Convex Optimization
Opportunistic fair scheduling for the downlink of IEEE 802.16 wireless metropolitan area networks
QShine '06 Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks
Fairness of traffic controls for inelastic flows in the Internet
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Cross-layer optimization for OFDM wireless networks-part I: theoretical framework
IEEE Transactions on Wireless Communications
Cross-layer optimization for OFDM wireless networks-part II: algorithm development
IEEE Transactions on Wireless Communications
Achievable rates in cognitive radio channels
IEEE Transactions on Information Theory
From theory to practice: an overview of MIMO space-time coded wireless systems
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
Maintaining utility fairness using weighting factors in wireless networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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This paper investigates the non-convexity of utility-based resource allocation problems in orthogonal frequency division multiple access (OFDMA) networks with heterogeneous traffic classes. Efficient transmission in OFDMA networks requires optimal resource allocation to users based on their current channel states. Also, utility-based resource allocation improves the network resource utilization and application level quality of service (QoS) provisioning. However, a major difficulty in using utility-based OFDMA resource allocation schemes is the nonconvexity of corresponding optimization problem. In this paper, a continuous optimization technique is proposed to treat the nonconvexity. The approach is based on a combination of penalty function methods and interior point methods. Numerical results demonstrate that the proposed approach solves the problem within limited time, and the solutions are close to near optimal solutions obtained by the search algorithm.