Research Article
Throughput Optimization for NOMA Cognitive Radios with Multi-UAV Assisted Relay
@INPROCEEDINGS{10.1007/978-3-031-08878-0_7, author={Le-Mai-Duyen Nguyen and Van Nhan Vo and Tran Thi Thanh Lan and Nguyen Minh Nhat and Anand Nayyar and Viet-Hung Dang}, title={Throughput Optimization for NOMA Cognitive Radios with Multi-UAV Assisted Relay}, proceedings={Industrial Networks and Intelligent Systems. 8th EAI International Conference, INISCOM 2022, Virtual Event, April 21--22, 2022, Proceedings}, proceedings_a={INISCOM}, year={2022}, month={6}, keywords={Cognitive Radio (CR) Non-Orthogonal Multiple Access (NOMA) Unmanned Aerial Vehicle (UAV) Continuous Genetic Algorithm (CGA) UAV Relay (UR)}, doi={10.1007/978-3-031-08878-0_7} }- Le-Mai-Duyen Nguyen
Van Nhan Vo
Tran Thi Thanh Lan
Nguyen Minh Nhat
Anand Nayyar
Viet-Hung Dang
Year: 2022
Throughput Optimization for NOMA Cognitive Radios with Multi-UAV Assisted Relay
INISCOM
Springer
DOI: 10.1007/978-3-031-08878-0_7
Abstract
In this paper, we investigate the throughput optimization for the non-orthogonal multiple access (NOMA) cognitive radio (CR) system with multi-unmanned aerial vehicle (UAV) assisted relays. We propose the communication protocol as follows: in the first phase, a secondary transmitter (ST) transmits the signals to the first UAV relay (UR) using non-orthogonal multiple access (NOMA); meanwhile, a ground base station (GBS) communicates with a primary receiver (PR) under the interference of the ST. In the second phase, the first UR applies the decode-and-forward (DF) technique to transfer the signals to the second UR. Simultaneously, the GBS communicates with PR under the interference of the first UR. Similarly, in the next phase, the UR forwards the signals, while the PR receives the information from the GBS without the interference. In the last two phases, the UR and the SRs receive the signals under the GBS’s interference. Accordingly, the outage probability of the primary network and the throughput of the secondary network is analyzed. Moreover, we propose constraint genetic algorithm (CGA) aided obtaining UR’s configurations to optimize the throughput of the secondary network under the constraints of the system performance of the primary network.
