Adaptive Gradient-Based Methods for Adaptive Power Allocation in OFDM-Based Cognitive Radio Networks

Pao, Wei-Chen; Chen, Yung‐Fang

doi:10.1109/tvt.2013.2272804

Cited by 28 publications

(15 citation statements)

References 43 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%

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%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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“…For a given interference information, the PA problem is proved to be concave but not easy to tackle. Similar PA problems were studied before such as [9], [13], [14], [15], [16], and [17]. In finding the dual variables, the authors of these references used the subgradient method, which is very sensitive to step size selection and needs a large number of iterations for convergence.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Power Allocation and Load Balancing for Hybrid VLC/RF Networks

Obeed

Salhab

Zummo

et al. 2018

J. Opt. Commun. Netw.

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“…The optimal resource allocation strategies have been well studied in CR with the spectrum sharing paradigm [9][10][11][12][13][14][15][16][17][18]. The designs of the optimal resource allocation strategies for CR networks combining different transmit power constraints and different interference power constraints were proposed in [9,10], and those designs were extended to CR with imperfect CSI in [12].…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the OP of a SU in spectrum sharing CR was minimized based on an optimal transmit power allocation scheme. Recently, adaptive gradient-based methods for power allocation in OFDMbased spectrum sharing CR have been proposed in [16]. Under both peak/average transmit power (PTP/ATP) and PIP/AIP constraints, optimal power control strategies that maximize the rates of CR with arbitrary input distributions were studied in [17].…”

Section: Introductionmentioning

confidence: 99%

“…In [27], the AIP constraint was regarded as the metric of the protection of the PU. Similar to the optimal power allocation strategies for conventional CR proposed in [9][10][11][12][13][14][15][16][17][18], which use the AIP constraint as the metric of the protection of the PU, the proposed optimal power allocation strategies for green CR also need to compute a complex nonnegative dual variable related to the AIP constraint. The computation of the nonnegative dual variable is extensively complex when there are several nonnegative dual variables required to simultaneously compute or when the probability distributions of the fading channels involved are complex.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

Zhou

Jiang

2018

Wireless Communications and Mobile Computing

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Green cognitive radios are promising in future wireless communications due to high energy efficiency. Energy efficiency maximization problems are formulated in delay-insensitive green cognitive radio and delay-sensitive green cognitive radio. The optimal resource allocation strategies for delay-insensitive green cognitive radio and delay-sensitive green cognitive radio are designed to maximize the energy efficiency of the secondary user. The peak interference power and the average/peak transmit power constraints are considered. Two algorithms based on the proposed resource allocation strategies are proposed to solve the formulated problems. Simulation results show that the maximum energy efficiency of the secondary user achieved under the average transmit power constraint is higher than that achieved under the peak transmit power constraint. It is shown that the design of green cognitive radio should take the tradeoff between its complexity and its achievable maximum energy efficiency into consideration. Related Works and Motivation.Since the operation of a CR should protect the quality of service (QoS) of the PU, a metric that evaluates the performance of the protection of the PU should be imposed. Basically, there are three metrics for protecting the PU from intolerance interference caused by the SU, namely, a peak interference power constraint (PIP), an average interference power (AIP) constraint, and an outage probability (OP) constraint [9][10][11][12][13]. A PIP constraint requires that the instantaneous interference power caused by the SU is below a prescribed interference power threshold, whereas the AIP constraint regulates the long-term interference power over all fading states to be not more than the maximum

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Adaptive Gradient-Based Methods for Adaptive Power Allocation in OFDM-Based Cognitive Radio Networks

Cited by 28 publications

References 43 publications

Solving resource allocation problems in cognitive radio networks: a survey

Solving resource allocation problems in cognitive radio networks: a survey

Joint Optimization of Power Allocation and Load Balancing for Hybrid VLC/RF Networks

Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

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