Abstract
Satellite-to-ground communication systems typically operate in environments with high interference levels, complex topologies, and stringent platform constraints. Therefore, intelligent, anti-interference, and low-power systems are required to achieve the desired transmission performance. This paper proposes a system for optimizing high-frequency satellite-to-ground communications using computer vision (CV) technology, like millimeter-wave (mmWave) satellite communication systems. The system uniquely combines CV-based target localization with adaptive beamforming and power control to optimize communication links with ground targets such as base stations, ships, and aircraft. This approach significantly outperforms traditional radio frequency-based methods in accuracy and efficiency, particularly in dynamic mmWave scenarios. Simulation results confirm the superiority of our system in terms of sum rate and energy efficiency, demonstrating its potential to revolutionize high-frequency satellite communications by providing reliable, high-quality service to terrestrial targets. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.
Original language | English |
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Pages (from-to) | 1 |
Number of pages | 1 |
Journal | IEEE Internet of Things Journal |
DOIs | |
Publication status | Accepted/In press - 2024 |
Keywords
- Array signal processing
- Location awareness
- Millimeter wave communication
- Satellites
- Time division multiple access
- Vectors
- Wireless communication
- computer vision
- deep learning
- satellite wireless communication
- wireless model optimization