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Related Experiment Video

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A Gradient-generating Microfluidic Device for Cell Biology

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Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device

Hudson Henrique de Souza Lopes¹, Lucas Jose Ferreira Lima¹, Telma Woerle de Lima Soares²

¹Electrical, Mechanical and Computer (EMC) School of Engineering, Federal University of Goias (UFG), Goiânia 74605010, GO, Brazil.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces DDPG-KRP, a deep reinforcement learning method for dynamic resource allocation in next-generation mobile networks. It efficiently manages network slices and device-to-device communications, outperforming other algorithms.

Keywords:

deep reinforcement learning device-to-device network slicing resource allocation

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Area of Science:

Telecommunications Engineering
Computer Science
Artificial Intelligence

Background:

Next-generation mobile networks (B5G/6G) require diverse resource management.
Network slicing (NS) and device-to-device (D2D) communication are key technologies for efficiency.
Integrating NS and D2D can enhance spectrum utilization and network performance.

Purpose of the Study:

To address dynamic resource allocation challenges in wireless networks with NS and D2D.
To develop an efficient deep reinforcement learning (DRL) approach for this complex problem.
To optimize resource allocation policies for maximizing long-term network rewards.

Main Methods:

Proposed DDPG-KRP: a DRL approach combining Deep Deterministic Policy Gradient (DDPG), K-Nearest Neighbors (KNNs), and Reward Penalization (RP).
DDPG-KRP determines resource allocation policies by maximizing cumulative rewards.
RP is used to eliminate undesirable actions, refining the DRL agent's decision-making process.

Main Results:

DDPG-KRP demonstrated efficiency in dynamic resource allocation for wireless networks with slicing.
The proposed method outperformed other DRL algorithms in simulation experiments.
Effective management of resources for combined NS and D2D communications was achieved.

Conclusions:

DDPG-KRP offers a robust solution for resource allocation in advanced mobile networks.
The integration of DRL, NS, and D2D communication presents a promising direction for future network optimization.
This approach enhances scalability and performance in diverse network environments.