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Oral Session B-2 : Mobile & Communication

A Lightweight CNN Model for SNR Estimation in OFDM Systems

원문정보

Abdullah Al Mahbub, Ijaz Ahmad, Seokjoo Shin

한국차세대컴퓨팅학회 한국차세대컴퓨팅학회 학술대회 ICNGC 2025 The 11th International Conference on Next Generation Computing 2025 2025.12 pp.83-86
피인용수 : 0건 (자료제공 : 네이버학술정보)

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

저자정보

  • 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

참고문헌

자료제공 : 네이버학술정보

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