Downlink Throughput Maximization in Multi-UAVs Networks Using Discrete Optimization

  • Saadullah KalwarEmail author
  • Kwan-Wu Chin
  • Zhenhui Yuan


Unmanned Aerial Vehicles (UAVs) require frequent transmissions to a ground station. In this paper, we consider Time Division Multiple Access (TDMA) and equip the ground station with a Successive Interference Cancellation (SIC) radio, and thereby, allowing it to receive multiple transmissions simultaneously. A key problem is to identify a suitable TDMA transmission schedule that allows UAVs to transmit successfully and frequently to the ground station. Moreover, in order for SIC to operate, there must be a sufficient difference in received power at the ground station. However, in practice, due to the mobility and orientation of UAVs, the ground station experiences random channel gains. To this end, we adopt a discrete optimization approach to select a transmission schedule that yields the highest expected number of successes against random channel gains. We prove our approach is able to find the optimal transmission schedule. In addition, we propose a novel heuristic approach to generate a subset of transmission schedules for use in large-scale UAV networks. Our results show both proposed approaches yield high throughput under various network conditions. The average number of successful transmissions for schedules generated by our solutions is greater than 70%. In contrast, a competing approach only has an average success rate of less than 50%. Lastly, we conducted a trace-based simulation using data from a testbed with three static or mobile UAVs. Our results show that using a transmission schedule where at most two UAVs transmit yields more transmission successes.


MAC TDMA MPR SIC Discrete optimization Wireless networks 



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Authors and Affiliations

  1. 1.School of Electrical, Computer and Telecommunications Engineering, EISUniversity of WollongongWollongongAustralia
  2. 2.Department of Electronic EngineeringNational University of Ireland, MaynoothMaynoothIreland

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