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Approaches to Low Power Radio Design

  • Alyssa Apsel
  • Xiao Wang
  • Rajeev Dokania
Chapter
Part of the Analog Circuits and Signal Processing book series (ACSP, volume 124)

Abstract

As discussed in the previous chapter, Low Power Communication is the key to realization of a low power sensor node. Since sensor nodes require only low data rate communication, it might sound intuitively clear that they should also naturally be low power. After all, if a node does less work, it should also consume lesser power. But somehow this doesn’t seem to fit the behavior for low data rate communication, short range radios. If we look at existing radio designs at various data rates and ranges, we see that the design space occupied is very wide. There are designs that operate at very high (~100 Mbps) data rates and designs that operate at low data rates (~100 Kbps). There are designs that take wall supply, consuming a few watts of power, while some work on batteries. If one looks at energy per bit requirements of these designs, while some designs operate at hundreds of nJ/bit, some other designs exist consuming only 0.1 nJ/bit (Fig. 2.1). Now if we plot the range over these designs and examine how they should scale according to the energy/bit requirements we should see radios working at low data rates while consuming few microwatts of power, as required by sensor networks. These designs do not exist. The reasons there aren’t any designs in this space requires us to consider the consequences of scaling traditional design as we go for lower and lower data rate. Since Shannon’s law governs the limit on the energy efficiency of radio communication and link margin vis-à-vis the channel capacity, it makes sense to have a closer look at it and then see what constraints typical radio architectures have that prevent power from scaling in low data rate applications.

Keywords

Sensor Node Spectral Efficiency Leakage Power Gain Stage Impulse Radio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA

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