This document discusses the design of a secondary user radio capable of accessing gaps in the FM radio band for Internet of Things applications. It proposes using Filter Bank Multicarrier (FBMC) modulation with the PHYDYAS filter to enable non-contiguous access while minimizing interference to primary FM radio users. The design includes an automatic channel masking module to identify available channels. Simulation results show FBMC provides a 47dB improvement in protecting primary user signal quality over conventional OFDM. The radio design is targeted for implementation on a ZynqSDR hardware platform for further real-world testing.

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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