Drone-Aided Networking with Massive Connectivity and High Spectral Efficiency Enabled

Author:

Bing Li1ORCID,Hu Lanke1,Gu Yating1ORCID,Yin Yue2

Affiliation:

1. School of Software, Northwestern Polytechnical University, Xi’an 710072, China

2. School of Microelectronics, Northwestern Polytechnical University, Xi’an 710072, China

Abstract

Drone-aided networking is considered a potential candidate for internet of things (IoT) networking in 5G and beyond, where drones are deployed to serve a large number of devices simultaneously for data collection, surveillance, remote sensing, etc. However, challenges arise due to massive connectivity requests as well as limited power budgets. To this end, this paper focuses on the design of drone-aided IoT networking, where a drone access point serves a large number of devices for efficient data transmission, collection, and remote sensing. Constant envelope signaling such as minimum shift keying (MSK) family is considered to avoid potential significant power inefficiency due to nonlinear distortion. To this end, code-domain non-orthogonal multiple access (NOMA) is developed and analyzed in terms of achievable sum spectral efficiency. Further, power allocation is derived based on the aforementioned analysis and is demonstrated to offer significantly improved performance in terms of sum spectral efficiency and user load. Simulation results confirm the feasibility of the proposed design and shows that the designed system can attain the promised performance using either simple convolutional code or complex polar code. The proposed system can be used in scenarios such as deep space communications, where MSK family signaling is adopted as well.

Funder

National Natural Science Foundation of China

Natural Science Basic Research Plan in Shaanxi Province of China

Ministry of Industry and Information Technology of the People’s Republic of China

Fundamental Research Funds for the Central Universities, Northwestern Polytechnical University

Aeronautical Science Foundation of China

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

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