Optimizing Air-Borne Network-in-a-Box Deployment for Efficient Remote Coverage

Among many envisaged drivers for sixth generation (6G), one is from the United Nation’s Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution which packs the whole wireless network into a single portable and reconfigurable box to support multiple access technologies, such as WiFi, 2G–5G, etc. In this article, we propose low-altitude platform station (LAPS)-based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs nonorthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users (GUs). The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association (UA), beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

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Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-09-06
DOI
https://doi.org/10.1109/jiot.2024.3455439
Citations
3
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
1.05

Funders

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article

Optimizing Air-Borne Network-in-a-Box Deployment for Efficient Remote Coverage

Sidrah Javed, Mohamed‐Slim Alouini, Cheng‐Xiang Wang, Yunfei Chen
3 citations
IEEE Internet of Things Journal
Distributed and Parallel Computing Systems
1.05
article

Optimizing Air-Borne Network-in-a-Box Deployment for Efficient Remote Coverage

Sidrah Javed, Mohamed‐Slim Alouini, Cheng‐Xiang Wang, Yunfei Chen
article en
3 citations

Abstract

Among many envisaged drivers for sixth generation (6G), one is from the United Nation’s Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution which packs the whole wireless network into a single portable and reconfigurable box to support multiple access technologies, such as WiFi, 2G–5G, etc. In this article, we propose low-altitude platform station (LAPS)-based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs nonorthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users (GUs). The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association (UA), beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

IEEE Internet of Things JournalVol. 11(23)
Durham University (GB), King Abdullah University of Science and Technology (SA), Southeast University (CN)
National Natural Science Foundation of China, King Abdullah University of Science and Technology, Horizon 2020 Framework Programme, Engineering and Physical Sciences Research Council, Global Collaborative Research, King Abdullah University of Science and Technology
Affordable and clean energy
Openalex Percentile: Top 22%
Distributed and Parallel Computing Systems
1.05
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