MIMO Channel Estimation and Evaluation for Airborne Traffic Surveillance in Cellular Networks
A channel estimation (CE) procedure based on compressed sensing is proposed to estimate the multiple-input multiple-output sparse channel for traffic data transmission from drones to ground stations. The proposed procedure consists of an offline phase and a real-time phase. In the offline phase, a pilot arrangement method, which considers the interblock and block mutual coherence simultaneously, is proposed. The real-time phase contains three steps. At the first step, it obtains the priori estimate of the channel by block orthogonal matching pursuit; afterward, it utilizes that estimated channel to calculate the linear minimum mean square error of the received pilots. Finally, the block compressive sampling matching pursuit utilizes the enhanced received pilots to estimate the channel more accurately. The performance of the CE procedure is evaluated by simulating the transmission of traffic data through the communication channel and evaluating its fidelity for car detection after demodulation. Simulation results indicate that the proposed CE technique enhances the performance of the car detection in a traffic image considerably. (C) 2018 Society of Photo- Optical Instrumentation Engineers (SPIE).
Channel estimation; Compressed sensing; Orthogonal frequency-division Multiplexing; Multiple-input multiple-output; Group sparsity; Linear minimum mean square error
Electrical and Computer Engineering
MIMO Channel Estimation and Evaluation for Airborne Traffic Surveillance in Cellular Networks.
Journal of Applied Remote Sensing, 12(1),