1/25

Kenny Barlee, University of Strathclyde (Scotland)

in the FM Radio band using FS-FBMC

Secondary User Access for IoT Applications

2/25

• Background + Motivation

• Transmitter Design

• Results as in paper

• Recent work

Overview

3/25

• 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

4/25

• 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

5/25

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

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

[GA

P]

Primary Users (PUs)Secondary Users (SUs)

6/25

• 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

7/25

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!)

8/25

• 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

9/25

• NC-OFDM uses rectangular pulse shaping, hence has high OOB leakage [6]

• FBMC uses specially designed pulse shaping filters, which minimizeOOB leakage

• FBMC is the more attractive candidate due to spectral containment

DSA Radio Design – Modulation Schemes

10/25

• 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

11/25

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)

12/25

• 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 regulatorminimum distance rules

DSA Radio Design – AutoMask

13/25

• 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

14/25

• 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 usedto make informed adjustments

– Friis free space model

– Perez-Vega Zamanillo/ FCC F(50,50) [9]

Testing SU Interference Levels

15/25

• 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

16/25

• 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

17/25

• 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

𝑠𝐹𝑀 𝑛 2 𝑃𝑆𝑈 =1

𝑁

𝑛−1

𝑁

𝑠𝐹𝑀+𝑆𝑈 𝑛 2 − 𝑃𝐹𝑀 𝑆𝐼𝑅 = 10 log10𝑃𝐹𝑀𝑃𝑆𝑈

At 4W, PHYDYAS leakage x88 LOWER

than PU power

At 4W, NC-OFDM leakage x625 HIGHER

than PU power

18/25

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

19/25

• 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

20/25

DSA Radio Design – ZynqSDR Tx Implementation

21/25

• 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

22/25

• A receiver PHY has been developed, which is able to infer unknown Txmasks

• Initial results show that transmitted data can be recovered correctly

• Fun quirk with the system – the OQAMconstellation contains9 clusters of points!

DSA Radio Design – Receiver

Received IQ (FBMC + FM Radio)

After FFT

After PHYDYAS Filter

After Max Effect

23/25

• The spectrum (a finite resource) is often poorly utilized

• In coming years there will be sensors everywhere, requiring low datarateconnections

• 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

24/25

• 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

25/25

[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

26/25

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