Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Attar, Alireza; Holland, Oliver; Nakhai, Mohammad Reza; Aghvami, A.H.

doi:10.1049/iet-com:20070355

Cited by 91 publications

(58 citation statements)

References 6 publications

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“…The three power update expressions (13)(14)(15) form the mathematical basis of ELCI. http://jwcn.eurasipjournals.com/content/2012/1/362…”

Section: Mathematical Basesmentioning

confidence: 99%

“…In order to compute (14) and (15), it is first required to evaluate μ k,l and λ m , respectively. We present here a simple and efficient method, optimized for the particular need of ELCI.…”

Section: Solving the Lagrangian Multipliersmentioning

confidence: 99%

“…In particular, the opportunistic approach of the CR paradigm in addition to the interference constraint imposed by the primary system can lead to a low-SINR system with heavy interference among SUs, while some SUs can enjoy a surrounding environment free of PUs and other SUs, such that the entire system is neither in the transmit power-limited or primary interference-limited state (i.e., in a mix state). In contrast, other works, e.g., [2,[11][12][13][14][15] have directly handled the problems without major assumptions. Some of the most popular applied approaches are: the iterative waterfilling (e.g., [16,17]), the sequential quadratic programming (e.g., [14]), the Lagrange duality method (e.g., [18]).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, other works, e.g., [2,[11][12][13][14][15] have directly handled the problems without major assumptions. Some of the most popular applied approaches are: the iterative waterfilling (e.g., [16,17]), the sequential quadratic programming (e.g., [14]), the Lagrange duality method (e.g., [18]). Many other particular iterative algorithms have been also proposed (e.g., [19]).…”

Section: Introductionmentioning

confidence: 99%

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Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

Vijayandran

Byun

Oien

et al. 2012

J Wireless Com Network

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We revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. We consider an interference-limited multi-user multi-channel environment, with a transmit sum-power constraint over all channels as well as an aggregate average interference constraint towards multiple primary users. Until very recently only sub-optimal algorithms were proposed due to the inherent non-convexity of the problem. Yet, the problem at hand has been neglected in the large-scale setting (i.e., number of nodes and channels) as usually encountered in practical scenarios. To tackle this issue, we first propose an exact mathematical adaptation of the well-known successive convex geometric programming with condensation approximations (SCVX) to better cope with large systems while keeping the convergence proof intact. Alternatively, we also propose a novel efficient low-complexity heuristic algorithm, ELCI. ELCI is an iterative approach, where the constraints are handled alternately based on the special property of the optimal solution, with a particular power update formulation based on the KKT conditions of the problem. In order to demonstrate ELCI's efficiency we compare it to two state-of-the-art algorithms, SCVX, and the recently proposed global optimum approach, MARL. The salient highlight of ELCI is the relatively fast and very good sub-optimal performance in large-scale CR systems.

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“…The three power update expressions (13)(14)(15) form the mathematical basis of ELCI. http://jwcn.eurasipjournals.com/content/2012/1/362…”

Section: Mathematical Basesmentioning

confidence: 99%

Section: Solving the Lagrangian Multipliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

Vijayandran

Byun

Oien

et al. 2012

J Wireless Com Network

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“…In [11], several uplink scheduling algorithms which can reduce the SU-to-PU interference were proposed. In [13], a single cell collocated PRN/CRN downlink model was proposed and the weighted sum-rate (WSR) maximization of all PUs and SUs was studied using proportional fair scheduling (PFS) and sequential quadratic programming algorithm. In [14], both centralized and distributed algorithms were designed and the WSR for several SU links was optimized using the Lagrange duality optimization.…”

Section: Introductionmentioning

confidence: 99%

Sum-Throughput Maximization in Downlink OFDMA-based Cellular Cognitive Radio Networks

2017

IJCCIE

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Abstract-We consider a downlink OFDMA-based cellular cognitive radio network (CRN) coexisted with primary radio networks (PRNs). The goal is joint scheduling and power allocation among CRN users to maximize the sum-throughput of the CRN satisfying the interference constraints on the PRN. To this aim, first we propose cognitive single cell multiple access channel (CSC-MAC) algorithm to find search area faster, where the goal is to find a simple, fast and stable method to calculate optimal dual variables in resource allocation for each individual user. Then, utilizing these values, we propose cognitive two cell iterative spectrum balancing (CTC-ISB) algorithm, where by considering interfering effects caused by adjacent cells as well as interference constraints on the primary network, the power and scheduling are allocated among the CRN users so that the sum-throughput of the CRN network is maximized. The proposed algorithm achieves near-optimal performance in practical applications. The proposed method is compared with other algorithms and simulation results are provided to assess its performance. Numerical results show that the proposed algorithm outperforms traditional algorithm in terms of sumthroughput maximization of the total network.

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Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

Wang

Huang

Yuan

et al. 2011

Int J Communication

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SUMMARY In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.

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Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Cited by 91 publications

References 6 publications

Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

Increasing sum-rate in large-scale cognitive radio networks by centralized power and spectrum allocation

Sum-Throughput Maximization in Downlink OFDMA-based Cellular Cognitive Radio Networks

Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

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