Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 6, Pages 4291-4302Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3103892
Keywords
Uplink; Throughput; Resource management; Unmanned aerial vehicles; Optimization; NOMA; Relays; Altitude decision; Line-of-Sight (LoS) propagation; resource optimization; unmanned aerial vehicles (UAVs) relaying; uplink throughput
Categories
Funding
- National Key Research and Development Program of China [2020YFB1807900]
- National Natural Science Foundation of China [61931005]
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This article explores the integration of unmanned aerial vehicles (UAVs) into emergency communications to achieve efficient network recovery. By considering nonorthogonal multiple access (NOMA) technology and jointly optimizing the UAV altitude, power control, and bandwidth allocation, the system's uplink throughput can be improved. Simulation results demonstrate the effectiveness of the algorithm, particularly in the context of AtG LoS propagation advantage.
Integrating unmanned aerial vehicles (UAVs) into emergency communications is a promising way to accomplish efficient network recovery with the advantages of UAV flexibility. To ensure information forwarding from the disaster area, this article considers an emergency communication scenario where a UAV provides uplink relaying services based on nonorthogonal multiple access (NOMA) for a set of disconnected ground wireless access points (APs) under the urban environment. To maximize the system uplink throughput, the UAV altitude, power control, as well as the bandwidth allocation between the access and backhaul links are jointly optimized. Especially, the constraint for the uplink rate fairness is also considered. Our formulated problem is nonconvex due to the complex uplink co-channel interference under the Line-of-Sight (LoS) probability-based Air-to-Ground (AtG) channel. To tackle this issue, we change our formulated problem into an equivalent form by coping with the information-causality and fairness constraints. Then, a joint altitude and resource allocation (JARA) algorithm is developed, which iteratively solves the altitude optimization subproblem and resource optimization subproblem until convergence. For each subproblem, we further introduce auxiliary variables so that it can be solved by using the successive convex approximation (SCA) method. Finally, two benchmarks are used for the throughput comparison, and simulation results verify that the system uplink throughput of our proposed algorithm is improved through the AtG LoS propagation advantage, uplink power control, as well as the bandwidth allocation between the access and backhaul links.
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