Reconfigurable Baseband Processing for Wireless Communications - Adaptive Signal Processing in Wireless Communications - page 465

F ∈, {} 01 forall k

k

p = 0 forall k whichsatisfy F k =0,

k

where R is the data rate, K is the number of the subcarriers, N 0 is the noise power density,
B is the band of interest for cognitive radio, h k is the subcarrier gain, and p k is the power
allocated to the corresponding subcarrier. F k is the factor indicating the availability of
subcarrier k to cognitive radio, where F k = 1 means the k th carrier can be used by cogni-
tive radio.
The system power minimization can also be applied under the constraint of a con-
stant data rate. We formulate it as follows:

K

∑

MinpP =

k total

k = 1

K

 

2

∑

F
K

hhp
N

    (15. 2)

Subjectto: R =

k

log 1 +

kk
B
K

2

 

0

k = 1

F ∈, {}forall 01 k

k

p = 0 for all k which satisfy F k =0.

k

A functional diagram of the system is presented in Figure 15.16 . A bit allocation vector
indicates how many bits are loaded on each subcarrier. The number of bits corresponds
to the different modulation types used for each subcarrier. The bit allocation vector is
determined by the spectrum occupancy information from spectrum sensing and the
SNR of subchannels. The bit allocation vector is disseminated via a signaling channel so
that both transmitter and receiver have the same information. The bit allocation vector
does not change frequently, for instance, only after several frames. The basic idea is to
load more bits on good subcarriers and zeros on carriers that cause interference to the
licensed user or lead to poor transmissions.

15.3.2.3 Reconfigurable Physical Layer
As already foreseen by Mitola [31], a cognitive radio is the final point of software-defined
radio platform evolution: a fully reconfigurable radio that changes its communication
functions depending on network and user demands. His definition on reconfigurability
is very broad, but we focus on the physical layer reconfigurability. We will discuss some
possibilities for reconfiguration.
The proposed architecture for spectrum sensing is shown in Figure 15.15 , where
cyclostationary feature detection is used as a complementary option. This option has
to be supported by a reconfigurable platform that can efficiently perform the spectrum
correlation. In [35], a two-step methodology to analyze the mapping of cyclostation-
ary feature detection (CFD) onto a Montium-based multicore processing platform is
proposed. In the first step, the tasks to be executed by each core are determined in a