Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/180160
Title: Multiple antenna-based THz communication system with channel correlation
Authors: Sharma, Shubha
Vashistha, Ankush
Madhukumar, A. S.
Keywords: Engineering
Issue Date: 2024
Source: Sharma, S., Vashistha, A. & Madhukumar, A. S. (2024). Multiple antenna-based THz communication system with channel correlation. 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). https://dx.doi.org/10.1109/VTC2024-Spring62846.2024.10683372
Project: FCP-NTU-RG-2022-014
NRF-CRP23-2019-0005
Conference: 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
Abstract: To minimize environmental impairments and channel fading in the THz band, this paper examines a multiple antenna-based THz communication system. The combined effects of path loss, molecular absorption, and statistical fading are considered for the THz system. A comprehensive correlation structure is utilized that uniquely determines the joint probability density function of the THz channel model. The considered correlation structure is a generalization of the correlation models based on hyper-power or power-correlation. The statistical characterizations of channel correlation for the combining schemes such as maximal ratio combining (MRC), selection combining (SC), and equal gain combining (EGC) are presented at the THz receiver. The asymptotic expressions are proposed for the outage probability for the mentioned diversity schemes. The obtained expressions are expressed in closed-form and are highly accurate in the high signal-to-noise ratio (SNR) region. From the analysis, diversity order and degradation due to correlation are determined. The obtained asymptotic expressions enable the easy estimation of the performance especially for more number of antennas, where Monte Carlo simulation takes a long time to execute. Additionally, presented results reveal the factors that can be utilized to optimize the system performance.
URI: https://hdl.handle.net/10356/180160
ISBN: 979-8-3503-8741-4
ISSN: 2577-2465
DOI: 10.1109/VTC2024-Spring62846.2024.10683372
Schools: College of Computing and Data Science 
School of Computer Science and Engineering 
Rights: © 2024 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CCDS Conference Papers

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