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Joint lifetime-outage optimization in relay-enabled IoT networks—A deep reinforcement learning approach

Title	Joint lifetime-outage optimization in relay-enabled IoT networks—A deep reinforcement learning approach
Author	Heidarpour, A. R., Heidarpour, M. R., Ardakani, M., Tellambura, C., Uysal, Murat
Publication Date:	2023-01
Publication Place	- IEEE
Subject	Cooperative communication, Deep reinforcement learning, Internet of Things, Lifetime, Multiple relay selection
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1089-7798
Record ID	9c4ca822-63ff-4cfc-94a5-3916e1779676
Library Location	Electrical & Electronics Engineering
Date	2023-01
Sample Text	Network lifetime maximization in Internet of things (IoT) is of paramount importance to ensure uninterrupted data transmission and reduce the frequency of battery replacement. This letter deals with the joint lifetime-outage optimization in relay-enabled IoT networks employing a multiple relay selection (MRS) scheme. The considered MRS problem is essentially a general nonlinear 0-1 programming which is NP-hard. In this work, we use the application of the double deep Q network (DDQN) algorithm to solve the MRS problem. Our results reveal that the proposed DDQN-MRS scheme can achieve superior performance than the benchmark MRS schemes.
DOI	10.1109/LCOMM.2022.3214146
Cilt	27

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Joint lifetime-outage optimization in relay-enabled IoT networks—A deep reinforcement learning approach

Author Heidarpour, A. R., Heidarpour, M. R., Ardakani, M., Tellambura, C., Uysal, Murat

Publication Date 2023-01

Publication Place - IEEE

Subject Cooperative communication, Deep reinforcement learning, Internet of Things, Lifetime, Multiple relay selection

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1089-7798

Record ID 9c4ca822-63ff-4cfc-94a5-3916e1779676

Library Location Electrical & Electronics Engineering

Date 2023-01

Sample Text Network lifetime maximization in Internet of things (IoT) is of paramount importance to ensure uninterrupted data transmission and reduce the frequency of battery replacement. This letter deals with the joint lifetime-outage optimization in relay-enabled IoT networks employing a multiple relay selection (MRS) scheme. The considered MRS problem is essentially a general nonlinear 0-1 programming which is NP-hard. In this work, we use the application of the double deep Q network (DDQN) algorithm to solve the MRS problem. Our results reveal that the proposed DDQN-MRS scheme can achieve superior performance than the benchmark MRS schemes.

DOI 10.1109/LCOMM.2022.3214146

Cilt 27