Energy-efficient detection system in time-varying signal and noise power
In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.
Energy-efficient detection, Scheduler, Time-varying, Signal-to-Noise Ratio (SNR), Estimation, Adaptive threshold
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign