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Quantifying Energy and Latency Improvements of FPGA-Based Sensors for Low-Cost Spectrum Monitoring

机译:量化基于FPGA的能量和潜伏改进,用于低成本频谱监测

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There is a recent interest in large-scale RF spectrum monitoring using low-cost crowdsourced spectrum sensors. A major challenge here is improving the latency and energy usage of signal processing algorithms on the sensor. This improves operational cost and effectiveness and also makes the sensors more responsive to the monitoring task. We specifically consider the case of signal detection using such sensors. Typical crowdsourced implementation using a low-cost software radio connected to a Raspberry Pi or a smartphone as host is not energy-efficient and incurs significant latencies. We propose use of field-programmable gate array (FPGA) to improve both metrics for the signal detection task. Our benchmarking shows significant improvements with FPGA platforms relative to using a Raspberry Pi or smartphone, upto a factor of 73 in terms of latency and a factor of 29 in terms of energy usage.
机译:近期使用低成本众群谱传感器对大型RF频谱监测的兴趣。这里的一项重大挑战正在提高传感器上信号处理算法的延迟和能量使用。这提高了运营成本和有效性,并且还使传感器更响应监测任务。我们特别考虑使用这种传感器的信号检测的情况。使用低成本软件无线电的典型众包实现连接到覆盆子PI或智能手机,因为主机不是节能,并引发了显着的延迟。我们建议使用现场可编程门阵列(FPGA)来改善信号检测任务的两个度量。我们的基准测试显示了FPGA平台相对于使用Raspberry PI或智能手机的显着改进,在延迟方面,高度为73,能源使用率为29倍。

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