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Kenny Barlee, University of Strathclyde (Scotland)
in the FM Radio band using FS-FBMC
Secondary User Access for IoT Applications

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• Background + Motivation
• Transmitter Design
• Results as in paper
• Recent work
Overview

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• Primary User (PU) = licensed radio spectrum user (e.g. cellular, TV,
satellite)
• Secondary User (SU) = unlicensed radio spectrum user
• Dynamic Spectrum Access (DSA) = technique used by SUs to identify and
gain access to available spectrum
• Software Defined Radio (SDR) = radio with a dynamic, software
controlled front end that is a key enabler in DSA
Presentation Key Terms

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• The radio spectrum is a finite (non exhaustive) resource
• High cost barriers associated with obtaining broadcast licenses
• Very competitive market – licenses worth £50billion to the UK economy
• In coming years cities will be full of millions of sensors, all requiring low
datarate connections
• Existing WiFi and free to use ISM bands are congested – another solution
required
Background and Motivation

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TV Broadcast CellularSatellite Aeronautical Nav
• The radio spectrum is underutilized
• Gaps in Primary User (PU) spectrum can be used by the Secondary User
(SU) with the help of Dynamic Spectrum Access (DSA) techniques
Background and Motivation
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
[GAP]
Primary Users (PUs)Secondary Users (SUs)

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• Band from 88 to 108 MHz, 100 individual 200 kHz wide channels
• Often poorly utilized [1,2]
– Research shows in cities with populations around 1m, only 25% of the band is
used, much less in rural areas
• Signals broadcast at these freqs have excellent propagation
characteristics
– Able to diffract around objects such as hills and human-made structures, and can
penetrate through buildings well
• Band is an excellent candidate for smart city IoT communication,
e.g. traffic signal sequencing, smart street lighting etc.
Background and Motivation – FM Band

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Research Aims
• Design a SU radio capable of filling gaps in the FM Band for low data
throughput applications (IoT)
– Radio to identify available channels by itself (then build channel mask, complete
with guardbands)
– Radio to use an adaptive modulation scheme
– Radio must cause minimal interference to FM Station PUs (IMPORTANT!)

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• To mould around PUs, radio requires adaptive, Non Contiguous (NC)
modulation scheme (i.e. can make signals with spectral holes)
• Out Of Band (OOB) leakage (power in disabled channels) must be
minimal in order to protect PU + meet regulator interference rules
• Favourite NC schemes in literature are: [3,4]
– NC-OFDM (normal OFDM, with zeros transmitted on ‘disabled’ subcarriers)
– FBMC (filterbank multicarrier, with zeros transmitted in ‘disabled’ subchannels)
└ Particular FBMC filter designed for DSA = PHYDYAS filter [5]
DSA Radio Design – Modulation Schemes

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• NC-OFDM uses rectangular
pulse shaping, hence has
high OOB leakage [6]
• FBMC uses specially
designed pulse shaping
filters, which minimize
OOB leakage
• FBMC is the more attractive
candidate due to spectral
containment
DSA Radio Design – Modulation Schemes

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• 2 possible FBMC transmitter architectures – Frequency Spread
(FS-FBMC) or Polyphase Network (PPN-FBMC)
• PHYDYAS FBMC requires Offset QAM symbols (FBMC/OQAM)
• FS-FBMC: symbols upsampled by K, filtered, input to IFFT, overlap/sum [7]
DSA Radio Design – Transmitter

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DSA Radio Design – Transmitter
• Design is HDL-ready (samples rather than frames, valid lines, fixed point)
• FBMC parameters – fS =20.48MHz, K =4, M =1024
• x1024 40kHz wide overlapping channels created (10 per FM channel)

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• Research has shown that the Matched Detector is the most reliable
sensing technique (i.e. the official receiver for the type of signal being
sensed, rather than generic Energy Detection)[8]
• Method adopted was to tune to each FM centre freq, FM demodulate,
perform channel classification, then store the results in RAM
• Guard bands are added based on regulator
minimum distance rules
DSA Radio Design – AutoMask

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• The AutoMask module was designed HDL-ready, and is optimised for
FPGA targeting (e.g. with pipelining and polyphase serialized decimation
filters)
• Mask creation is a function of a ‘detection window’
• Only takes 0.64 seconds to generate with a detection window of 2048
samples per FM channel
DSA Radio Design – AutoMask

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• Recordings of the FM Radio spectrum were obtained using a USRP B210
with a standard VHF/UHF omnidirectional antenna
• In central Glasgow, 22 stations were found
• The USRP was uncalibrated; hence samples received by the computer
had relative power levels
• Channel model estimations were used
to make informed adjustments
– Friis free space model
– Perez-Vega Zamanillo/ FCC F(50,50) [9]
Testing SU Interference Levels

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• The FM Band recording was passed through the AutoMask module, and
a mask was generated
• 275 OQAM subchannels (of 1024) were eligible for use (a function of the
chosen guardband size), a total bandwidth of 5.5MHz
• SU signals with various transmit powers were generated using the
PHYDYAS FBMC PHY and an equivalent NC-OFDM PHY
• These SU signals were overlaid on the FM Band recording, to simulate
the RF transmission, and the interference they would cause to the PU
Testing SU Interference Levels

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• Plotting the power spectra of the signals, clear to see that there is
minimal OOB leakage with PHYDYAS FBMC when compared to NC-OFDM
• Each of the known PU FM stations were demodulated in turn, to allow
the interference caused by the SU to be explored
Testing SU Interference Levels

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• Signal to Interference Ratio (SIR) was found for each SU Tx power
• PHYDYAS radio shows 47dB improvement in PU SIR over NC-OFDM
Testing SU Interference Levels – Quantitative
𝑃𝐹𝑀 =
1
𝑁 𝑛−1
𝑁
𝑠 𝐹𝑀 𝑛 2 𝑃𝑆𝑈 =
1
𝑁 𝑛−1
𝑁
𝑠 𝐹𝑀+𝑆𝑈 𝑛 2 − 𝑃𝐹𝑀 𝑆𝐼𝑅 = 10 log10
𝑃𝐹𝑀
𝑃𝑆𝑈
At 4W, PHYDYAS
leakage x88 LOWER
than PU power
At 4W, NC-OFDM
leakage x625 HIGHER
than PU power

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MOS 1 = noise
MOS 5 = perfect audio
• Audio listening tests were then
performed to classify how ‘bad’ the
quality of each station was
• Each station, at each transmit
power, for both PHYDYAS FBMC and
NC-OFDM were evaluated
• Mean Opinion Score was used:
Testing SU Interference Levels – Qualitative

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• Next step was to target the Transmitter + AutoMask to radio hardware
• Rapid prototyping made easy from Simulink with the ‘Zynq Based Radio’
support package for ZynqSDR [10]
DSA Radio Design – ZynqSDR Tx Implementation

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DSA Radio Design – ZynqSDR Tx Implementation

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• Perform tests in the University’s RF shielded anechoic chamber to
investigate how the SU interferes with standard FM Radio receivers
• Find an optimal guard band size (tradeoff between interference and data
throughput)
DSA Radio Design – Next Steps

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• A receiver PHY has been developed,
which is able to infer unknown Tx
masks
• Initial results show that transmitted
data can be recovered correctly
• Fun quirk with the
system – the OQAM
constellation contains
9 clusters of points!
DSA Radio Design – Receiver
Received IQ (FBMC + FM Radio)
After FFT
After PHYDYAS Filter
After Max Effect

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• The spectrum (a finite resource) is often poorly utilized
• In coming years there will be sensors everywhere, requiring low datarate
connections
• While the proposed DSA radio PHY is not ‘mmWave’, it is equally as valid
an access technique for 5G communications
• The idea of DSA is gaining ground in 5G research (e.g. 5G Rural First
project), and it is accepted that it will play a crucial role in enabling
access to the radio spectrum for next gen communications
Conclusions

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• The novel DSA radio PHY developed can enable SU access in the band
traditionally used for FM Radio
• Initial tests suggest the radio can coexist with the PU, causing very little
interference
• The PHYDYAS FBMC radio provides a 47dB improvement in PU
interference over NC-OFDM
• The radio’s ‘smart’ abilities have been demonstrated, in that it can
generate its own channel mask within 0.64 seconds of turn on
Conclusions

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[1] D. Otermat, C. Otero, I. Kostanic, “Analysis of the FM Radio Spectrum for Internet of Things Opportunistic Access Via Cognitive Radio”, in Proc. of WF-IoT’15, Milan,
IT, pp. 166-171, Dec 2015
[2] D. Otermat, C. Otero, I. Kostanic, “Analysis of the FM Radio Spectrum for Secondary Licensing of Low-Power Short-Range Cognitive Internet of Things Devices”, in
IEEE Access, Oct 2016
[3] B. Farhang-Boroujeny, R. Kempter, “Multicarrier communication techniques for spectrum sensing and communication in cognitive radio”, in IEEE Commun. Mag,
vol. 46 no. 4, pp. 80-85, Apr 2008
[4] R. Gerzaguet et al., “The 5G candidate waveform race: a comparison of complexity and performance”, EURASIP Journal on Wireless Commun. and Networking, Jan
2017
[5] M. Bellanger. (2010, Jun). FBMC physical layer: a primer, PHYDYAS. [Online]
Available: http://www.ict-phydyas.org/teamspace/internal-folder/FBMC-Primer_06-2010.pdf
[6] B. Farhang-Boroujeny, “OFDM Versus Filter Bank Multicarrier”, in IEEE Signal Process. Mag., vol. 28 no. 3, pp. 92-112, May 2011
[7] M. Bellanger, “FS-FBMC: a flexible robust scheme for efficient multicarrier broadband wireless access”, IEEE Globecom Workshops, Anaheim, USA, Dec 2012
[8] M. Hoyhtya, “Spectrum Occupancy Measurements: A Survey and Use of Interference Maps”, IEEE Communications Surveys and Tutorials, vol. 18 no. 4, pp. 2386-
2414, April 2016
[9] C. Perez-Vega and J. Zamanillo. (2002, Jun). Path Loss Model for Broadcasting Applications and Outdoor Communications Systems in the VHF and UHF Bands.
[Online].
Available: http://personales.unican.es/perezvr/pdf/FCC%20Model02.pdf
[10] MathWorks. (2018). Zynq SDR Support from Communications System Toolbox. [Online].
Available: https://uk.mathworks.com/hardware-support/zynq-sdr.html
References

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Research co-funded by the MathWorks DCRG Grant on
Dynamic Spectrum Access for 5G Communications 2016-2018