An optimal adaptive coding and modulation mode selection method based on machine learning for underwater acoustic communication

Hyun-Woo Jeong; Ji-Won Jung; Jun-ho Kim; Byoung-sun Ahn

doi:10.7776/ASK.2025.44.5.471

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2025 Vol.44, Issue 5 Preview Page Next Page

Research Article

An optimal adaptive coding and modulation mode selection method based on machine learning for underwater acoustic communication 수중 음향 통신을 위한 머신러닝 기반의 최적의 적응형 부호 및 변조 모드 선택 방식

30 September 2025. pp. 471-481

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Abstract

This paper proposes a Machine Learning (ML) based Adaptive Coding and Modulation (ACM) scheme that dynamically selects the optimal transmission mode in Underwater Acoustic Communication (UAC) environments with highly time-varying characteristics and high propagation loss. The proposed method uses four types of Channel Quality Indicators (CQI) as input features, including Input Signal-to-Noise Ratio (ISNR), Received Signal-to-Noise Ratio (RSNR), Pilot Bit Error Rate (P-BER), and Channel Impulse Response (CIR), to predict the optimal ACM mode while maintaining both communication success probability and transmission efficiency. To improve the reliability of the classifier, only received data set that meet a specific estimated coded bit error rate (EC-BER) threshold are used for training. A random forest classifier is employed to implement the ACM system, achieving an accuracy of approximately 98.7 % in simulations. Compared to conventional fixed transmission schemes, the proposed method which adaptivel y selects the optimal mode achieves up to twice the transmission rate under various channel conditions.

Keywords

Machine Learning (ML)

Adaptive Coding and Modulation (ACM)

Underwater Acoustic Communication (UAC)

Channel Quality Indicator (CQI)

Estimated Coded-Bit Error Rate (EC-BER)

본 논문에서는 시변성이 큰 수중 음향 통신 환경에서 채널 부호화율과 변조 방식을 적응적으로 변경하여 최적의 전송 모드를 선택하는 기계 학습 기반의 Adaptive Coding and Modulation(ACM )방식을 제안한다. 제안한 방식은 Input Signal to Noise Ratio(ISNR), Received Signal to Noise Ratio(RSNR), Pilot-Bit Error Rate(P-BER), Channel Impulse Response(CIR) 등 4가지 Channel Quality Indicator(CQI)를 입력으로 사용하여, 통신 성공 가능성과 전송률을 동시에 고려한 최적의 ACM 모드를 분류기로 예측한다. 특히, Estimated Coded Bit Error Rate (EC-BER)를 기준으로 복호 가능성이 확보된 데이터만을 선별하여 학습 데이터로 활용함으로써, 분류기의 성능과 신뢰성을 높였다. 제안한 방식은 랜덤 포레스트 분류기를 기반으로 구현되었으며, 실험 결과 약 98.7%의 높은 정확도를 보였다. 또한 기존의 고정 전송 방식과 비교하여 제안한 방식은 다양한 채널 조건에서 항상 최적의 모드를 선택하며, 최대 2배 이상의 전송률 향상을 달성하였다.

키워드

기계 학습

적응형 부호 및 변조

수중 음향 통신

채널 품질 지시자

부호화 비트 추정 오류율

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Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 44
No :5
Pages :471-481
Received Date : 2025-07-24
Revised Date : 2025-09-01
Accepted Date : 2025-09-23
DOI :https://doi.org/10.7776/ASK.2025.44.5.471

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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