DRL-based Resource Management for Task-Centered Semantic Communication

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00381942" target="_blank" >RIV/68407700:21230/24:00381942 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/PIMRC59610.2024.10817269" target="_blank" >https://doi.org/10.1109/PIMRC59610.2024.10817269</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PIMRC59610.2024.10817269" target="_blank" >10.1109/PIMRC59610.2024.10817269</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DRL-based Resource Management for Task-Centered Semantic Communication

Popis výsledku v původním jazyce

The evolution of Artificial Intelligence (AI) integrated with the Sixth-generation (6G) framework poses significant challenges to low-latency applications. Recently, semantic communication has emerged as a promising technique for future intelligent applications. However, the resource management problem combined with semantics is not fully explored. In this paper, we present a deep reinforcement learning-based twin-delayed deep deterministic policy gradient (TD3) for task-centered semantic communication. The proposed TD3 algorithm optimizes bandwidth, and semantic information and prioritizes data with maximum signal-to-noise ratio (SNR) for the efficient transmission of useful information. Simulation results demonstrate the effectiveness of the proposed TD3 scheme compared to state-of-the-art work in terms of transmission efficiency by up to 36% for varying users and up to 33% for varying SNR.

Název v anglickém jazyce

DRL-based Resource Management for Task-Centered Semantic Communication

Popis výsledku anglicky

The evolution of Artificial Intelligence (AI) integrated with the Sixth-generation (6G) framework poses significant challenges to low-latency applications. Recently, semantic communication has emerged as a promising technique for future intelligent applications. However, the resource management problem combined with semantics is not fully explored. In this paper, we present a deep reinforcement learning-based twin-delayed deep deterministic policy gradient (TD3) for task-centered semantic communication. The proposed TD3 algorithm optimizes bandwidth, and semantic information and prioritizes data with maximum signal-to-noise ratio (SNR) for the efficient transmission of useful information. Simulation results demonstrate the effectiveness of the proposed TD3 scheme compared to state-of-the-art work in terms of transmission efficiency by up to 36% for varying users and up to 33% for varying SNR.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

ISBN

9798350362244

ISSN

2166-9570

e-ISSN

2166-9589

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Anchorage, Alaska

Místo konání akce

Valencia

Datum konání akce

2. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001450175000112