Abstract
Wireless communication relies heavily on system performance. It is very important to consider channel behavior when designing wireless communication systems. Fading, scattering, interference, and other channel aspects affect the received signal quality. In MATLAB 2015 simulations, Rayleigh, Rician, and Nakagami fading channel models were compared for fading envelope, signal power, and channel capacity. Multipath fading environments require the use of parameters such as source velocity and pdf to analyze and design digital communication systems. The present study analyzes and simulates wireless channel behavior under different distributions of fading.
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References
Kumar, S., Gupta, P.K., Singh, G., Chauhan, D.S.: Performance analysis of rayleigh and rician fading channel models using matlab simulation. Int. J. Intelli. Sys. Appl. 5(9), 94 (2013)
Prabhu, G.S., Shankar, P.M.: Simulation of flat fading using MATLAB for classroom instruction. IEEE Trans. Educ. 45(1), 19–25 (2002)
Khan, M. J., Singh, I., Tayal, S.: BER Performance using BPSK modulation over rayleigh and rician fading channel. In: 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), pp. 434–437. IEEE (April 2022)
Bellorado, J., Ghassemzadeh, S., Kavcic, A.: Approaching the capacity of the MIMO Rayleigh flat-fading channel with QAM constellations, independent across antennas and dimensions. IEEE Trans. Wireless Commun. 5(6), 1322–1332 (2006)
Mohapatra, P.K., Jena, P.K., Bisoi, S.K., Rout, S.K., Panigrahi, S.P.: Channel equalization as an optimization problem. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1158–1163. IEEE (October 2016)
Mohapatra, P.K., Rout, S.K., Bisoy, S.K., Sain, M.: Training Strategy of Fuzzy-Firefly based ANN in Non-linear Channel Equalization. IEEE Access (2022)
Panda, S., Mohapatra, P.K., Panigrahi, S.P.: A new training scheme for neural networks and application in non-linear channel equalization. Appl. Soft Comput. 27, 47–52 (2015). https://doi.org/10.1016/j.asoc.2014.10.040
Mohapatra, P., Samantara, T., Panigrahi, S.P., Nayak, S.K.: Equalization of Communication Channels Using GA-Trained RBF Networks. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D.P. (eds.) Progress in Advanced Computing and Intelligent Engineering. AISC, vol. 564, pp. 491–499. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6875-1_48
Mohapatra, P., Sunita, P., Panigrahi, S.P.: Equalizer Modeling Using FFA Trained Neural Networks. In: Soft Computing: Theories and Applications, pp. 569-577. Springer, Singapore (2018)
Pradyumna, M., et al.: Shuffled Frog-Leaping Algorithm trained RBFNN Equalizer. Int. J. Comp. Info. Sys. Indu. Manage. Appl. 9, pp. 249–256 (2017)
Kumar Mohapatra, P., et al.: Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization. Symmetry 14(10), 2078 (2022)
Rout, S.K., Rath, A.K., Bhagabati, C.: Energy efficient dynamic node localization technique in wireless sensor networks. Indian J. Sci. Technol. 10(15), 1–8 (2017)
Panchal, A., Dutta, A.K.: Performance analysis and design of MIMO power NOMA with estimated parameters error statistics along with SIC and hardware imperfections. IEEE Trans. Veh. Technol. 70(2), 1488–1500 (2021)
Narayana, M., Bhavana, G.: Performance analysis of MIMO system under Fading Channels (Rayleigh and Rician) Using SVD PCA and FSVD. journal of engineering technology 5(2), 116–126 (2016)
Zhang, D., Zhou, P., Jiang, C., Yang, M., Han, X., Li, Q.: A stochastic process discretization method combing active learning Kriging model for efficient time-variant reliability analysis. Comput. Methods Appl. Mech. Eng. 384, 113990 (2021)
Tang, L., Hongbo, Z.: Analysis and simulation of Nakagami fading channel with MATLAB. In: Asia-Pacific Conference on Environmental Electromagnetics, 2003. CEEM 2003. Proceedings, pp. 490–494. IEEE (November 2003)
Gvozdarev, A.S.: The novel approach to the closed-form average bit error rate calculation for the Nakagami-m fading channel. Digital Signal Processing 127, 103563 (2022)
Sijbers, J., den Dekker, A. J., Scheunders, P., Van Dyck, D.: Maximum-likelihood estimation of Rician distribution parameters (2004)
Mounika, I.S.D., Sharma, D., Sharma, P.K.: Analysis of different detection techniques of MIMO in future generation of wireless communication. In: international journal of pure and applied mathematics 114(12), 419–426 (2017)
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Mohapatra, P.K., Rout, S.K., Panda, R.N., Meda, A., Panda, B.K. (2022). Performance Analysis of Fading Channels in a Wireless Communication. In: Panda, M., et al. Innovations in Intelligent Computing and Communication. ICIICC 2022. Communications in Computer and Information Science, vol 1737. Springer, Cham. https://doi.org/10.1007/978-3-031-23233-6_13
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DOI: https://doi.org/10.1007/978-3-031-23233-6_13
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