Earticle

초록

영어

This paper investigates deep learning-based SNR estimation for OFDM systems. A lightweight ResNet-inspired model is applied to estimate SNR under AWGN, Rayleigh, and Rician channels. Specifically, our model consists of two residual blocks to ensure a lightweight design. The dataset includes wide SNR ranges with realistic impairments such as fading and frequency offsets. Performance is evaluated using mean square error (MSE) and mean absolute error (MAE). Results show stable estimation across all channels with low error values in the low SNR regions.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Signal Preprocessing
B. Dataset
C. Data labeling
D. Network training
E. Receiver
III. EVALUATION
IV. CONCUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Abdullah Al Mahbub [ Dept. of Computer Engineering, Chosun University, Gwangju, South Korea ]
• Ijaz Ahmad [ Dept. of Electrical and Computer Engineering, Korea University, Seoul, South Korea ]
• Seokjoo Shin [ Dept. of Computer Engineering, Chosun University, Gwangju, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004