Optimization of Training Sequence Based Sparse Channel Estimation for Mmwave Communications in 5G
R. Umamaheswari1, Ch. Sumanth Kumar2

1R.Umamaheswari, Vignan’s Institute of Information Technology, Visakhapatnam (A.P), India.
2Ch.Sumanth Kumar, GITAM University, Visakhapatnam (A.P), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 577-581 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2864028419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper to achieve higher data rates with high spectral efficiency and high accuracy we designed training sequence sparse channel estimation based on BAT, Cuckoo and Firefly algorithms. By using the above techniques we design a Training sequence channel estimation to reduce the bit error rate, mean square error and accurate recovery of data. The firefly optimization is the promising technique to reduce the bit error rate and to increase the signal to noise ratio to achieve high spectral efficiency Gbps.
Keyword: Quadrature Amplitude Modulation , Bit Error Rate, Signal to Noise Ratio.
Scope of the Article: 5G Communication