Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches

Lingling Zhang; Jianan Zhang; Weijie Tan; Chengkai Tang

doi:10.1109/ICSPCC59353.2023.10400211

Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches

Lingling Zhang
, Jianan Zhang
, Weijie Tan
, Chengkai Tang

School of Electronics and Information

Northwestern Polytechnical University Xian
Guizhou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, we present an adaptive modulation coding scheme based on DDQN algorithm. Taking the different need of communication system into account, we construct two communication strategies, which are energy efficiency prioritized strategy and reliability prioritized strategy. Simulation is conducted over the time varying underwater acoustic channel, with comparison to traditional fixed link and DQN adaptive method. The results show that based on the reliability priority strategy, the DDQN method can select the modulation mode that makes the BER lower in the current environment for data transmission, while based on the energy efficiency priority strategy, the DDQN method could reach higher system throughput and energy efficiency compared to the fixed MCS and DQN adaptive method.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350316728
DOIs	https://doi.org/10.1109/ICSPCC59353.2023.10400211
State	Published - 2023
Event	2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 - Zhengzhou, Henan, China Duration: 14 Nov 2023 → 17 Nov 2023

Publication series

Name	Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

Conference

Conference	2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Country/Territory	China
City	Zhengzhou, Henan
Period	14/11/23 → 17/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Adaptive Modulation and Coding
Deep Reinforcement Learning
Double Deep Q-network
Underwater Acoustic Communication

Access to Document

10.1109/ICSPCC59353.2023.10400211

Cite this

Zhang, L., Zhang, J., Tan, W., & Tang, C. (2023). Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches. In Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPCC59353.2023.10400211

Zhang, Lingling ; Zhang, Jianan ; Tan, Weijie et al. / Optimizing Communication with DDQN-Based Adaptive Modulation Coding : Two Strategic Approaches. Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023).

@inproceedings{7e349d289fd94e08a153bb728207450c,

title = "Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches",

abstract = "In this paper, we present an adaptive modulation coding scheme based on DDQN algorithm. Taking the different need of communication system into account, we construct two communication strategies, which are energy efficiency prioritized strategy and reliability prioritized strategy. Simulation is conducted over the time varying underwater acoustic channel, with comparison to traditional fixed link and DQN adaptive method. The results show that based on the reliability priority strategy, the DDQN method can select the modulation mode that makes the BER lower in the current environment for data transmission, while based on the energy efficiency priority strategy, the DDQN method could reach higher system throughput and energy efficiency compared to the fixed MCS and DQN adaptive method.",

keywords = "Adaptive Modulation and Coding, Deep Reinforcement Learning, Double Deep Q-network, Underwater Acoustic Communication",

author = "Lingling Zhang and Jianan Zhang and Weijie Tan and Chengkai Tang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 ; Conference date: 14-11-2023 Through 17-11-2023",

year = "2023",

doi = "10.1109/ICSPCC59353.2023.10400211",

language = "英语",

series = "Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023",

}

Zhang, L, Zhang, J, Tan, W & Tang, C 2023, Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches. in Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023, Zhengzhou, Henan, China, 14/11/23. https://doi.org/10.1109/ICSPCC59353.2023.10400211

Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches. / Zhang, Lingling; Zhang, Jianan; Tan, Weijie et al.
Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimizing Communication with DDQN-Based Adaptive Modulation Coding

T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

AU - Zhang, Lingling

AU - Zhang, Jianan

AU - Tan, Weijie

AU - Tang, Chengkai

PY - 2023

Y1 - 2023

N2 - In this paper, we present an adaptive modulation coding scheme based on DDQN algorithm. Taking the different need of communication system into account, we construct two communication strategies, which are energy efficiency prioritized strategy and reliability prioritized strategy. Simulation is conducted over the time varying underwater acoustic channel, with comparison to traditional fixed link and DQN adaptive method. The results show that based on the reliability priority strategy, the DDQN method can select the modulation mode that makes the BER lower in the current environment for data transmission, while based on the energy efficiency priority strategy, the DDQN method could reach higher system throughput and energy efficiency compared to the fixed MCS and DQN adaptive method.

AB - In this paper, we present an adaptive modulation coding scheme based on DDQN algorithm. Taking the different need of communication system into account, we construct two communication strategies, which are energy efficiency prioritized strategy and reliability prioritized strategy. Simulation is conducted over the time varying underwater acoustic channel, with comparison to traditional fixed link and DQN adaptive method. The results show that based on the reliability priority strategy, the DDQN method can select the modulation mode that makes the BER lower in the current environment for data transmission, while based on the energy efficiency priority strategy, the DDQN method could reach higher system throughput and energy efficiency compared to the fixed MCS and DQN adaptive method.

KW - Adaptive Modulation and Coding

KW - Deep Reinforcement Learning

KW - Double Deep Q-network

KW - Underwater Acoustic Communication

UR - https://www.scopus.com/pages/publications/85184849717

U2 - 10.1109/ICSPCC59353.2023.10400211

DO - 10.1109/ICSPCC59353.2023.10400211

M3 - 会议稿件

AN - SCOPUS:85184849717

T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 14 November 2023 through 17 November 2023

ER -

Zhang L, Zhang J, Tan W, Tang C. Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches. In Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023). doi: 10.1109/ICSPCC59353.2023.10400211

Optimizing Communication with DDQN-Based Adaptive Modulation Coding: Two Strategic Approaches

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UN SDGs

Keywords

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