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Dynamic Resource Allocation and Energy Minimization in the NOMA System for Emerging Network using Deep Learning Algorithm
by
Anoop Kumar Khambra,
Rajesh Kumar Rai
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 59 |
| Year of Publication: 2025 |
| Authors: Anoop Kumar Khambra, Rajesh Kumar Rai |
10.5120/ijca2024924321
|
Anoop Kumar Khambra, Rajesh Kumar Rai . Dynamic Resource Allocation and Energy Minimization in the NOMA System for Emerging Network using Deep Learning Algorithm. International Journal of Computer Applications. 186, 59 ( Jan 2025), 16-20. DOI=10.5120/ijca2024924321
@article{
10.5120/ijca2024924321,
author = {
Anoop Kumar Khambra,
Rajesh Kumar Rai
},
title = { Dynamic Resource Allocation and Energy Minimization in the NOMA System for Emerging Network using Deep Learning Algorithm },
journal = {
International Journal of Computer Applications
},
issue_date = { Jan 2025 },
volume = { 186 },
number = { 59 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = {
16-20
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume186/number59/dynamic-resource-allocation-and-energy-minimization-in-the-noma-system-for-emerging-network-using-deep-learning-algorithm/
},
doi = { 10.5120/ijca2024924321 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-03T00:30:36.814336+05:30
%A Anoop Kumar Khambra
%A Rajesh Kumar Rai
%T Dynamic Resource Allocation and Energy Minimization in the NOMA System for Emerging Network using Deep Learning Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 59
%P 16-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The next generation of wireless network communication requires high data rates and low latency, posing significant challenges in resource allocation. In next-generation networks, resource allocation remains a major issue, with recent approaches focusing on both dynamic and static allocation strategies. The proposed approach utilizes deep learning models, particularly Long Short-Term Memory (LSTM) networks, to optimize power and spectrum allocation in real-time. By leveraging deep learning's ability to handle complex, high-dimensional data, the algorithm adapts to varying channel conditions and user requirements while minimizing energy consumption. A key feature of the proposed model is its capability to dynamically allocate resources based on Channel State Information (CSI) and Quality of Service (QoS) constraints, ensuring the efficient utilization of available bandwidth.
References
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Index Terms
Computer Science
Information Sciences
Keywords