Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

Bintao Hu; Wenzhang Zhang; Hengyan Liu; Junwei Zhang; Zhiqiang Yao; Yue Yin

doi:10.1109/CCAI65422.2025.11189810

Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

Bintao Hu
, Wenzhang Zhang
, Hengyan Liu
, Junwei Zhang
, Zhiqiang Yao
, Yue Yin^*

^*Corresponding author for this work

Communication University of China
XiangTan University
Keio University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Low-altitude economy (LAE) is one of the important scenarios in 6G non-orthogonal multiple access (NOMA)-assisted Internet of Things (IoT) wireless caching networks (WCNs). It is necessary to allow the LAE IoT networks to cache the most popular files at the network edge to reduce the traffic and latency consumption over the backhaul links. In this work, we consider an unmanned aerial vehicle (UAV)-assisted LAE IoT WCN system to minimise the total latency based on content placement phase and content delivery phase models. To overcome the optimisation problem, we propose a genetic algorithm (GA)-based power allocation joint optimisation (GA-PAJO) algorithm among all IoT user devices (UDs) by jointly optimising power allocation at the BS and each UAV. Simulation results demonstrate that our proposed optimisation scheme outperforms the benchmarks in terms of minimising total latency among all IoT UDs.

Original language	English
Title of host publication	2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	324-328
Number of pages	5
ISBN (Electronic)	9798331535674
DOIs	https://doi.org/10.1109/CCAI65422.2025.11189810
Publication status	Published - 2025
Event	5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025 - Hybrid, Haikou, China Duration: 23 May 2025 → 25 May 2025

Publication series

Name	2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025

Conference

Conference	5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025
Country/Territory	China
City	Hybrid, Haikou
Period	23/05/25 → 25/05/25

Keywords

genetic algorithm
internet of things
Low-altitude economy
NOMA
unmanned aerial vehicle
wireless caching network

Access to Document

10.1109/CCAI65422.2025.11189810

Cite this

Hu, B., Zhang, W., Liu, H., Zhang, J., Yao, Z., & Yin, Y. (2025). Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks. In 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025 (pp. 324-328). (2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCAI65422.2025.11189810

Hu, Bintao ; Zhang, Wenzhang ; Liu, Hengyan et al. / Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks. 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 324-328 (2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025).

@inproceedings{40d5c0890e68410a837c57aa485fa2a2,

title = "Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks",

abstract = "Low-altitude economy (LAE) is one of the important scenarios in 6G non-orthogonal multiple access (NOMA)-assisted Internet of Things (IoT) wireless caching networks (WCNs). It is necessary to allow the LAE IoT networks to cache the most popular files at the network edge to reduce the traffic and latency consumption over the backhaul links. In this work, we consider an unmanned aerial vehicle (UAV)-assisted LAE IoT WCN system to minimise the total latency based on content placement phase and content delivery phase models. To overcome the optimisation problem, we propose a genetic algorithm (GA)-based power allocation joint optimisation (GA-PAJO) algorithm among all IoT user devices (UDs) by jointly optimising power allocation at the BS and each UAV. Simulation results demonstrate that our proposed optimisation scheme outperforms the benchmarks in terms of minimising total latency among all IoT UDs.",

keywords = "genetic algorithm, internet of things, Low-altitude economy, NOMA, unmanned aerial vehicle, wireless caching network",

author = "Bintao Hu and Wenzhang Zhang and Hengyan Liu and Junwei Zhang and Zhiqiang Yao and Yue Yin",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025 ; Conference date: 23-05-2025 Through 25-05-2025",

year = "2025",

doi = "10.1109/CCAI65422.2025.11189810",

language = "English",

series = "2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "324--328",

booktitle = "2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025",

}

Hu, B , Zhang, W , Liu, H, Zhang, J, Yao, Z & Yin, Y 2025, Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks. in 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025. 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025, Institute of Electrical and Electronics Engineers Inc., pp. 324-328, 5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025, Hybrid, Haikou, China, 23/05/25. https://doi.org/10.1109/CCAI65422.2025.11189810

Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks. / Hu, Bintao ; Zhang, Wenzhang ; Liu, Hengyan et al.
2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 324-328 (2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

AU - Hu, Bintao

AU - Zhang, Wenzhang

AU - Liu, Hengyan

AU - Zhang, Junwei

AU - Yao, Zhiqiang

AU - Yin, Yue

PY - 2025

Y1 - 2025

N2 - Low-altitude economy (LAE) is one of the important scenarios in 6G non-orthogonal multiple access (NOMA)-assisted Internet of Things (IoT) wireless caching networks (WCNs). It is necessary to allow the LAE IoT networks to cache the most popular files at the network edge to reduce the traffic and latency consumption over the backhaul links. In this work, we consider an unmanned aerial vehicle (UAV)-assisted LAE IoT WCN system to minimise the total latency based on content placement phase and content delivery phase models. To overcome the optimisation problem, we propose a genetic algorithm (GA)-based power allocation joint optimisation (GA-PAJO) algorithm among all IoT user devices (UDs) by jointly optimising power allocation at the BS and each UAV. Simulation results demonstrate that our proposed optimisation scheme outperforms the benchmarks in terms of minimising total latency among all IoT UDs.

AB - Low-altitude economy (LAE) is one of the important scenarios in 6G non-orthogonal multiple access (NOMA)-assisted Internet of Things (IoT) wireless caching networks (WCNs). It is necessary to allow the LAE IoT networks to cache the most popular files at the network edge to reduce the traffic and latency consumption over the backhaul links. In this work, we consider an unmanned aerial vehicle (UAV)-assisted LAE IoT WCN system to minimise the total latency based on content placement phase and content delivery phase models. To overcome the optimisation problem, we propose a genetic algorithm (GA)-based power allocation joint optimisation (GA-PAJO) algorithm among all IoT user devices (UDs) by jointly optimising power allocation at the BS and each UAV. Simulation results demonstrate that our proposed optimisation scheme outperforms the benchmarks in terms of minimising total latency among all IoT UDs.

KW - genetic algorithm

KW - internet of things

KW - Low-altitude economy

KW - NOMA

KW - unmanned aerial vehicle

KW - wireless caching network

UR - https://www.scopus.com/pages/publications/105020973616

U2 - 10.1109/CCAI65422.2025.11189810

DO - 10.1109/CCAI65422.2025.11189810

M3 - Conference Proceeding

AN - SCOPUS:105020973616

T3 - 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025

SP - 324

EP - 328

BT - 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2025

Y2 - 23 May 2025 through 25 May 2025

ER -

Hu B , Zhang W , Liu H, Zhang J, Yao Z, Yin Y. Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks. In 2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 324-328. (2025 IEEE 5th International Conference on Computer Communication and Artificial Intelligence, CCAI 2025). doi: 10.1109/CCAI65422.2025.11189810

Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

AIOT-Empowered Smart Vehicle Research, Teaching and Learning Exploration

Development and Commercial Potential of AI-Assisted Smart Wearable Devices

Internet of Things Engineering Teaching Innovation Driven by AI Integration and Application

Cite this

Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Projects

AIOT-Empowered Smart Vehicle Research, Teaching and Learning Exploration

Development and Commercial Potential of AI-Assisted Smart Wearable Devices

Internet of Things Engineering Teaching Innovation Driven by AI Integration and Application

Cite this