Which Signal-to-Noise Ratio Is Used in Simulations? Transmitter Side versus Receiver Side: A Study Based on Long Term Evolution Downlink Transmission

Author:

Liu Yu-Sun1ORCID,You Shingchern D.2ORCID,Jhan Zong-Ru3,Li Meng-Fan4

Affiliation:

1. Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

2. Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

3. Research and Development Department, Actiontec Electronics, Inc., Taipei 106, Taiwan

4. Software Development Department, A-MTK Co., Ltd., New Taipei City 235, Taiwan

Abstract

The bit error rate (BER) in relation to the signal-to-noise ratio (SNR) serves as a widely recognized metric for assessing the performance of communication systems. The concept of SNR is so integral that many existing studies presume its definition to be understood, often omitting the specifics of its calculation in their simulations. Notably, the computation of SNR from the perspective of the transmitter yields distinct behaviors and outcomes compared to that from the receiver’s side, particularly when the channel encompasses more than mere noise. Typically, research papers utilize the transmitter-side (or ensemble-average) SNR to benchmark the BER performance across various methodologies. Conversely, the receiver-side (or short-term) SNR becomes pertinent when prioritizing the receiver’s performance. In the context of simulating the long-term evolution (LTE) downlink, applying both SNR calculation approaches reveals that the receiver-side SNR not only produces a significantly lower BER compared to the transmitter-side SNR but also alters the relative BER performance rankings among the channel models tested. It is deduced that while the transmitter-side SNR is apt for broad performance comparisons, it falls short in thoroughly examining the BER behavior of a receiver across varying SNR scenarios. Therefore, the transmitter-side SNR is useful when comparing the performance of the simulated system with other studies. Conversely, if the primary concern is the actual BER performance of the receiver, the receiver-side SNR could provide a more accurate performance assessment.

Funder

National Science and Technology Council, Taiwan

Publisher

MDPI AG

Reference28 articles.

1. Yang, K., Huang, Z., Wang, X., and Wang, F. (2019). An SNR Estimation Technique Based on Deep Learning. Electronics, 8.

2. Mean effective gain of antennas in a wireless channel;Glazunov;IET Microw. Antennas Propag.,2009

3. Channel power gain estimation for terahertz vehicle-to-infrastructure networks;Lin;IEEE Commun. Lett.,2022

4. Liu, Y.-S., You, S.D., Jhan, Z.-R., and Li, M.-F. (2018, January 15–16). Comparative study of two signal-to-noise ratio calculation methods in LTE downlink simulations. Proceedings of the Wireless Internet: 11th EAI International Conference, WiCON 2018, Taipei, Taiwan.

5. Ergodic and mixing properties of infinite memory channels;Adler;Proc. Am. Math. Soc.,1961

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