Simulation reveals compressed sensing enhances channel reconstruction accuracy in LEO satellite-UAV networks, indicating improved communication throughput.
The combination of low Earth orbit (LEO) satellite constellations and unmanned aerial vehicle (UAV) fleets represents a revolutionary structure for future communication systems, providing widespread connectivity and improved operational adaptability. Nevertheless, the considerable mobility of LEO satellites brings about notable Doppler shifts, and the air‐to‐ground communication channel is naturally limited, posing a crucial obstacle in accurately and effectively estimating the channel. This study explores a centralized cooperative compressed sensing (CS) framework in order to effectively tackle this particular challenge. We conduct a simulation of a network in which multiple UAVs work together to sense a sparsely populated communication channel from a LEO satellite. Every UAV obtains a limited set of compressed measurements and transmits them to a central controller. This controller consolidates the measurements and utilizes the orthogonal matching pursuit (OMP) algorithm to simultaneously reconstruct the high‐dimensional channel impulse response. The simulation carried out in the MATLAB software models a practical scenario involving cooperative UAVs and noncooperative UAVs, a channel of specific length with sparsity, and includes the Doppler effect arising from a LEO satellite communicating in the S‐band. The findings illustrate the effectiveness of the collaborative method in attaining a precise channel reconstruction with a low normalized mean squared error (NMSE). The examination validates that through combining measurements taken at a rate below the Nyquist limit, the network can effectively address the limited sampling capabilities of each UAV. This results in a resilient and precise reconstruction of the sparse channel structure. Communication throughput in dynamic systems involving LEO and UAV technology. This study confirms the capacity of collaborative CS as a fundamental technology enabling efficient, high‐capacity communication in dynamic LEO‐UAV systems.
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Ahmed Moumena (2025) studied this question.
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