A reliable command and control (C2) data link is required for unmanned aircraft systems (UAS) operations in order to monitor the status and support the control of UAS. A practical realization of the C2 communication and mission data links for commercial UAS operations is via LTE/5G networks. While the trajectory of each UAS directly determines the flight distance and mission cost in terms of energy dissipation, it also has a strong correlation to the quality of the communication link provided by a serving base station, where quality is defined as the achieved signal-to-interference-plus-noise ratio (SINR) required to maintain the control link of the UAS. Due to signal interference and the use of RF spectrum resources, the trajectory of a UAS not only determines the communication link quality it will encounter, but also influences the link quality of other UAS in its vicinity. Therefore, effective UAS traffic management must plan the trajectory for a group of UAS taking into account the impact to the interference levels of other base stations and UAS communication links. In this paper, an SINR Aware Predictive Planning (SAPP) framework is presented for trajectory planning of UAS leveraging 4G/5G communication networks in a simulated environment. The goal is to minimize flight distance while ensuring a minimum required link quality for C2 communications between UAS and base stations. The
predictive control approach is proposed to address the challenges of the time varying SINR caused by the interference from other UAS’s communication. Experimental results show that the SAPP framework provides more than 3dB improvements on average for UAS communication parameters compared to traditional trajectory planning algorithms while still achieving shortest path trajectories and collision avoidance.
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TAMU‐RPL: Thompson sampling‐based multichannel RPL
For the success of critical applications in the IoT, there is a need to counteract the effects of external interference, especially when the unlicensed spectrum is used. One way of improving the performance is with a routing protocol that quickly reacts to changes in the environment and avoids path and/or frequencies with higher interference. In this paper, we propose an optimization to the RPL protocol, called TAMU‐RPL, that can keep a more accurate estimation of link quality to the neighbors and quickly react to degradation on the links. TAMU‐RPL also uses the quality of links at different frequencies and opportunistically avoids the ones with bad quality. It is evaluated through simulations with connectivity traces from a 40‐node test bed and in a real 5‐node deployment. We compare TAMU‐RPL with a baseline RPL protocol and a Dijkstra‐based shortest‐path algorithm. Results show that TAMU‐RPL can successfully explore the neighbors and obtain ETX values much closer to the ones obtained with a shortest‐path tree, even when link quality changes over time. In the simulation evaluation, TAMU‐RPL was able to double the number of packets received at the sink compared to RPL and reduced the average delay of packets (in time slots) by more than 10%. In the real deployment, the number of packets received at the sink was increased by more than 33%.
more » « less- NSF-PAR ID:
- 10457553
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Transactions on Emerging Telecommunications Technologies
- Volume:
- 31
- Issue:
- 2
- ISSN:
- 2161-3915
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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