Cognitive radio networks (CRN) have been tipped as one of the most promising paradigms for next
generation wireless communication, due primarily to its huge promise of mitigating the spectrum
scarcity challenge. To help achieve this promise, CRN develop mechanisms that permit spectrum
spaces to be allocated to, and used by more than one user, either simultaneously or opportunistically,
under certain preconditions. However, because of various limitations associated with CRN, spectrum
and other resources available for use in CRN are usually very scarce. Developing appropriate models
that can efficiently utilise the scarce resources in a manner that is fair, among its numerous and diverse
users, is required in order to achieve the utmost for CRN. 'Resource allocation (RA) in CRN' describes
how such models can be developed and analysed.
In developing appropriate RA models for CRN, factors that can limit the realisation of optimal solutions
have to be identified and addressed; otherwise, the promised improvement in spectrum/resource
utilisation would be seriously undermined. In this thesis, by a careful examination of relevant literature,
the most critical limitations to RA optimisation in CRN are identified and studied, and appropriate
solution models that address such limitations are investigated and proffered.
One such problem, identified as a potential limitation to achieving optimality in its RA solutions, is the
problem of heterogeneity in CRN. Although it is indeed the more realistic consideration, introducing
heterogeneity into RA in CRN exacerbates the complex nature of RA problems. In the study, three
broad classifications of heterogeneity, applicable to CRN, are identified; heterogeneous networks,
channels and users. RA models that incorporate these heterogeneous considerations are then developed
and analysed. By studying their structures, the complex RA problems are smartly reformulated as
integer linear programming problems and solved using classical optimisation. This smart move makes
it possible to achieve optimality in the RA solutions for heterogeneous CRN.
Another serious limitation to achieving optimality in RA for CRN is the strictness in the level of
permissible interference to the primary users (PUs) due to the activities of the secondary users (SUs).
To mitigate this problem, the concept of cooperative diversity is investigated and employed. In
the cooperative model, the SUs, by assisting each other in relaying their data, reduce their level of
interference to PUs significantly, thus achieving greater results in the RA solutions. Furthermore,
an iterative-based heuristic is developed that solves the RA optimisation problem timeously and
efficiently, thereby minimising network complexity. Although results obtained from the heuristic are
only suboptimal, the gains in terms of reduction in computations and time make the idea worthwhile,
especially when considering large networks.
The final problem identified and addressed is the limiting effect of long waiting time (delay) on the
RA and overall productivity of CRN. To address this problem, queueing theory is investigated and
employed. The queueing model developed and analysed helps to improve both the blocking probability
as well as the system throughput, thus achieving significant improvement in the RA solutions for
Since RA is an essential pivot on which the CRN's productivity revolves, this thesis, by providing
viable solutions to the most debilitating problems in RA for CRN, stands out as an indispensable
contribution to helping CRN realise its much-proclaimed promises.