Adaptive Modulation for Wobbling Drone Air-to-Ground Links in Millimeter-Wave Bands
The emerging drones enabled air-to-ground (A2G) communications in millimeter-wave (mm-wave) bands are facing the Doppler effect that arises from the inevitable wobbling of the drones. The fast time-varying channel for drones A2G communications can make the channel state information (CSI) from channel estimation outdated, i.e., imperfect CSI. In this article, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave drones A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the signal transmission time subject to the maximum tolerable BEP. A power control policy is proposed in conjunction with the adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy can maximize the temporally averaged transmission rate while reducing the power consumption by up to 50% for wobbling mm-wave drones A2G links.
Authors
- Songjiang Yang (ORCID: https://orcid.org/0000-0002-9052-9092)
- Zitian Zhang (ORCID: https://orcid.org/0000-0003-1825-735X)
- Xiaoli Chu
- Jiliang Zhang (ORCID: https://orcid.org/0000-0003-3750-6841)
- Jie Zhang (ORCID: https://orcid.org/0000-0002-3354-0690)
Institutions
- Purple Mountain Laboratories (CN)
- State Key Laboratory of Synthetical Automation for Process Industries
- Zhejiang Gongshang University (CN)
- University of Sheffield (GB)
- Northeastern University (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/jiot.2024.3508697
- Citations
- 13
- Primary Topic
- Millimeter-Wave Propagation and Modeling
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- Zhejiang Gongshang University
- National Natural Science Foundation of China
- State Key Laboratory of Synthetical Automation for Process Industries
- National Mobile Communications Research Laboratory, Southeast University