A Simulation on lattice reduction techniques for wireless communication MIMO QAM 64x64 and above Preesat Biswas a , Shanti Rathore a , M.R. Khan b a Department of ECE, Dr. C.V. Raman University, Bilaspur, CG, India b Department of ET&T Engineering, Government Engineering College, CSVTU, Jagdalpur, CG, India article info Article history: Received 9 November 2020 Received in revised form 9 November 2020 Accepted 17 November 2020 Available online xxxx Keywords: MIMO ML MMSE MMSE_LLL QAM AWGN Rician channels BER SNR abstract In wireless communication system, signal those pass through in transmission and receiving antenna in MIMO (Multi-input multi- output) channel to lattice reduce over a QAM (quadrature-amplitude-modula tion) system, which is simulated maximum likelihood (ML) estimation and minimum mean square error (MMSE) . In this paper ‘‘A Simulation on Lattice Reduction Techniques for wireless Communication MIMO with QAM 64x64 and above”, here the minimum likelihood (ML) estimation and MMSE for multi-input multi-output (MIMO) channels. Generally, in OFDM (orthogonally frequency division multiplexing) sys- tem of a MMSE system completely eliminates interference and the results in suboptimal performance due to noise enhancement in symbol. The main aim simulated MMSE detector that the transmitter must spend within M QAM quadrature- amplitude-modulation () alphabet and the ML, MMSE allow to trans- mitted signal and remove the interference which comes from in receiver and pass through it. Which is QAM 64x64 above simulated BER (bit error rate) with SNR. Ó 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering. 1. Introduction MIMO (multi-input multi-output) uses in wireless communica- tion system, this technology are generally folded system with increase the capacity system and enhance the system energy effi- cient. In this MIMO system design of low complex signal process- ing techniques, this happened demanding of increase data which is easily to transmitted uses of increase the number of antennas with low power and low noise interfacing. In MIMO channel which transmitted and received signal, they are generally maximum like- lihood (ML) detection [1,2] to optimized but with prohibitive com- plexity .When the MIMO dimension has large cardinally. Liner detection ZF (zero forcing) and MMSE (linear minimum-mean- square-error) have low complexities [4,5]. The Lattice reduction it was improved like ‘‘LLL (Lenstra, Lenstra, Lova ´ sz)” reduction [2] or system reduce [3]. As the transmitted symbols re drawn from QAM (quadrature amplitude modulation) with finite alph-bet and PAM(pulse amplitude- modulation) symbol, the modulus can also be used in MIMO detection for improving the detection performance[6,7]. The unmodulated matrix which is easily solve by MIMO transmis- sion and received signal, like detection and preceding problem. The main solution of transformed back to original domain which is unchanged. OFDM(orthogonal Frequency-Division Multiplexing) avoid frequency selective fading and it high spectrum efficiency to achieve large capacity. Using this OFDM in MIMO channel, reduce the co-channel interference and ZF and MMSE help avoid the higher power transmission. 2. Lattice basis reduction in two dimensions The shortest distance of vector propagation which is lattice with gauss reduce kb1k  kb2k  kb2 + qb1k for all q 2 Z. where L the lattice basis vector on the basis of b 1 ; b 2 ,R 2 be linear independent vectors. In order to b 1 ; b 2 for R 2 is Lagrange-Gauss reduced and reduce the shortest possible distance by the Euclidean norm as shown in the Figs. 1 and 2 is square Lattice and Figs. 3 and 4 represent Hexagonal Lattice. B ¼ 1 0 0 1   is the square lattice and B ¼ 1 2 2 1 0 ffiffiffi 3 p   is the Hexagonal lattice. https://doi.org/10.1016/j.matpr.2020.11.570 2214-7853/Ó 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering. E-mail address: preesat.eipl@gmail.com (P. Biswas) Materials Today: Proceedings xxx (xxxx) xxx Contents lists available at ScienceDirect Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr Please cite this article as: P. Biswas, S. Rathore and M.R. Khan, A Simulation on lattice reduction techniques for wireless communication MIMO QAM 64x64 and above, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.11.570