Resource allocation is an important issue in wireless communication networks. In
recent decades, cognitive radio technology and cognitive radio-based networks have
obtained more a nd more attention and have been well studied to improve spectrum
utilization and to overco me the problem of spectrum scarcity in future wir eless com-
munication systems. Many new challenges on resource allocation appear in cogni-
tive radio-based networks. In this boo k, we focu s on effective solutions to resource
allocation in several important cognitive radio-based networks, including a cogni-
tive radio-based opportunistic spectrum access network, a cognitive radio- based cen-
tralized network, a cognitive radio-based cellular network, a cognitive radio-based
high-speed vehicle network, and a cognitive radio-based smart grid.
In a cognitive radio-based opportunistic spectrum access network, secondary
users hope to utilize the spectrum hole for their communications. To max imize the
throughput, the secondary user wishes to access the licen sed spectrum when the spec-
trum is detected as idle. However, to protect the pr imary user, it is important to pre-
vent the secondary user from accessing the spectrum even if the spectr um is de te cted
as idle; that is to say, there is a trade-off b etween throughput maximization and pr i-
mary user protection. We will introduce the probabilistic slot allocation scheme to
effectively allocate the transmission slots to the seco ndary user in order to maxi-
mize its throughput when the collision probability p erceived by the primary u ser is
constrained u nder a required threshold.
There are many centralized networks in practice, such as cellular networks and
wireless local area networks. In the cognitive radio-based centralized network, col-
laborative spectrum sensing has also been well studied because the degradation of
sensing performance caused by multi-path fading and shadowing can be effectively
overcome via collaborative sensing. Moreover, the sensing time can a lso be reduced
via collaborative sensing since more sensing data can be obtained simultaneously.
It is challenging to de sig n an algorithm for the fast discovery of available chan-
nels. When the numb er of sensing nodes is large, some nodes may be wasted if all
the sensing nodes collaboratively sense one channel, since the observations from
only a part of the sensing nodes are needed to deter mine the channel state with the

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