Compressed sampling technologies for UAV data backhaul: opportunities and challenges

Abstract

With the widespread application of unmanned aerial vehicle (UAV), the real-time sharing of data between UAV and base station has become an urgent demand in industry. However, the spectrum resources available for UAV data transmission are extremely precious, resulting in rather limited channel bandwidth. This, in turn, motives ones to explore efficient technologies for real-time non-destructive backhaul of drone data under bandwidth-constrained conditions. As a new technology breaking through the classical Nyquist sampling theorem, the compressive sampling (CS) turns out to be a promising solution to the aforementioned problem. By comparing the core parameters of current UAV communication technologies, this paper reveals that the existing standards of communications cannot meet the increasing requirements of UAV data transmission. Subsequently, four representative CS techniques, including compressed sensing, one-bit compressed sampling, phase retrieval and matrix completion, are briefly reviewed. Then, the simulations of compressed sensing and matrix completion technologies with real-world data are carried out to demonstrate the effectiveness which reveals that compressed sensing and matrix completion methods are able to significantly reduce the transmission time of data backhaul without changing the bandwidth. Ultimately, this paper also describes four research and development directions in the field of UAV data compression and recovery.