Channel Estimation for IRS-based UAV Communications using ADALM PLUTO

Unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRS) present a fascinating combination to improve wireless communications by constantly enhancing signal propagation. Nevertheless, fast UAV mobility and IRS-induced multipath effects make effective channel estimate still a major challenge. This work explores an IRS-based UAV communication system with real-time channel estimate using ADALM PLUTO SDR. Incorporating IRS-induced phase changes, a pilot-assisted estimate framework is constructed to describe the UAV-to-IRS and IRS-to-ground channels. Channel accuracy is improved and noise is reduced by use of a minimal mean squared error (MMSE) estimator. ADALM PLUTO experimental validation affirms enhanced signal quality and resistance to fading. Low-cost, real-time IRS-UAV communication systems have their basis in the proposed framework. AI-driven estimate methods will be investigated in next work to maximize performance.