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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

<a href="/en/project/GA23-05646S" target="_blank" >GA23-05646S: Intelligent Radio Resource and Mobility Management based on Federated Learning</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

ISBN

979-8-3503-5125-5

ISSN

2334-0983

e-ISSN

2576-6813

Number of pages

6

Pages from-to

2701-2706

Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Cape Town

Event date

Dec 8, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001511158700449