Resource allocation for heterogeneous cognitive radio networks

Awoyemi, Babatunde Seun; Maharaj, B. T.; Alfa, Attahiru Sule

doi:10.1109/wcnc.2015.7127734

Cited by 22 publications

(13 citation statements)

References 9 publications

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“…Figure shows the interference channel gain patterns for the various PUs and the consequent bit allocation to each SU when only direct communication is considered. These results are similar to and validated by the ones obtained in two studies . The bit allocation is done with careful consideration of the interference gains to the PUs.…”

Section: Resultssupporting

confidence: 85%

“…The choice of the number of PUs, SUs, and other parameters used in the simulation is informed by the need to compare results obtained with similar works in the literature. Hence, to validate the results presented in this section, comparative results of the works of Awoyemi et al and Rahulamathavan et al have been reproduced, albeit at a heterogeneous scale (involving more SUs in 2 different categories). The other results simply build on the validated ones presented.…”

Section: Resultssupporting

confidence: 68%

“…In some earlier works of the authors of this paper, it has been shown that to achieve an optimal or near‐optimal productivity level for the distribution or allocation of the rather scarce and/or limited resources of the CRN, it is best to allocate low data rates (or none at all) to subchannels where the interference gains to PUs are quite high . This is understandable, as allocating high data rates to such subchannels would imply high transmit power by the SUs and consequently, high interference to the PUs because of the high interference gain.…”

Section: Introductionmentioning

confidence: 92%

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Resource allocation in heterogeneous cooperative cognitive radio networks

Awoyemi

Maharaj

Alfa

2016

Int J Communication

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In cognitive radio networks (CRNs), resources available for use are usually very limited. This is generally because of the tight constraints by which the CRN operate. Of all the constraints, the most critical one is the level of permissible interference to the primary users. Attempts to mitigate the limiting effects of this constraint, thus achieving higher productivity, are a current research focus, and in this work cooperative diversity is investigated as a promising solution. Cooperative diversity has the capability to achieve diversity gain for wireless networks. In the work, therefore, the possibility of and mechanism for achieving greater utility for the CRN when cooperative diversity is incorporated are studied. To accomplish this, a resource allocation model is developed and analyzed for the heterogeneous, cooperative CRN. In the model, during cooperation, a best relay is selected to assist the secondary users that have poor channel conditions. Overall, the cooperation makes it feasible for virtually all the secondary users to improve their transmission rates while still causing minimal harm to the primary users. The results show a marked improvement in the resource allocation performance of the CRN when cooperation is used in contrast to when the CRN operates only by direct communication. KEYWORDScognitive radio networks, cooperative diversity, heterogeneous users, heuristic, linear and nonlinear programming, resource allocation 1 Int J Commun Syst. 2017;30:e3247.wileyonlinelibrary.com/journal/dac

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Section: Resultssupporting

confidence: 85%

Section: Resultssupporting

confidence: 68%

Section: Introductionmentioning

confidence: 92%

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Resource allocation in heterogeneous cooperative cognitive radio networks

Awoyemi

Maharaj

Alfa

2016

Int J Communication

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“…In earlier works of the authors, a comprehensive survey of various approaches that have been investigated and employed in achieving optimal (or nearoptimal) solutions for RA in CRN has been carried out, with their pros and cons highlighted and discussed [5]. Optimal RA solutions for heterogeneous CRN were investigated in [6,7] and the works were further strengthened in [8] to include QoS provisioning. The concept of cooperative diversity was introduced in [9] to address the problem of interference mitigation, thereby achieving even better results.…”

Section: Related Literaturementioning

confidence: 99%

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Awoyemi

Maharaj

Alfa

2017

Wireless Communications and Mobile Computing

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Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA's productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

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“…This is a major and very useful transformation. The idea was used by Awoyemi et al [17] in solving a class of RA problems for heterogeneous CRN. Mitran et al [12] had a pure ILP right from the start.…”

Section: A Linear Objective Functionmentioning

confidence: 99%

Mixed-integer programming based techniques for resource allocation in underlay cognitive radio networks: A survey

et al. 2016

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For about the past decade and a half research efforts into cognitive radio networks (CRNs) have increased dramatically. This is because CRN is recognized as a technology that has the potential to squeeze the most out of the existing spectrum and hence virtually increase the effective capacity of a wireless communication system. The resulting increased capacity is still a limited resource and its optimal allocation is a critical requirement in order to realize its full benefits. Allocating these additional resources to the secondary users (SUs) in a CRN is an extremely challenging task and integer programming based optimization tools have to be employed to achieve the goals which include, among several aspects, increasing SUs throughput without interfering with the activities of primary users (PUs). The theory of the optimization tools that can be used for resource allocations (RA) in CRN have been well established in the literature; convex programming is one of them, in fact the major one. However when it comes to application and implementation, it is noticed that the practical problems do not fit exactly into the format of well established tools and researchers have to apply approximations of different forms to assist in the process. In this survey paper, the optimization tools that have been applied to RA in CRNs are reviewed. In some instances the limitations of techniques used are pointed out and creative tools developed by researchers to solve the problems are identified. Some ideas of tools to be considered by researchers are suggested, and direction for future research in this area in order to improve on the existing tools are presented.

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Resource allocation for heterogeneous cognitive radio networks

Cited by 22 publications

References 9 publications

Resource allocation in heterogeneous cooperative cognitive radio networks

Resource allocation in heterogeneous cooperative cognitive radio networks

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Mixed-integer programming based techniques for resource allocation in underlay cognitive radio networks: A survey

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