Spectral Classification

As discussed in Section 2.3.2, a major task in CR is spectrum sensing, which pertains to the assignment of a state to the spectral bands/frequencies regarding their occupancy status. This assignment process is called spectral classification and the signal PSD is the basis for it.

In Section 2.3.2 it was also discussed that if the spectral classification assumes the existence of only two occupancy states, then the SU in a CR network will miss the opportunity to exploit the so-called gray areas of the spectrum. In these areas the signal of PU is present but weak to be of use, rendering the relevant channel useful for a CR SU. However, with the consideration of only two occupancy states, the gray spectral bands are identified as occupied and not as opportunities for the SU, apparently impacting the efficient use of the spectrum available for a CR network.

In the light of the above mentioned pertaining to the existence of the gray spectral bands, a novel spectral classification procedure is proposed, where all three possible

one of the three states, two energy/power thresholds

T

1 and

T

2 must be defined which

respectively determine the existence of either a vacant or occupied frequency. Intermediate values characterize the gray spectral areas.

T

1 is set to the average input

noise level, so at a specific frequency, if the average energy within the corresponding spectral band is lower than

T

1, only noise is present and the frequency is classified as

vacant and available for a SU to access in a CR network.

T

1 level is given by

1 1 0

1

1 0

to ta l S N R

P

T

=

+

(7.8)

Hence it is a function of the signal input SNR and

P

, the latter being the total signal plus noise power over bandwidth

B

. Both SNR and

P

can be measured with conventional analog test equipment.

To set

T

2, as signals of interest are generally frequency sparse, the average signal

power can be very low, much closer to the noise level

T

1 than the average power

within occupied bands, hence it is not a good candidate for determining

T

2. Instead

the root mean square (RMS) value of the signal PSD is used, since it gives values greater than the average power, closer to that of the occupied bands. Actually the sparser the signal is, the lower the RMS value of its PSD. If the average energy within a spectral band is higher than

T

2, then the band is classified as occupied and

unavailable for SU access.

In general the spacing between

T

1 and

T

2 is location-specific. For example, it will

be notably narrower in a building than in an outdoor line-of-sight environment (Haykin, 2007). In this thesis communications in an open environment are assumed.

The next step is to determine the energy within each band. The bandwidth

B

of a sparse signal with known PSD from a CS architectures like the UNI-RD or UNI-CM, is scanned at a resolution

∆f

to determine the average energy within spectral bands of width

b

(the bandwidth per carrier), such that

B=N∆f

and

b=m∆f

, where

m

and

N

are positive integers.

N

is the number of samples which if the signal would have been sampled at the Nyquist rate, while

m

denotes the number of frequency resolutions

within the band. It is assumed the occupancy status assigned to a specific band, applies to all the individual frequencies of width

∆f

within this band, so if a band is occupied, all frequencies within this band are considered occupied and none can be used by a SU as a carrier frequency. The energy within a band having a centre frequency

f

iis given by:

2 1 2

(

)

(

)

m i i i m i

E

f

P f

f

+ − +

=

∑

∆

_(7.9) where ,.., 2 2 m m i= N− .

A frequency classification vector is now formed comprising entries corresponding to the frequencies

f

i, by assigning the value

1

for an occupied frequency,

0

for vacant

and

½

for gray. A reference vector is also introduced, which represents the real occupancy status within

B

, where active carrier frequencies along with these around them and for a width equal to

b

are assigned the value

1

, while all other frequencies are

0

. In the ideal case, the status of the bands is always known so there are no gray frequencies, and they are all either occupied or vacant. The accuracy of the spectral classification process is measured by comparing the frequency classification and reference vectors, using their Euclidean distance. The lower the Euclidean distance between the vectors, the higher the classification accuracy and the relevant performance of a CS structure like the UNI-RD and UNI-CM, which have derived the PSD.

The next section presents a generic CS framework, which encompasses both the UNI-CS and WH-CS models.

In document A Cognitive Radio Compressive Sensing Framework (Page 169-171)