A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks
Tóm tắt
Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.
Tài liệu tham khảo
Zeng Y, Liang Y-C, Hoang AT, Zhang R (2010) A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 2010:1–15
Zhang S, Wu T, Lau VKN (2009) A low-overhead energy detection based cooperative sensing protocol for cognitive radio systems. IEEE Trans Wirel Commun 8(11):5575–5581
Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio networks. IEEE Commun Surv Tutor 11(1):116–129
Liang YC, Zeng Y, Peh ECY, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7(4):1326–1337
Kalai AT, Servedio RA (2005) Boosting in presence of noise. J Comput Syst Sci 71(3):266–290
Lee WY, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wirel Commun 7(10):3845–3857
Sutton PD, Nolan KE, Doyle LE (2008) Cyclostationary signatures in practical cognitive radio applications. IEEE J Sel Areas Commun 26(1):13–24
Tumuluru VK, Wang P, Niyato D (2010) A neural network based spectrum prediction scheme for cognitive radio. In: IEEE international conference on communications, pp 1–5
Tumuluru VK, Wang P, Niyato D (2012) Channel status prediction, n for cognitive radio networks. J Wirel Commun Mob Comput 12(10):862–874
Xing X, Jing T, Cheng W, Huo Y, Cheng X (2013) Spectrum prediction in cognitive radio networks. IEEE Wirel Commun 20(2):90–96
He A (2010) A survey of artificial intelligence for cognitive radios. IEEE Trans Veh Technol 59(4):1578–1592
Subhedar M, Birajdar G (2011) Spectrum sensing techniques in cognitive radio networks—a survey. Int J Next Gener Netw 3(2):37–51
Wan X, Hu P, Wang Z (2016) ISM band prediction algorithm based on two dimensional LMBP neural network. J Telecommun Sci 32(3):52–59
Atapattu S, Tellambura C, Jiang H (2001) Energy detection based cooperative spectrum sensing in cognitive radio networks. IEEE Trans Wirel Commun 10(4):1–10
Mannor S, Meir R, Zhang T (2003) Greedy algorithms for classification—consistency convergence rates and adaptivity. J Mach Learn Res 4:713–742
Lin G, Cheng Y, Jiang H et al. (2016) Performance analysis of three-state HMM in shortwave channel estimation. J Commun Technol 49(3):44–59
Sharma SK, Chatzinotas S, Ottersten B (2013) Eigen value based sensing and SNR estimation for cognitive radio in presence of noise correlation. IEEE Trans Veh Technol 62(8):3671–3684
Li H (2015) Cognitive radio based on the support vector machine to estimate the spectrum of leisure. J Mod Electron Technol 38(7):617–627
Zhao Q, Tong L, Swami A, Chen Y (2007) Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: a POMDP framework. IEEE J Sel Areas Commun 25(3):589–600
Che Y, Zhang R, Gong Y (2013) On design of opportunistic spectrum access in the presence of reactive primary users. IEEE Trans Commun 61(7):2678–2691
Xu Y, Lu H, Chen X et al. (2014) Prediction method of cognitive radio spectrum based on support vector machine. J Telecommun Sci 6(17):2855–2863
Noh J, Oh S (2014) Cognitive radio channel with cooperative multi-antenna secondary systems. IEEE J Sel Areas Commun 32(3):539–549