A Novel LS/LMMSE Based PSO Approach for 3D-Channel Estimation in Rayleigh Fading
Main Article Content
Abstract
A high transmission rate can be obtained using Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) model. The most commonly used 3D-pilot aided channel estimation (PACE) techniques are Least Square (LS) and Least Minimum Mean Square (LMMSE) error. Both of the methods suffer from high mean square error and computational complexity. The LS is quite simple and LMMSE being superior in performance to LS providing low Bit Error Rate (BER) at high Signal to Noise ratio (SNR). Artificial Intelligence when combined with these two methods produces remarkable results by reducing the error between transmission and reception of data signal. The essence of LS and LMMSE is used priory to estimate the channel parameters. The bit error so obtained is compared and the least bit error value is fine-tuned using particle swarm optimization (PSO) to obtained better channel parameters and improved BER. The channel parameter corresponding to the low value of bit error rate obtained from LS/LMMSE is also used for particle initialization. Thus, the particles advance from the obtained channel parameters and are processed to find a better solution against the lowest bit error value obtained by LS/LMMSE. If the particles fail to do so, then the bit error value obtained by LS/LMMSE is finally considered. It has emerged from the simulated results that the performance of the proposed system is better than the LS/LMMSE estimations. The performance of OFDM systems using proposed technique can be observed from the imitation and relative results.
Downloads
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
K. Kusume and M. Fallgren, " Updated scenarios, requirements and KPIs for 5G mobile and wireless system with recommendations for future investigations, Mobile and wireless communications Enablers for theTwenty - twenty Information Society (METIS) ," 2015. Document Number : ICT – 317669 – METIS / D1.5, Deliverable D1.5.
G. Vrinda and P. Rajoo, "An improved energy aware distributed unequal clustering protocol for heterogenous wireless sensor networks," In :Eng. Sci. Technol., Int. J. 19 (2016) 1050 – 1058.
S. Coleri, M. Ergen, A. Puri and A. Bahai, "Channel estimation techniques based on pilot arrangement in OFDM systems," IEEE Trans. Broadcast. 48 (3) (2002), 223–229.
C.Y.L.Ting and An Lin, " Predictive equalizer design for DVB - T system," In : IEEE Int. Symp. Circuits Syst. 2 (2005), 940–943.
DVB Document A133, February, 2012. Implementation guidelines for a second Generation Digital terrestrial television broadcasting system (DVB-T2).
G. L Stuber, J. R. Barry and S.W. Mclaughlin, " Broad band MIMO OFDM Wireless Communications, " Proceedings of the IEEE, 2004; vol: 92; pp. 271-294.
S.H.Muller, R. W. Bauml, R. F. H Fisher and J.B. Huber, "OFDM with reduced peak - to - average power ratio by multiple signal representation," Ann. Telecommun., 1997; vol: 52; pp. 58-67.
V. Tarokh, H. Jafarkhani and A. R. Calderbank, "Space-Time Block Coding for Wireless Communications: Performance Results," IEEE Journal on Selected Areas in Communications,1999; vol: 17; pp. 451-460.
L.Hanzo, M. Munster and B. J Choi., "OFDM and MC-CDMA for Broadband Multi – User Communications, WLANs and Broadcasting," Hoboken, NJ : Wiley-IEEE Press, 2003; Ed: 1; pp.23 – 49.
J. Kennedy and R. Eberhart, " Particle swarm optimization,"in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942 – 1948, December 1995.
J. Kennedy and R. C. Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001.
D.Goldbeg,"Genetic Algorithms in Search,Optimization and Machine Learning,"Addison-Wesley, Readig, Mass, USA, 1989.
M.Y. Alias, S. Chen, and L. Hanzo, "Multiple-antenna-antenna-aided OFDM employing genetic – algorithm - asisted minimum bit error rate multiuser detection," IEEE Transactions on Vehicular Technology, vol. 54, no. 5, pp. 1713–1721, 2005.
Y. Xiao, N. Zhang, Z.-W. Wang and K. Kim2, "Particle Swarm Optimization for MIMO Receivers," IET 2nd International Conference on Wireless, Mobile and Multimedia Networks(ICWMMN) Proceedings, pp. 225-259, IEEE 2013.
C.-T. Wang, " Particle – Swarm – Optimization – Based Minimum - BER Detection for MIMO Systems with Time - Reversal Space - Time Block Codes," 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), IEEE, 2008.
Y. (Geoffrey) Li, " Pilot – symbol - aided channel estimation for OFDM in wireless systems," IEEE Transaction on Vehicular Technology, Vol. 49, no. 4, pp.1207-1215, 2000.
S. Wei and P. Zhiwen, "Iterative LS channel estimation for OFDM systems based on transform - domain processing", International Conference on Wireless Communications, Networking and Mobile Computing, vol. 1, pp.416-419, September 2007.
C. Ribeiro and A. Gameiro, "An OFDM symbol design for reduced complexity MMSE channel estimation", Journal of Communications, vol.3, no.4, September 2008