Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 21, Issue 4, Pages 2573-2590Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3113640
Keywords
Unmanned aerial vehicles (UAVs); full-duplex (FD); non-orthogonal multiple access (NOMA); optimal UAV placement; power allocation; sum-rate maximization
Funding
- Ministry of Science and Technology of Taiwan [MOST 109-2221-E-110-050-MY3, MOST 110-2221-E-110-020]
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This paper investigates the improvement of overall sum-rate throughput in an unmanned aerial vehicles (UAVs)-assisted full-duplex non-orthogonal multiple access (NOMA) system based cellular network. It proposes a two-stage dynamic user clustering method and joint optimization of UAV placement and power allocation to reduce cross-interference. Simulation results show that the proposed solution outperforms conventional schemes.
This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system's performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into K clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.
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