Interference Alignment with Frequency-Clustering for Efficient Resource Allocation in Cognitive Radio Networks

Abstract : In this paper, the problem of resource allocation in overloaded OFDM based MIMO cognitive radio (CR) system is considered. The objective is to allocate the different subcarrier and distribute the available user power in order to maximize the CR system throughput. The interference induced to the primary system should not be harmful and should not exceeds the prescribed limit. Interference alignment (IA) technique is employed in order to achieve an efficient use of the available radio resources. Without affecting the quality of service of the primary system, IA enables the secondary users to share the available spectrum which increases the CR system degrees-offreedom. However, IA feasibility is an obstacle which limits the number of secondary users that can share a given subcarrier. This resource management problem is formulated as a mixed-integer optimization problem. To reduce the computational complexity of the problem, an efficient suboptimal algorithm is proposed through two phases. IA with frequency-clustering is performed in the first phase to overcome IA feasibility conditions while the power is distributed among subcarriers in the second phase. Simulations show that IA technique achieves a significant sumrate increase of CR systems compared to traditional CR systems that use orthogonal multiple access transmission techniques with a significant reduction of the computational complexity.
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Communication dans un congrès
Globecom'2014, Dec 2014, Austin, Texas, United States. 7 p., 2014
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https://hal-supelec.archives-ouvertes.fr/hal-01073532
Contributeur : Myriam Andrieux <>
Soumis le : vendredi 10 octobre 2014 - 08:37:49
Dernière modification le : mercredi 16 mai 2018 - 11:23:49

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  • HAL Id : hal-01073532, version 1

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Mohammed El-Absi, Musbah Shaat, Faouzi Bader, Thomas Kaiser. Interference Alignment with Frequency-Clustering for Efficient Resource Allocation in Cognitive Radio Networks. Globecom'2014, Dec 2014, Austin, Texas, United States. 7 p., 2014. 〈hal-01073532〉

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