Optimal resource allocation techniques for MIMO-OFDMA based cognitive radio networks using integer linear programming

Rahulamathavan, Yogachandran; Cumanan, Kanapathippillai; Lambotharan, Sangarapillai

doi:10.1109/spawc.2010.5670885

Cited by 11 publications

(25 citation statements)

References 13 publications

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“…Other classical optimisation tools used for solving well-defined linear or convex programming RA problems are mostly off-shots of either the simplex or the interior point methods. Some of the most common methods and the corresponding references where they have been employed in obtaining solutions to RA problems in CRN are as follows: branch-and-bound (BnB) [19,45], branchand-cut (BnC) [46], lift-and-shift (LnS) [47], iterative and double-loop iterative methods [21,23], dual decomposition [21,48], Lagrangian duality [48,49], barrier method [50,51], gradient decent approach [52], column generation [53,54] etc. In general, these methods, because of their advantage of obtaining optimal solutions, are highly significant.…”

Section: Solutions Using Classical Optimisationmentioning

confidence: 99%

“…The new or reformulated problem is, in most cases, an easier version of the original problem, and such that classical optimisation tools may be employed in arriving at viable solutions. This method has been applied in a number of RA problems in CRN, examples are in references [22,45,56,[60][61][62][63]. In [56], the authors, in an attempt to solve the utility maximisation problem for spectrum sharing in CRN, due to its non-convexity and tight coupling between power and interference, had to reformulate.…”

Section: Solution By Linearisationmentioning

confidence: 99%

“…An important part of the solution was in the reformulation of the heterogeneous base station selection problem to a minimum cost-flow problem, which was then solved as a directional graph with low computational complexity. The works in [45,[61][62][63] have all followed a similar pattern of reformulating RA problems which were originally non-linear, non-convex NP-hard problems into integer linear programming (ILP) problems, and then solving optimally using a BnB optimisation technique. The main advantage of a reformulation approach is that optimal solutions can be obtained to seemingly difficult problems, and usually with much less computational complexity, once the special structure has been found and exploited in achieving the reformulation.…”

Section: Solution By Linearisationmentioning

confidence: 99%

See 2 more Smart Citations

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources at their disposal in meeting the demands of their numerous users and driving their operations. As a result, a considerable amount of research work has been recently dedicated to investigating and developing resource allocation (RA) models that capture the essentials of CRN. The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. To address this problem and bridge the gap, in this paper, a comprehensive study on the prevalent techniques developed for addressing RA problems in CRN is carried out, with an intent to put some structure, relevance and meaning to the various solution approaches. The solution models are therefore grouped and/or classified based on certain outstanding criteria, and their strengths and weaknesses highlighted. Open-ended problems are identified, and suggestions for improving solution models are given. The study therefore gives good directions for further investigations on developing RA solutions in CRN.

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Section: Solutions Using Classical Optimisationmentioning

confidence: 99%

Section: Solution By Linearisationmentioning

confidence: 99%

Section: Solution By Linearisationmentioning

confidence: 99%

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Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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“…Another group of papers that had linear objective functions include ( [18], [19], [20], [21]). The work of Rahulamathavan et al [18] is an extension of their previous work [11] which was on OFDMA to MIMO-OFDMA. In their paper, Guo and Huang [19] considered opportunistic spectrum access in a cell covering environment.…”

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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“…With the fixed c = [c 1 , c 2 , …, c g , …, c G ] substituting (11) into (13) and searching among all users, joint sub-carrier assignment and power allocation can be done simultaneously, according to (14).…”

Section: Subject Tomentioning

confidence: 99%

Opportunistic usage of television white space with respect to the long term evolution‐advanced parameters

Shahraki¹

2015

IET Communications

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Change of transmission from analogue to digital in television (TV) systems has resulted in the formation of considerable spectrum holes in various geographical locations. Cognitive radio (CR) is one of the best structures that can be opportunistically used for optimal exploitation of these spectral sources. In this study, the authors intend to investigate the use of multiple-input multiple-output orthogonal frequency division multiple access-based structure in the TV white spaces bands in the downlink path with respect to long term evolution advanced (LTE-A) parameters. For this purpose, while different spectrum holes in the TV frequency range have been introduced, their limitations in practical applications have fully been discussed. Besides, since the previous power allocation algorithms are not effective enough to be applied in the present scenario, a proper algorithm for maximising the total capacity of the CR system has been introduced. The performance efficiency of the proposed power allocation algorithm has been evaluated and justified through the consideration of the system parameters based on the LTE-A standard.

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Optimal resource allocation techniques for MIMO-OFDMA based cognitive radio networks using integer linear programming

Cited by 11 publications

References 13 publications

Solving resource allocation problems in cognitive radio networks: a survey

Solving resource allocation problems in cognitive radio networks: a survey

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

Opportunistic usage of television white space with respect to the long term evolution‐advanced parameters

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