Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

Adian, Mehdi Ghamari; Aghaeinia, Hassan

doi:10.1007/s11277-013-1486-0

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…The water-filling schemes developed from the idea of the water jug problem. Examples where these schemes have been employed in solving RA problems in CRN can be found in references [66][67][68][69][70][71]. The methods are simple to develop and they give very close-to-optimal solutions with reduced complexities.…”

Section: Solutions By Heuristics or Meta-heuristicsmentioning

confidence: 99%

“…Several game models exist, and some of them have been employed in solving multi-objective RA problems in CRN. Some examples and the corresponding references where they have been applied are cooperative game [70,71], non-cooperative game [82] Nash bargaining (Pareto optimisation) [17,18] and Stackelberg game [83,84].…”

Section: Solutions By Multi-objective Optimisationmentioning

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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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 By Heuristics or Meta-heuristicsmentioning

confidence: 99%

Section: Solutions By Multi-objective Optimisationmentioning

confidence: 99%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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“…The small cells utilize the licensed spectrum of the macro cell through cognitive radio with the overlay style. The authors in [7,8] build an interference model for a hierarchical heterogeneous network and optimize the time frequency resource allocation. The proposed interference model considers the interference of the licensed network to the second-and third-level networks, as well as the interference between the second-and third-level networks.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed interference model considers the interference of the licensed network to the second-and third-level networks, as well as the interference between the second-and third-level networks. The authors in [8,9] aim to maximize the capacity of the cognitive network, considering the interference constraint of the licensed network and transmission power constraint of the cognitive network. The authors in [3] propose a resource allocation scheme for cognitive femtocells considering sub-channel occupation fairness, co-tier and cross-tier interference mitigation, and spectrum-sensing errors.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the power optimization problem demands high computational complexity, and it is also hard to achieve a closed-form solution. What is worse, some researches [7][8][9][10] assume fixed channel gains from transmitters to receivers in spite of their positions. Obviously, this is against the common sense that channel gain varies with transmission distance.…”

Section: Introductionmentioning

confidence: 99%

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Power management in adjacent cognitive femtocells with distance-dependent interference in full coverage area

Zhang

Yang

et al. 2016

J Wireless Com Network

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In cognitive heterogeneous networks, when multiple adjacent femtocells are deployed, uncontrolled transmission power can lead to severe mutual interference. In order to reduce interference while satisfying capacity requirements, we propose a power management scheme for an adjacent femtocell network. Firstly, we build an interference model for adjacent femtocells, in which distance-dependent interference to terminals within the whole femtocell coverage area is considered. Then, we calculate the total capacity of adjacent femtocells based on the mutual interference and formulate a non-convex optimization problem to maximize the capacity under the transmission power constraints. To solve the non-convex problem, we divide it into two convex subproblems and solve them with the Lagrange dual theory and linear programming method. Finally, we derive the closed-form expression of the optimal power configuration to maximize capacity while minimizing energy consumption simultaneously. The simulation results indicate that the proposed power scheme demonstrates obvious improvement in terms of capacity and power economization, compared with the maximal power configuration method.

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Resource allocation in MIMO‐OFDM‐based cooperative cognitive radio networks: optimal and suboptimal low complexity approaches

Adian

Aghaeinia

2014

Int J Communication

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SUMMARYThe problem of resources allocation in multiple-input multiple-output-orthogonal frequency division multiplexing based cooperative cognitive radio networks is considered, in this paper. The cooperation strategy between the secondary users is decode-and-forward (DF) strategy. In order to obtain an optimal subcarrier pairing, relay selection and power allocation in the system, the dual decomposition technique is recruited. The optimal resource allocation is realized under the individual power constraints in source and relays so that the sum rate is maximized while the interference induced to the primary system is kept below a pre-specified interference temperature limit. Moreover, because of the high computational complexity of the optimal approach, a suboptimal algorithm is further proposed. The jointly allocation of the resources in suboptimal algorithm is carried out taking into account the channel qualities, the DF cooperation strategy, the interference induced to the primary system and the individual power budgets. The performance of the different approaches and the impact of the constraint values and deploying multiple antennas at users are discussed through the numerical simulation results.

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Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

Cited by 14 publications

References 14 publications

Solving resource allocation problems in cognitive radio networks: a survey

Solving resource allocation problems in cognitive radio networks: a survey

Power management in adjacent cognitive femtocells with distance-dependent interference in full coverage area

Resource allocation in MIMO‐OFDM‐based cooperative cognitive radio networks: optimal and suboptimal low complexity approaches

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