al. Optimal power allocation strategies for fading cognitive radio channel with primary user outage constraint. IEEE J Sel Areas Commun. 2011 Feb;29(2):374–383.
   Web of Science ®Google Scholar
• Naeem M, Iiianko K, Karmokar A, et al. Optimal power allocation for green radio: fractional programming approach. IET Commun. 2013 Aug;7(12):1279–1286.
   Web of Science ®Google Scholar
• Zheng L, Tan CW. Maximizing sum rates in cognitive radio networks: convex relaxation and global optimization algorithms. IEEE J Sel Areas Commun. 2014 Mar;32(3):667–680.
   Web of Science ®Google Scholar
• Nguyen HV, Nguyen V-D, Kim HM, et al. A convex optimization for sum rate maximization in a MIMO cognitive radio network. Proceedings of the IEEE Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria; Jul. 2016. p. 495–497.
   Google Scholar
• Ahmad A, Khan MTR. Resource allocation for SC-FDMA based cognitive radio systems. Int J Commun Syst. 2017 Mar;30(5):1–13.
   Web of Science ®Google Scholar
• Moghadam MS, Talebi S. Jointly optimal rate control and total transmission power for cooperative cognitive radio system. IET Commun. 2017 Aug;11(11):1679–1688.
   Web of Science ®Google Scholar
• Shiung D, Chen K-C. On the optimal power allocation of a cognitive radio network. Proceedings of the 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo; 2009. p. 1241–1245.
   Google Scholar
• Zhang Z, Wu Q, Wang J. Energy efficient power allocation strategy in cognitive relay networks. Radio Eng. 2012 Sep;21(3):809–814.
   Google Scholar
• Pao W-C, Chen Y-F. Adaptive gradient based methods for adaptive power allocation in OFDM- based cognitive radio networks. IEEE Trans Veh Technol. 2014 Feb;63(2):836–848.
   Web of Science ®Google Scholar
• Sturm J. Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones. Optim Methods Softw. 1999 Sep;11–12:625–653.
   Web of Science ®Google Scholar
• Toh KC, Todd M, Tutuncu RH. SDPT3 – a Matlab software package for semidefinite programming, version 1.3. Optim Methods Softw. 1999 Jan;11:545–581.
   Web of Science ®Google Scholar
• Yu LS, Jun XW, Yan G, et al. Resource allocation for multiple description coding multicast in OFDM-based cognitive radio networks. The J Chin Univ Posts Telecommun. 2012 Oct;19(5):51–57.
   Google Scholar
• Qilin Q, Minturn A, Yang Y. An efficient water filling algorithm for power allocation in OFDM based cognitive radio systems,” Proceedings of the IEEE International Conference on Systems and Informatics, Yantai; May 2012. p. 2069–2073.
   Google Scholar
• Jun P, Zhen H, Zhengjfa Z, et al. A worst case robust distributed power allocation in OFDM based cognitive radio network. Proceedings of the 32nd Chinese Control Conference (CCC), Xian; Jul 2013. p. 6422–6427.
   Google Scholar
• Forouzan N, Ghorashi SA. New algorithm for joint sub-channel and power allocation in multicell OFDMA – based cognitive radio networks. IET Commun. 2014 Mar;8(4):508–515.
   Web of Science ®Google Scholar
• Dulek B, Gezici S, Sawai R, et al. Power adaptation for cognitive radio systems under average SINR loss constraint in the absence of path loss information. Wireless Pers Commun. 2014 Jul;77(1):151–172.
   Web of Science ®Google Scholar
• Hosseini E, Falahati A. Improving water-filling algorithm to power control cognitive radio system based upon traffic parameter and QoS. Wireless Pers Commun. 2012 Aug;70(4):1747–1759.
   Web of Science ®Google Scholar
• Krishnan KV, Sajith RM, Khara S. Dynamic resource allocation in OFDM based cognitive radio system considering primary user QoS and secondary user proportional constraints. J Commun Technol Electron. 2015 Nov;60(11):1269–1275.
   Web of Science ®Google Scholar
• Gu J, Lee T-J, Chung MY. Water-filling technique-based proportionally fair scheduling algorithm for SC-FDMA. Trans Emerg Telecommun Technol. 2016 Dec;27(12):1664–1671.
   Web of Science ®Google Scholar
• Chitra S, Kumaratharan N. Multimedia transmission in MC-CDMA using adaptive subcarrier power allocation and CFO compensation. Int J Electron. 2017 Jul. doi:10.1080/00207217.2017.1357201.
   Web of Science ®Google Scholar
• Roumeliotis AJ, Vassaki S, Panagopoulos AD. Joint power and time allocation scheme with QoS constraints in overlay multi-user cognitive radio networks. Wireless Pers Commun. 2018 Jan;98(1):337–362.
   Web of Science ®Google Scholar
• Li F, Lam K-Y, Wang L. Power allocation in cognitive radio networks over Rayleigh-fading channels with hybrid intelligent algorithms. Wireless Networks. 2018 Oct;24(7):2397–2407.
   Web of Science ®Google Scholar
• Zhou X, Wu B, Ho P-H, et al. An efficient power allocation algorithm for OFDM based underlay cognitive radio network. Proceedings of the IEEE Global Telecommunications Conference, Houston (TX); 2011. p.1–5.
   Google Scholar
• Rezai F, Tadaion A. Interference alignment in cognitive radio networks. IET Commun. 2014 Mar;8(10):1769–1777.
   Web of Science ®Google Scholar
• Le Nir V, Scheers B. Implementation of an adaptive OFDMA PHY/MAC on USRP platforms for cognitive tactical radio network. Proceedings of the IEEE Military Communications and Information Systems Conference, Gdansk, Poland; Oct 2012. p.1–7.
   Google Scholar
• Datla D, Wyglinski AM, Mindon GJ. A spectrum surveying framework for dynamic spectrum access network. IEEE Trans Veh Technol. 2009 Oct;58(8):4158–4168.
   Web of Science ®Google Scholar
• Elalem M, Zhao L. Effective capacity optimization for cognitive radio network based on underlay scheme in gamma fading channel. Proceedings of the International Conference on Computing Networking and Communication, San Diego (CA); Jan 2013. p. 714–718.
   Google Scholar
• Akin S, Fidler M. On the transmission rate strategies in cognitive radios. IEEE Trans Wireless Commun. 2016 Mar;15(3):2335–2350.
   Web of Science ®Google Scholar
• Gastpar M. On capacity under received-signal constraints. Proceedings of the Allerton Conference Communications, Control, Computing, Monticello (IL); Oct 2004. p. 1322–1331.
   Google Scholar
• Ghasemi A, Sousa ES. Capacity of fading channels under spectrum sharing constraints. Proceedings of the IEEE International Conference on Communications (ICC’06), Istanbul; Jun. 2006. p. 4373–4378.
   Google Scholar
• Kang X, Liang Y-C, Nallanathan A, et al. Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Trans Wireless Commun. 2009 Feb;8(2):940–950.
   Web of Science ®Google Scholar
• Farraj AK, Ekin S. Performance of cognitive radios in dynamic fading channels under primary outage constraint. Wireless Pers Commun. 2013 May;73(8):637–649.
   Google Scholar
• Vassaki S, Poulakis MI, Panagopoulous AD, et al. Qos driven power allocation under peak and average interference power constraints in cognitive radio networks. Wireless Pers Commun. 2014 Apr;78:449–474.
   Web of Science ®Google Scholar
• Zhao C, Kwak K. Comprehensive capacity ensured distributed binary power allocation in dense cognitive networks. J Network Syst Manage. 2010 Mar;18(1):24–42.
   Web of Science ®Google Scholar
• Chen Z, Gao F, Zhang Z, et al. Multiple-level power allocation strategy for secondary users in cognitive radio networks. EURASIP J Adv Signal Process. 2014 Dec;2014(51):1–7.
   Google Scholar
• Wu P, Schober R, Bhargava VK. Optimal power allocation for wideband cognitive radio networks employing SC-FDMA. IEEE Commun Lett. 2013 Apr;17(4):669–672.
   Web of Science ®Google Scholar
• Musavian L, Aissa S. Fundamental capacity limits of cognitive radio in fading environments with imperfect channel state information. IEEE Trans Commun. 2009 Nov;57(11):3472–3480.
   Web of Science ®Google Scholar
• Suraweera HA, Smith PJ, Shafi M. Capacity limits and performance analysis of cognitive radio with imperfect channel knowledge. IEEE Trans Veh Technol. 2010 May;59(4):1811–1822.
   Web of Science ®Google Scholar
• Sun K, Jung BC, Chong S, et al. Power allocation policies with full and partial inter- system channel state information for cognitive radio networks. Wireless Networks. 2012 Jun;19:99–113.
   Web of Science ®Google Scholar
• Rezki Z, Alouini M-S. Ergodic capacity of cognitive radio under imperfect channel-state information. IEEE Trans Veh Technol. 2012 Jun;61(5):2108–2119.
   Web of Science ®Google Scholar
• Kundu S, Prasad B, Roy SD. Spectrum sharing networks with imperfect channel state information. Proceedings of the 3rd International Conference on Computer, Communication, Control and Information Technology, Hooghly; Feb. 2015. p. 1–6.
   Google Scholar
• Akin M, Gursoy MC. Performance analysis of cognitive radio systems with imperfect channel sensing and estimation. IEEE Trans Commun. 2015 May;63(5):1554–1566.
   Web of Science ®Google Scholar
• Zhang X, Xing J, Yan Z, et al. Outage performance study of cognitive relay networks with imperfect channel knowledge. IEEE Commun Lett. 2013 Jan;17(1):27–30.
   Web of Science ®Google Scholar
• Bala I, Bhamrah MS, Singh G. Capacity in fading environment based on soft sensing information under spectrum sharing constraints. Wireless Networks. 2017 Feb;23(2):519–531.
   Web of Science ®Google Scholar
• Chang Z, Zhang Q, Guo X, et al. Energy-efficient resource allocation for OFDMA two-way relay networks with imperfect CSI. EURASIP J Wireless Commun Networking. 2015;2015(225):1–11.
   Google Scholar
• Shen Y, Kwak KS. Robust power control for cognitive radio networks with proportional rate fairness. ICT Express. 2015 Jun;1(1):22–25.
   Web of Science ®Google Scholar
• Wang H, Zhou G. Power allocation based on data classification in wireless sensor networks. Sensors. 2017 May;17(5):1–13.
   Web of Science ®Google Scholar
• Mokari N, Parsaeefard S, Azmi P, et al. Robust ergodic uplink resource allocation in underlay OFDMA cognitive radio networks. IEEE Trans Mobile Comput. 2016 Feb;15(2):419–431.
   Web of Science ®Google Scholar
• Shi S, An K, Li G, et al. Optimal power control in cognitive satellite terrestrial networks with imperfect channel state information. IEEE Wireless Commun Lett. 2017 Sep;7(1):34–37.
   Web of Science ®Google Scholar
• Goldsmith AJ, Varaiya PP. Capacity of fading channels with channel side information. IEEE Trans Inf Theory. 1997 Nov;43(6):1986–1992.
   Web of Science ®Google Scholar
• Haykin S. Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun. 2005 Feb;23(2):201–220.
   Web of Science ®Google Scholar
• Musavian L, Aissa S. Capacity of spectrum-sharing channels with minimum rate requirements. IEEE Proceedings of the International Conference on Communications (ICC), Beijing (ICC), Beijing; May 2008. p. 4639–4643.
   Google Scholar
• Smith P, Dmochowski P, Suraweera H, et al. The effects of limited channel knowledge on cognitive radio system capacity. IEEE Trans Veh Technol. 2013 Feb;62(2):927–933.
   Web of Science ®Google Scholar
• Jaafar W, Ohtsuki T, Ajib W, et al. Impact of CSI on the performance cognitive relay networks with partial relay selection. IEEE Trans Veh Technol. 2016 Feb;65(2):673–684.
   Web of Science ®Google Scholar
• Bansal G, Hossain MJ, Bhargava VK. Adaptive power loading for OFDM-based cognitive radio systems with statistical interference constraint. IEEE Trans Wireless Commun. 2011 Sep;10(9):2786–2791.
   Web of Science ®Google Scholar
• Kaligineedi P, Bansal G, Bhargava VK. Power loading algorithms for OFDM-based cognitive radio systems with imperfect sensing. IEEE Trans Wireless Commun. 2012 Dec;11(12):4225–4230.
   Web of Science ®Google Scholar
• Safavi SH, Ardebilipour M, Salari S. Relay beam-forming in cognitive two-way networks with imperfect channel state information. IEEE Wireless Commun Lett. 2012 Aug;1(4):344–347.
   Web of Science ®Google Scholar
• Chen J, Si J, Li Z, et al. On the performance of spectrum sharing cognitive relay networks with imperfect CSI. IEEE Commun Lett. 2012 Jul;16(7):1002–1005.
   Web of Science ®Google Scholar
• Sutton PD, Lotze J, Lahlou H, et al. Multi-platform demonstrations using the Iris architecture for cognitive radio network testbeds. Proceedings of the 5th International Conference on Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), Cannes, France; Jun. 2010. p. 1–5.
   Google Scholar
• Sutton PD, Lotze J, Lahlou H, et al. Iris: an architecture for cognitive radio networking testbeds. IEEE Commun Mag. 2010 Sep;48(9):114–122.
   Web of Science ®Google Scholar
• Lotze J, Fahmy SA, Noguera J, et al. A model-based approach to cognitive radio design. IEEE J Sel Areas Commun. 2011 Feb;29(2):455–468.
   Web of Science ®Google Scholar
• Chen Z, Guo N, Qiu RC. Building a cognitive radio network testbed. Proceedings of the IEEE SoutheastCon, Nashville (TN); Mar. 2011. p. 91–96.
   Google Scholar
• Sanchez A, Moerman I, Bouckaert S, et al. Testbed federation: An approach for experimentation-driven research in cognitive radios and cognitive networking. Proceedings of the IEEE Future Network & Mobile Summit (FutureNetw), Warsaw, Poland; Jun. 2011, pp. 1–9.
   Google Scholar
• He Z, Liu T, Tang Y, et al. The UESTC reconfigurable cognitive radio testbed. International ICST Conference on Communications and Networking (CHINACOM), Harbin, China; Aug. 2011. p. 939–943.
   Google Scholar
• Budihal R, Desikan B, Jamadagni HS. Co-operative spectrum sensing: Implementation and benchmarking on ANRC cognitive radio testbed. Proceedings of the IEEE International Conference on Signal Processing and communications, Bangalore, India; Aug. 2012. p. 1–5.
   Google Scholar
• Macaluso I, Ozgul B, Forde TK, et al. Spectrum and energy efficient block edge mask-compliant waveforms for dynamic environments. IEEE J Sel Areas Commun. 2014 Feb;32(2):307–321.
   Web of Science ®Google Scholar
• Cardoso LS, Massouri A, Guillon B, et al. CorteXlab: a facility for testing cognitive radio networks in a reproducible environment. Proceedings of the IEEE 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), Oulu, Finland; Jun 2014. p. 503–507.
   Google Scholar
• Mishra SM, Cabric D, Chang C, et al. A real time cognitive radio testbed for physical and link layer experiments. Proc. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore (MD); Nov 2015. p. 562–567.
   Google Scholar
• Rawat DB. ROAR: An architecture for real-time opportunistic spectrum access in cloud-assisted cognitive radio networks. Proceedings of the IEEE 13th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas (NV); Jan 2016. p. 1–6.
   Google Scholar
• Ambede A, Shreejith S, Vinod AP, et al. Design and realization of variable digital filters for software-defined radio channelizers using an improved coefficient decimation method. IEEE Trans Circuits Syst-II: Express Briefs. 2016 Jan;63(1):59–63.
   Web of Science ®Google Scholar
• Raschella A, Umbert A. Implementation of cognitive radio networks to evaluate spectrum management strategies in real-time. Comput Commun. 2016 Apr;79(1):37–52.
   Google Scholar
• Shabara Y, Mohamed A, Ali AKA. A hardware implementation for efficient spectrum access in cognitive radio networks. IEEE Wireless Communications and Networking Conference (WCNC), San Francisco (CA); Mar 2017, pp. 1–6.
   Google Scholar
• Sadek A, Mostafa H, Nassar A, et al. Towards the implementation of multiband multi-standard software defined radio using dynamic partial reconfiguration. Int J Commun Syst. 2017 Jun. doi:10.1002/dac.3342.
   Web of Science ®Google Scholar
• Jashbi F, So DKC. Hybrid overlay/underlay cognitive radio network with MC-CDMA. IEEE Trans Veh Technol. 2016 Apr;65(4):2038–2047.
   Web of Science ®Google Scholar
• Yang H, Xie X, Vaslilakos AV. Stakelberg game-based power control with outage probability constraints for cognitive radio networks. Int J Distrib Sensor Networks. 2015;2015:1–9.
   Google Scholar
• Tang M, Xin Y. Energy efficient power allocation in cognitive radio network using coevolution chaotic particle swarm optimization. Comput Networks. 2016 May;100(8):1–11.
   Google Scholar
• Wang S, Ruby R, Leung VCM, et al. Energy-efficient power allocation for multi-user single-AF-relay underlay cognitive radio networks. Comput Networks. 2016 Jul;103(5):115–128.
   Google Scholar
• Yand L, Alouini MS. Performance comparison of different selection combining algorithms in presence of co-channel interference. IEEE Trans Veh Technol. 2006;55(2):559–571.
   Web of Science ®Google Scholar
• Nachtigall J, Zubho A, Redlich J-P. The impact of adjacent channel interference on multi-radio systems using IEEE 802.11. Proceedings of the International Wireless Communication and Mobile Computing Conference, Crete island, Greece; Aug 2008. p. 874–881.
   Google Scholar
• Angalakis V, Papadakis S, Siris VA, et al. Adjacent channel interference in IEEE 802.11a is harmful: Tested validation of simple qualification model. IEEE Comm Mag. 2010;49(3):334–344.
   Web of Science ®Google Scholar
• Shamson ZA, Abdulrazak LF, Rahman TA. Co-channel and adjacent channel interference evaluation for IMT-Advanced co-existence with existing fixed systems. Proceedings of the IEEE International RF and Microwave Conference, Kuala Lumpur; Dec 2008. p. 65–69.
   Google Scholar
• Sum CS, Funada R, Wang J, et al. Error performance and throughput evaluation of a multi Gbps millimeter-wave WPAN systems in the presence of co-channel and adjacent channel interference. IEEE J Sel Areas Commun. 2009 Oct;27(8):1433–1442.
   Web of Science ®Google Scholar
• Hu D, Mao S. On co-channel and interference channel interference mitigation in cognitive radio networks. Proc. IEEE Military Communications Conference, Baltimore (MD); Nov 2011. p. 13–18.
   Google Scholar
• Bedeer E, Dobre OA, Ahmed MH, et al. Rate–interference tradeoff in OFDM-based cognitive radio systems. IEEE Trans Veh Technol. 2015 Sep;64(9):4292–4298.
   Web of Science ®Google Scholar
• Bansal G, Hasan Z, Hossain J, et al. Subcarrier and power adaptation for multiuser OFDM based cognitive radio systems. Proceedings of the National Conference on Comm. (NCC), Chennai; Jan 2010. p. 29–31.
   Google Scholar
• Ding L, Nagaraju PB, Melodia T, et al. Software defined joint routing and waveform selection for cognitive ad hoc networks. Proceedings of the IEEE Military Communications Conference, San Jose (CA); Nov 2010. p. 1453–1459.
   Google Scholar
• Khoshkholg MG, Navaie K, Yanikomeroglu H. Access strategies for spectrum sharing in fading environment: overlay, underlay and mixed. IEEE Trans Mobile Comput. 2010 Dec;9(12):1780–1793.
   Web of Science ®Google Scholar
• Higuchi K, Benjebbour A. Non-orthogonal multiple access (NOMA) with successive interference cancellation for future radio access. IEICE Trans Commun. 2015 Mar;E98-B(3):403–414.
   Google Scholar
• Islam SMR, Avazov N, Dobre OA, et al. Power domain non-orthogonal multiple access (NOMA) in 5G systems: potential and challenges. IEEE Commun Surv Tutorials. 2017;19(2):721–742.
   Web of Science ®Google Scholar
• Han W, Zhang Y, Wang X, et al. Orthogonal power division multiple access: a Green communication perspective. IEEE J Sel Areas Commun. 2016 Dec;34(12):3828–3842.
   Web of Science ®Google Scholar
• Yang Z, Ding Z, Fan P, et al. On the performance of non-orthogonal multiple access systems with partial channel information. IEEE Trans Commun. 2016 Feb;64(2):656–657.
   Web of Science ®Google Scholar
• Pandit S, Singh G. Spectrum sharing in cognitive radio networks: medium access control protocol based approach. Cham: Springer; 2017.
   Google Scholar
• Ding Z, Liu Y, Choi J, et al. Applications of non-orthogonal multiple access in LTE and 5G networks. IEEE Commun Mag. 2017 Feb;55(2):185–191.
   Web of Science ®Google Scholar
• Liu Y, Ding Z, Elkashlan M, et al. Nonorthogonal multiple access in large-scale underlay cognitive radio networks. IEEE Trans Veh Technol. 2016 Dec;65(12):10152–10157.
   Web of Science ®Google Scholar
• Lv L, Chen J, Ni Q. Cooperative non-orthogonal multiple access in cognitive radio. IEEE Commun Lett. 2016 Oct;20(10):2059–2062.
   Web of Science ®Google Scholar
• Nikopour H, Yi E, Bayesteh A, et al. SCMA for downlink multiple access of 5G wireless networks. Proceedings of the IEEE Global Communication Conference, Austin, (TX); Dec 2014. p. 3940–3945.
   Google Scholar
• Nikopour H, Baligh H. Sparse code multiple access. Proceedings of the IEEE International Symposium on Personal Indoor and Mobile Radio Communication, London; Sep 2013. p. 332–336.
   Google Scholar
• [cited 2016 Feb 12]. Available from: http:/wwwen.zte.com.cn/endata/magazine/ztetechnologies/2015/no3/201505/P020150512471681585293.pdf
   Google Scholar
• Yunzheng T, Long L, Shang L, et al. A survey: several technologies of non-orthogonal transmission for 5G. Chin Commun. 2015 Oct;12(10):1–15.
   Web of Science ®Google Scholar
• Zheng J, Li B, Su X, et al. Pattern division multiple access (PDMA) technique for cellular future radio access. Proceedings of the IEEE International Conference on Wireless Communications and Signal Processing, Nanjing; Oct 2015. p. 1–5.
   Google Scholar
• Do D-T, Le A-T, Le C-B, et al. On exact outage and throughput performance of cognitive radio based non-orthogonal multiple access networks with and without D2D link. Sensors. 2019 Jul;19(5):1–17.
   Google Scholar
• Stoica R-A, Abreu GTF, Ishibashi K. Massively concurrent non-orthogonal multiple access for 5G networks and beyond. IEEE Access. 2019 Jun;7:82080–82100.
   Web of Science ®Google Scholar
• Thakur P, Kumar A, Pandit S, et al. Frameworks of non-orthogonal multiple access techniques in cognitive radio communication systems. Chin Commun. 2019 Jun;16(6):129–149.
   Web of Science ®Google Scholar
• Thakur P, Singh G. Sum-Rate analysis of MIMO based CR-NOMA communication system. Proceedings of the 5th International Conference on Image Information Processing, Waknaghat, India; Nov 2019. p. 1–5.
   Google Scholar
• Fettweis G, Krondorf M, Bittner S. GFDM- Generalized frequency division multiplexing. Proceedings of the IEEE Vehicular Technology Conference, Barcelona; 2009. p. 1–4.
   Google Scholar
• Dutta R, Michailow N, Lantmaier M, et al. GFDM interference cancellation for flexible cognitive radio PHY design. Proceedings of the IEEE Vehicular Technology Conference, Quebec City QC; 2012. p. 1–5.
   Google Scholar
• Gaspar I, Mendes L, Matthe M, et al. GFDM – a framework for virtual PHY services in 5G networks. arXiv:1507.04608v1[cs. IT] 16 Jul 2015.
   Google Scholar
• Zhang Y, Zheng J, Chen H-H. Cognitive radio networks: architectures, protocols, and standards. Boca Raton (FL): CRC Press, Inc.; 2010.
   Google Scholar
• Li D. Opportunistic DF-AF selection for cognitive relay networks. IEEE Trans Veh Technol. 2016 Apr;65(4):2790–2796.
   Web of Science ®Google Scholar
• Oi Y, Imran A, Souza RD, et al. On the optimization of distributed compression in multi-relay cooperative networks. IEEE Trans Veh Technol. 2016 Apr;65(5):2114–2128.
   Google Scholar
• Sengupta S, Subbalakshmi KP. Open research issues in multi-hop cognitive radio networks. IEEE Commun Mag. 2013;51(4):168–178.
   Web of Science ®Google Scholar
• Qadirn J, Baig A, Ali A, et al. Multicasting in cognitive radio networks: algorithms, techniques and protocols. J Network Comput Appl. 2014 Oct;45:44–61.
   Web of Science ®Google Scholar
• Arachchige CJL, Venkatesan S, Chandrasekaran R, et al. Minimal time broadcasting in cognitive radio networks. Proceedings of the International Conference on Distributed Computing and Networking (ICDCN), Bangalore, India; 2011. p. 364–375.
   Google Scholar
• Song Y, Xie J. A QoS-based broadcast protocol for multi-hop cognitive radio ad hoc networks under blind information. Proc. IEEE Global Telecommunication Conference (GLOBECOM), Houston (TX); Dec 2011. p. 1–5.
   Google Scholar
• Song Y, Xie J. A distributed broadcast protocol in multi-hop cognitive radio ad hoc networks without a common control channel. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Orlando (FL); Mar 2012. p. 2273–2281.
   Google Scholar
• Song Y, Xie J, Wang X. A novel unified analytical model for broadcast protocol in multi-hop cognitive radio ad-hoc networks. IEEE Trans Mobile Comput. 2014 Aug;13(8):1653–1667.
   Web of Science ®Google Scholar
• Song Y, Xie J. BRACER: a distributed broadcast protocol in multi-hop cognitive radio ad-hoc networks with collision avoidance. IEEE Trans Mobile Comput. 2015 Mar;14(3):509–524.
   Web of Science ®Google Scholar
• Kaiser T, Cao H, Jiang W, et al. A current snapshot and some thoughts on commercialization for future cellular systems. J Signal Process Syst. 2013 Dec;73(3):217–225.
   Web of Science ®Google Scholar
• Martinez ML, Alcaraz JJ, Alonso JV, et al. Automated spectrum trading mechanisms: understanding the big picture. Wireless Networks. 2015 Feb;21(2):685–708.
   Web of Science ®Google Scholar
• Khattab A, Bayoumi MA. Standardization of cognitive radio networking: a comprehensive survey. Ann Telecommun. 2015 Dec;70(3):465–477.
   Google Scholar
• https://www.theguardian.com/technology/2017/jun/27/petya-ransomware-cyber-attack-who-what-why-how; [cited 2017 Oct 6].
   Google Scholar
• Naeem A, Rehmani MH, Saleem Y, et al. Network coding in cognitive radio networks: a comprehensive survey. IEEE Commun Surv Tutorials. 2017;19(3):1945–1973.
   Web of Science ®Google Scholar
• Cardenas A, Radosavac S, Baras J. Evaluation of detection algorithms for MAC layer misbehavior: theory and experiments. IEEE/ACM Trans Networking. 2009;17(2):605–617.
   Web of Science ®Google Scholar
• Fragkiadakis AG, Tragos EZ, Askoxylakis IG. A survey on security threats and detection techniques in cognitive radio networks. IEEE Commun Surv Tutorials. 2013;15(1):428–445.
   Web of Science ®Google Scholar
• Soto J, Nogueira M. A framework for resilient and secure spectrum sensing on cognitive radio networks. Comput Networks. 2017 Mar;115:130–138.
   Web of Science ®Google Scholar
• Xu D, Li X. Optimal power allocation for cognitive radio networks with primary user secrecy rate loss constraint. Proceedings of the IEEE International Conference on Communication, London; Jun 2015. p. 7615–7621.
   Google Scholar
• Pazin L, Leviatan Y. Rotated-T slot antenna for cognitive radio operation in the 3.1–6 GHz frequency range. J Electromagn Waves Appl. 2015 May;29(9):1149–1156.
   Web of Science ®Google Scholar
• Wu Y, Zhang T, Tsang D. Joint pricing and power allocation for dynamic spectrum access networks with stackelberg game model. IEEE Trans Wireless Commun. 2011;10(1):12–19.
   Web of Science ®Google Scholar
• Hu H, Zhang H, Yu H. Energy-efficient sensing for delay-constrained cognitive radio systems via convex optimization. J Optim Theory Appl. 2016 Jan;168(1):310–331.
   Web of Science ®Google Scholar
• Liu Z, Wang P, Xia Y, et al. Chance-constraint optimization of power control in CRNs. Peer-to-Peer Networking Appl. 2016 Jan;9(1):245–253.
   Web of Science ®Google Scholar
• Chen W-H, Lin W-R, Tsao H-C, et al. Probabilistic power allocation for CRNs with outage constraints and one-bit side information. IEEE Trans Signal Process. 2016 Feb;64(4):867–881.
   Web of Science ®Google Scholar
• Hu H, Zhang H, Li N. Location-information-assisted joint spectrum sensing and power allocation for CRNs with primary-user outage constraint. IEEE Trans Veh Technol. 2016 Feb;65(2):658–672.
   Web of Science ®Google Scholar
• Oro SD, Ekici E, Palazzo S. Optimal power allocation and scheduling under jamming attacks. IEEE/ACM Trans Networking. 2017 Jun;25(3):1310–1323.
   Web of Science ®Google Scholar
• Zhu G-X, Pei X-B, Qu D-M, et al. Joint bandwidth allocation and power control with interference constraints in multi-hop CRNs. J Zhejiang Univ-Sci C (Comput & Electron). 2010;11(2):139–150.
   Web of Science ®Google Scholar
• Almasoud AM, Kamal AE. Multi-objective optimization for many-to-many communication in CRNs. IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA; Dec 2015. p. 1–6.
   Google Scholar
• Balieiro A, Yoshioka P, Dias K, et al. A multi-objective genetic optimization for spectrum sensing in cognitive radio. Expert Syst Appl. 2014 Jun;41(18):3640–3650.
   Google Scholar
• Thakur P, Singh G. Security and interference management in the cognitive inspired internet of medical things. In: Amit Kumar Singh and Mohamed Elhoseny, editors. Intelligent data security Solutions for e-health Applications. Elsevier; Forthcoming 2020.
   Google Scholar
• Dai H, Liu Y, Chen G, et al. Safe charging for wireless power transfer. IEEE/ACM Trans Networking. 2017 Sep;25(6):3531–3544.
   Web of Science ®Google Scholar
• Shkolnikov YP, et al. “Electromagnetic interference and exposure from household wireless networks. in IEEE Symposium on Product Compliance Engineering Proceedings, Anaheim (CA); Oct 2011. p. 1–5.
   Google Scholar
• Gandhi OP, Morgan LL, Salles AAD, et al. Exposure limits: The underestimation of absorbed cell phone radiation, especially in children. Electromagn Biol Med. 2011;31(1):34–51.
   PubMed Web of Science ®Google Scholar
• Karinen A, Heinävaara S, Nylund R, et al. Mobile phone radiation might alter protein expression in human skin. BMC Genomics. 2008;9(1):77.
   PubMedGoogle Scholar
• Hygienic standard for environmental electromagnetic waves. [cited 1987]. [Online]. Available from: http://www.moh.gov.cn/zwgkzt/pgw/201212/34317.shtml
   Google Scholar
• Ahlbom A, et al. Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300 GHz). International commission on non-ionizing radiation protection. Health Phys. 1998;74(4):494–522.
   PubMed Web of Science ®Google Scholar