Improved Preamble Structure for Timing Synchronization in MIMO-OFDM Systems
Suparna Sreedhar A1, Suma Sekhar2, Sakuntala S. Pillai3

1Suparna Sreedhar A, Department of Electronics and Communication, LBS Institute of Science and Technology for Women, Trivandrum, India.
2Suma Sekhar, Department of Electronics and Communication, LBS Institute of Science and Technology for Women, Trivandrum (Kerala), India.
3Sakuntala S. Pillai, Department of Electronics and Communication, Mar Baselios College of Engineering and Technology, Trivandrum (Kerala), India.

Manuscript received on 15 August 2015 | Revised Manuscript received on 25 August 2015 | Manuscript Published on 30 August 2015 | PP: 38-41 | Volume-4 Issue-6, August 2015 | Retrieval Number: F4159084615/15©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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 Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM systems, symbol timing synchronization is important inorder to find an estimate of where the symbol starts. In this paper, an efficient preamble structure is proposed for improving the timing synchronization in MIMO-OFDM systems. The proposed short preamble consists of four sub symbols having equal duration. The first and third sub symbols are Constant Amplitude Zero Autocorrelation (CAZAC) sequences while second and fourth are CAZAC sequences weighted by Pseudorandom Noise (PN) sequences. Simulation results show that the proposed preamble structure could provide sharper correlation peak when compared to the conventional Schmidl’s and Minn’s methods in both AWGN and Rayleigh channels. Also the Correct Detection Rate (CDR) of the proposed method is better than the conventional methods at high SNR values. Hence a better timing synchronization can be achieved.
Keywords: CAZAC, Correct Detection Rate, MIMO, OFDM, Timing Synchronization

Scope of the Article: Deep Learning