Poster Abstract: Design of a Low-cost Sensor Node forDistributed Spectrum Sensing
Hojun Kim, Makoto Suzuki, Shunsuke Saruwatari,Kousuke Nishimura, Masateru Minami, Hiroyuki Morikawa
Morikawa Laboratory
Research Center for Advanced Science and Technology
The University of Tokyo
4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan{kimhojun, makoto, saru, kosuke, minami, mori}@mlab.t.u-tokyo.ac.jp
Abstract

2

In distributed spectrum sensing, we deploy ten thousandsof sensor nodes, and a low-cost sensor node is necessary. In
this work, we design the sensor node specialized in power
level measurement for distributed spectrum sensing, and we
discuss 3 components of the node, which are frequency
range, RBW and a detector.

We minimize the cost per a sensor node for high-density
and wide area spectrum sensing. A general spectrum analyzer is more than ten thousand dollars on the market, and
it is impractical to deploy many general spectrum analyzers.
Our ultimate goal is to make a 1-chip spectrum sensor, andembeds the chip to various devices, such as a cellular phone.
In order to make the 1-chip spectrum sensor, we have to identify minimum requirements for a low cost sensor node whichcollects the spectrum usage information. It is also important
to know influences of each component on accuracy of thespectrum usage information.
We develop a $100 sensor node specialized in measuring
radio power level for knowing spectrum usage. There aresome devices, which enable us to measure radio power level,such as high-end spectrum analyzers, portable spectrum analyzers, and simple spectrum analyzers. Table 1 shows comparison among the spectrum analyzers and our node. The
purpose of the spectrum analyzer decides the price of thespectrum analyzer. Our node only has one purpose for measuring radio power level. We discuss 3 components required
for measuring radio power level: frequency range, RBW(Resolution Band Width), and a detector by comparing withthe general spectrum analyzers.
We fix frequency range to VHF/UHF bands (30MHz∼3GHz) where spectrum allocation is crowded. Out of30MHz∼3GHz bands, there are some factors which complicate measuring mechanism as compared with its usage rate.
For example, in HF, which is 9kHz∼30MHz, a sensor node
has to prepare a function for spurious and internal noise. InSHF/EHF, which is 3GHz∼300GHz, the sensor node has tomatch impedance between different wiring materials. Moreover, the cost of a local oscillator becomes high to generatehigh frequency.
We fix RBW to 200kHz that lies the bandwidth of 1 channel for the FM broadcasting. 200kHz RBW is selected inconsideration of fast sweep speed, and the cost of components. RBW is a function to separate two adjacent channels to each frequency, and decided by the bandwidth of anintermediate frequency (IF) filter. Sweep time is inversely
proportional to square of RBW. When RBW is too narrow,sweep time becomes too long. High-end spectrum analyzers have IF filters having various bandwidth, and the band-Categories and Subject Descriptors
C.2 [Computer-Communication Networks]: Distributed Systems; C.3 [Special-Purpose and ApplicationBased Systems]: Signal Processing Systems

General Terms
DesignKeywords
Distributed Spectrum Sensing, Cognitive Radio, Sensor
Node1Introduction

Spectrum is inherently a limited resource. The need for
wireless devices forces us to face spectrum shortage, and wehave to effectively use spectrum resources [1]. For the effective spectrum usage, we have to deeply understand and sharethe current spectrum usage information. The sharing spectrum usage information is useful for researchers who study
cognitive radio or spectrum policy. There have been several
wide-band spectrum usage surveys [1][2], but the surveys
only measure spectrum at few points.
We need higher-density spectrum data for the deep understanding of the spectrum usage information. We are developing visualization of radio spectrum with distributed spectrum sensing over wide area. Distributed spectrum sensing
is high-density spectrum usage monitoring with distributed
ten thousands of sensor nodes. Distributed spectrum sensing
has two requirements: a low-cost sensor node and scalable
spectrum information management system with a limited resource.
In this work, we discuss design of construction for a lowcost sensor node. The scalable spectrum information management system can be found elsewhere [3].
Copyright is held by the author/owner(s).
SenSys’09, November 4–6, 2009, Berkeley, CA, USA.
ACM 978-1-60558-748-6

Distributed Spectrum Sensing

Table 1. Comparison of spectrum analyzers.

CostFreq. range
RBWDetector
PurposeHigh-end
spectrum analyzer
FSL6 [4]about $10,000˜
9kHz˜6GHz
300Hz˜10MHz
MAX, MIN,SAMPLE, AVERAGE,RMS, QUASI PEAK
General purpose

SRM-3000 [5]about $15,500
100kHz˜3GHz
1kHz˜5MHz

SpeCat2 [6]about $3,990
100kHz˜3GHz
1kHz˜250kHz

Simplespectrum analyzer
GigaSt [7]about $385
3MHz˜12GHz
15kHz˜180kHz

MAX, RMS

MAX, MIN, RMS

MAX, RMS

RMS

Safety analysis of RFInterference,
power level

Power level,
freq response

Power level

Portable spectrum analyzer

width is from hundreds of Hz to tens of MHz. The high-end
spectrum analyzers switch the IF filters to analyze various
signals. When we measure EMI (Electro Magnetic Interference), C/N (Carrier vs. Noise), and a spurious emission at
low frequency, the spectrum analyzers require narrow RBW,
which is hundreds of Hz. When we analyze the wideband
signal such as wireless LAN, the spectrum analyzers require
wide RBW, which is tens of MHz. On the other hand, since
the purpose of our node is only to measure radio power level,
the node does not need functions except for the purpose. For
example, our node does not need to separate adjacent signals.
Our node uses only RMS as a detector. General spectrum
analyzers have some detectors offering positive/negative
peak, RMS, sample, average values, and so on. The general
spectrum analyzers switch detectors corresponding to various measurement standards. However, our node uses only
one detector for RMS value, which is generally used to judge
the radio spectrum usability.

3Design

From discussion in section 2, we design a spectrum sensor node. Figure 1 shows the block diagram of the designed
sensor node. The sensor node, which is superheterodyne receiver, is composed of an attenuator (ATT), high-pass filters(HPF), low-pass filters (LPF), mixers, local oscillators (LO),band-pass filters (BPF), a detector (DET), and a micro controller (MCU). The attenuator attenuates input power levelto under the maximum signal level where the mixers work.
The mixers up/down-convert the received signal to intermediate frequency (IF) with multistep combination of high-passfilters, low-pass filters, and local oscillators. After the bandpass filter passes IF band, the detector measures signal power
level. The micro controller converts analog IF signals to digital signals. In addition, the micro controller controls not only
switches which decide the number of step for mixer input,
but also the local oscillator which generates required oscillation frequency. The following detaily shows components ofthe sensor node.
(1) Up/down converter: Mini-Circuits AT-20 is used as anattenuator, and TUF is used as a mixer. A PLL circuit is usedfor high stability of local oscillators. The PLL circuit consists of a loop filter, a voltage-controlled oscillator (VCO),
and a frequency synthesizer. Mini-Circuits POS series areused as the VCO, and Fujitsu MB15E05 is used as the frequency synthesizer. Inverting level shift with a lag-lead filter

Mixer
In

Mixer

ATTHPFOurs
Ours< $10030MHz˜3GHz
200kHzMixer

10.7MHz

BPF

LPF

1472~2672MHz

1950MHz

467.3MHzLOLOLODETMCUUp Converter

Down Converter

Figure 1. Block diagram.
is used as the loop filter.
(2) BPF: Murata Manufacturing SFELF10M7 is used.
SFELF10M7 has 10.7MHz center frequency and 200kHz
bandwidth.
(3) Detector: Analog Devices AD8307 is used. AD8307
provides a logarithmic amplifier as well as RMS detection.
(4) Micro controller: Microchip PIC18F2321 is used.

4Conclusions

We have discussed design of a low-cost spectrum sensing
node for distributed spectrum sensing. The sensor node is
specialized in measuring radio power level. We are currently
preparing for deployment of the sensor nodes in wide area.

5Acknowledgments

This work is supported by the Ministry of Internal Affairs
and Communications, Japan.

6References[1] R.B. Bacchus et al. Long-Term, Wide-Band Spectral Monitoring in
Support of Dynamic Spectrum Access Networks at the IIT Spectrum
Observatory. In Proc. of DySPAN, 2008.
[2] M. A. McHenry et al. Chicago Spectrum Occupancy Measurements& Analysis and a Long-term Studies Proposal. In Proc. of Workshop
on TAPAS, 2006.
[3] J. Naganawa et al. Demo Abstract: Radio Information Management
for a Distributed Spectrum Sensing. In Proc. of SenSys, 2009.
[4] R&S, FSL6.
http://www.rohde-schwarz.com/us/products.html.
[5] Narda, SRM-3000.
http://www.narda-sts.com/landingpage.php.
[6] NEC, SpeCat2.
http://www.nec-eng.co.jp/pro/specat/index.html.
[7] GigaSt.
http://www.wa.commufa.jp/gigast/.