A 65 nm CMOS low power RF front-end for L1/E1 GPS/Galileo signals

Utilizing Massive Spatiotemporal Samples for Efficient and Accurate Trajectory Prediction

“…2. The data points in this figure are obtained by varying N cell and N dur from 2 6 to 2 10 . The distortions of both approaches are plotted against log 2 ðN vq Þ, where N vq is the product of N cell and N dur : log 2 ðN vq Þ indicates the number of bits for representing a discrete state in a quantized trajectory.…”

“…Thus, the second factor is the power consumption of mobile devices. Many researches have designed low powered components for mobile devices, for example, in GPS receiver [10], CPU [11], display [12], and networking [13]. Apart from energy saving, there have been breakthroughs in battery technology that improve both charge capacity and recharging time by about 10 times, such as in [14].…”