Instrumentation & Measurement Magazine 23-9 - 63

in very noisy environments and very low signal power
conditions.

Conclusion

Fig. 5. An example of DVB-T signal acquisition from USRP.

As an example, in Fig. 5 a signal acquisition is reported for
a DVB-T signal. Please note that the reported figure refers to
a full-band (56 MHz) acquisition, just to prove USRP capabilities, but the sensing phase is explicitly performed on the
specified channel. In Fig. 4, channels from 1 to 4 are reported
as an example, but the system can perform sensing anywhere
in the interval 70 MHz to 6 GHz. Results, in terms of Pd and Pfa
[11], are reported in Fig. 6.
In detail, 1,000 tests have been performed for an 8 MHz
user, with Signal to Noise Ratio spanning 30 dB to -20 dB (corresponding to 50 dBm to -100 dBm reported later, considering
an experienced average noise power equal to -80 dBm).
The adopted figure of merit is the Probability of Detection
(Pd), described in (1):
	

Pd , j


 

Ntest
i 1

 ch , j  i 

Ntest

	(1)

where the subscript " j " refers to the channel we are analyzing,
the function χch,j(i) is the indicator function for channel j at the
ith test, meaning that it is equal to 1 when the channel is classified as occupied, 0 otherwise, and Ntest is the total number of
tests, in our case 1,000.
It can be highlighted that Pd is increasing with power and
it achieves 1, i.e., perfect detection, as long as P≥−80dBm. As
expected, such probability also increases with Probability of False Alarm, which is inversely proportional to the
detection threshold level. Further investigations are currently in progress to achieve better performance, especially

Spectrum scarcity will be an increasingly important factor in
future communication paradigms where an increasing range
of spectrum users are crammed into a limited bandwidth.
Better usage of the currently unused spectrum of others will
be vital to achieve the necessary bandwidth and capacity
for delivery of new telecommunications services. Spectrum
measurements can assist in understanding what is actually
happening in terms of local spectrum usage and characteristics, and pervasive spectrum sensing will greatly improve
decisions and opportunities for locally unused spectrum to
be reused as assessed by a spectrum database-or even by
devices themselves in some contexts. The IEEE 802.15.22.3
standard aims to rectify the current fragmented world of proprietary spectrum sensing and measurement systems by
introducing a widely-applicable " Spectrum Characterization
and Occupancy Sensing (SCOS) " standard, thereby inviting
the necessary economy of scale that will bring down the cost
of spectrum sensing and measurement systems, and allow a
far larger number of sensors and their clients to interoperate.
It is noted here that due to phenomena such as hidden and
exposed terminal problems, and the inherently variable nature
of spectrum propagation, spectrum sensing only becomes of
significant value when a large distribution of sensors and their
associated measurements can collaborate/cooperate towards
sensing decisions or the development of, for example, better
propagation loss models.
Work still needs to be done on the IEEE 802.15.22.3 standard before it can be fully adopted and used by the community.
Testing with a practical implementation represents a crucial
stage of development. For this reason, this manuscript has described a preliminary implementation of the standard, where
measurements and communication tasks are accomplished
through programming commercial software defined radios in
accordance with the current draft of IEEE 802.15.22.3. Results
have shown the correct operation of the SCOS and some associated example sensing performances.

References
[1]	 T. Taher, R. Bacchus, K. Zdunek, and D. Roberson, " Long-term
spectral occupancy findings in Chicago, " in Proc. 2011 IEEE Symp.
New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp.
100-107, May 2011.
[2]	 S. Barnes, P. Botha, and B. Maharaj, " Spectral occupation of TV
broadcast bands: measurement and analysis, " Measurement, vol.
93, pp. 272-277, 2016.
[3]	 N. Faruk, O. W. Bello, O. Sowande, S. Onidare, M. Muhammad,
and A. Ayeni, " Large scale spectrum survey in rural and urban
environments within the 50MHz-6GHz bands, " Measurement,
vol. 91, pp. 228-238, 2016.
[4]	 T. Yucek and H. Arslan, " A survey of spectrum sensing
algorithms for cognitive radio applications, " IEEE Commun.

Fig. 6. Performance for DVB-T user detection at different power levels
December 2020	

Surveys Tutorials, vol. 11, no. 1, pp. 116-130, 2009.

IEEE Instrumentation & Measurement Magazine	63



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