Coordinated Machine Learning for Channel Reuse and Transmission Power Allocation for D2D Communication

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00384985" target="_blank" >RIV/68407700:21230/24:00384985 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/GLOBECOM52923.2024.10901327" target="_blank" >http://dx.doi.org/10.1109/GLOBECOM52923.2024.10901327</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/GLOBECOM52923.2024.10901327" target="_blank" >10.1109/GLOBECOM52923.2024.10901327</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Coordinated Machine Learning for Channel Reuse and Transmission Power Allocation for D2D Communication

Popis výsledku v původním jazyce

Mutual reuse of communication channels among device-to-device (D2D) pairs enhances the spectral efficiency of the mobile networks. However, the interference among D2D pairs mutually reusing the same channels imposes a significant challenge. In combination with allocation of the transmission power of each pair for the reused channels, the problem of joint D2D channel reuse and transmission power allocation becomes NP-hard. Thus, we employ deep deterministic policy gradient (DDPG) to decide how the D2D channels should be reused by the D2D pairs. Then, for the reused channels, we allocate the transmission power of the D2D pairs sharing the channels using deep neural network (DNN). However, combining the DDPG-based channel reuse with the DNN-based transmission power allocation leads to an accumulation of errors introduced by DDPG and DNN. The accumulated errors degrade the overall communication capacity. Thus, we also introduce a coordination between DNN and DDPG to suppress the effect of the error accumulation. Simulation results demonstrate that the proposed DDPG-based channel reuse even without coordination increases the sum capacity by 15% compared to state-of-the-art works. On top of this gain, the coordination of both DDPG and DDN adds another 12% in the sum capacity.

Název v anglickém jazyce

Coordinated Machine Learning for Channel Reuse and Transmission Power Allocation for D2D Communication

Popis výsledku anglicky

Mutual reuse of communication channels among device-to-device (D2D) pairs enhances the spectral efficiency of the mobile networks. However, the interference among D2D pairs mutually reusing the same channels imposes a significant challenge. In combination with allocation of the transmission power of each pair for the reused channels, the problem of joint D2D channel reuse and transmission power allocation becomes NP-hard. Thus, we employ deep deterministic policy gradient (DDPG) to decide how the D2D channels should be reused by the D2D pairs. Then, for the reused channels, we allocate the transmission power of the D2D pairs sharing the channels using deep neural network (DNN). However, combining the DDPG-based channel reuse with the DNN-based transmission power allocation leads to an accumulation of errors introduced by DDPG and DNN. The accumulated errors degrade the overall communication capacity. Thus, we also introduce a coordination between DNN and DDPG to suppress the effect of the error accumulation. Simulation results demonstrate that the proposed DDPG-based channel reuse even without coordination increases the sum capacity by 15% compared to state-of-the-art works. On top of this gain, the coordination of both DDPG and DDN adds another 12% in the sum capacity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA23-05646S" target="_blank" >GA23-05646S: Inteligentní přidělovaní rádiových prostředků a řízení mobility založené na federovaném učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

ISBN

979-8-3503-5125-5

ISSN

2334-0983

e-ISSN

2576-6813

Počet stran výsledku

6

Strana od-do

2701-2706

Název nakladatele

IEEE Industrial Electronic Society

Místo vydání

Vienna

Místo konání akce

Cape Town

Datum konání akce

8. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001511158700449