S. Benco, F. Crespi, A. Ghittino, A. Perotti, "Software-defined white-space cognitive systems: implementation of the spectrum sensing unit", Proceedings of the 2nd International Workshop of COST Action IC0902 October 5–7 2011, Castelldefels and Barcelona, Spain

1. Software-defined white-space
cognitive systems:
Implementation of the spectrum sensing unit
Castelldefels, October 6th 2011
Sergio Benco,
Floriana Crespi, Andrea Ghittino, Alberto Perotti
Integrated Networks Laboratory (INLAB),
CSP s.c.a r.l. - ICT innovation,
TURIN (ITALY)

2. Outline
 TV White-Spaces Spectrum Sensing
 IEEE 802.22 Spectrum Sensing model
 DVB-T CP autocorrelation Spectrum Sensing
 Threshold calculation
 Performance
 Conclusions and future work
Software-defined white-space cognitive systems 2

3. Spectrum sensing for TV White-Spaces
TV White-Spaces represent the area (space domain) and
the portion of the spectrum (VHF and UHF bands) where
the broadcast signal strength falls below the sensitivity
level of Primary User (PU) receivers
Regulatory bodies are currently discussing about
Secondary User (SU) spectrum sensing requirements in
order to avoid interference to DVB-T receivers
Interference issues can be faced through:
 SU geo-location and PU database queries
 Cognitive Pilot Channel (CPC)
 SU autonomous sensing (cooperative or not)
Software-defined white-space cognitive systems 3

4. IEEE 802.22 Spectrum Sensing model
PU protection contour (Dkm)
Sensitivity range of the PU Rx
PU ITU-R: PRPU = -92dBm @ 132km
(RX) ERP TX = +90dBm, height: 500m, 615MHz)
SU PU
(TX) Keep-out region (Rkm)
Range at wich the Desired/Undesired
SU (D/U) ratio falls below 23 dB
SU spectrum sensing requirements:
●
PU Rx characteristics: F/B = 14 dB; D/U = 23 dB
● At PU Rx: PRSU ≤ PRPU – D/UdB + F/BdB
● At PU Rx: PRSU ≤ -101 dBm At SU Rx: Sens. ≤ -115 dBm
A SU must detect a PU Tx at a range of: Rkm + Dkm
Software-defined white-space cognitive systems 4

5. DVB-T spectrum sensing: CP autocorrelation
Ns Symbol samples
NCP CP samples N0CP N0 d N1CP N1d
Nd Data samples
K Number of symbols
Ns
K −1 i+kN s +N cp −1
CP correlator:
See
R xx (i) = ∑ ∑ x (n) x (n+ N d )
˙
k=0 n=i+kN s
References (1)(2)
CP correlator test:
DVB-T sensing module parameters
max∣R xx (i)∣ Modes 8k (6817 subcarriers)
T CP = i ⩾ γ 2k (1705 subcarriers)
Avg∣R xx (i)∣ < CP lengths 1/4, 1/8, 1/16, 1/32
i∈J Channel bandwidth 8 MHz
Sampling rate 12.5 MS/s (12.5 MHz)
Software-defined white-space cognitive systems 5

6. DVB-T spectrum sensing: applied threshold
False Alarm
max∣R xx (i)∣ Probability (PFA )
i ̂
θ ⩾ γ
T CP = = obtained through
Avg∣R xx (i)∣ ∣R xx∣ <
̄
i∈J Monte-Carlo
simulations over
1000 trials
γ = γ ⋅ Avg∣R xx (i)∣ ⇒
̂ P FA ⩽ 0.1
i ∈J
J =N ∖Q
N ={n∈ℕ : 0 ⩽ n < N s }
̂ ̂
Q={q∈ℕ : θ− N CP ⩽ q < θ+N CP }
The threshold is adaptive w.r.t. the actual average
correlation level plus a fixed margin that depends on PFA
Software-defined white-space cognitive systems 6

7. DVB-T spectrum sensing over K symbols
Symbol synchronization permits to obtain a coherent
combining and average over K subsequent DVB-T
symbols thus achieving a processing gain of about 5 dB
for each 10 dB increase in K
1 symbol 10 symbols 100 symbols
SNR = -15dB SNR = -15dB SNR = -15dB
AWGN channel AWGN channel AWGN channel
Software-defined white-space cognitive systems 7

8. DVB-T OFDM sensing: performance
The detection time Tdet of this real-time module is calculated at the
target sensing performance (PFA=0.1, PD=0.9) for a given SNR:
SNR (PD=0.9) = -17 dB
Symbols = 100
Tdet = 112.00 ms + Tproc
SNR (PD=0.9) = -12 dB
Symbols = 10
Tdet = 11.20 ms + Tproc
Tch move time = 2000 ms
Tsensing = Tch move time – 2Tdet
Software-defined white-space cognitive systems 8

9. Conclusions and future work
● The DVB-T spectrum sensing based on CP autocorrelation
offers a good trade-off between complexity and effectiveness
● The first attempts to exploit TV white space have raised the
problem of high sensitivity requirements for the SU spectrum
sensing unit
● We have developed a real-time module for OFDM spectrum
sensing that approaches the requirements for the IEEE
802.22 WRAN spectrum sensing unit
● Future work will provide a SU network able to continuously
monitor the TV White-Spaces through a CP-based spectrum
sensing module using the GNURadio/USRP2 platform
Software-defined white-space cognitive systems 9

10. References
(1) D. Danev, E. Axell, and E. G. Larsson, “Spectrum Sensing Methods for
Detection of DVB-T Signals in AWGN and Fading Channels”, In Proc.
IEEE PIMRC, pp. 2721-2726, Dec. 2010
(2) V. Gaddam, M. Ghosh, “Robust Sensing of DVB-T Signals”, New
Frontiers in Dynamic Spectrum, 2010 IEEE Symposium on , vol., no.,
pp.1-8, 6-9 April 2010
(3) S. Shellhammer, V. Tawil, G. Chouinard, M. Muterspaugh, M. Ghosh,
“Spectrum sensing simulation model”, IEEE 802.22-06/0028r10, Sept.
2006
(4) C.R. Stevenson, C. Cordeiro, E. Sofer, G. Chouinard, “Functional
Requirements for the 802.22 WRAN Standard”, IEEE 802.22-
05/0007r46, September 2005
Software-defined white-space cognitive systems 10

11. Contacts
Sergio Benco
Consulting Engineer,
Integrated Networks Laboratory (INLAB)
R&D dept.
mail: sergio.benco@csp.it
cell: +39 329 0118356
tel. +39 011-4815164
CSP innovation in ICT
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