Convex Optimization
Wireless Communications
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Opportunistic Scheduling of Delay Sensitive Traffic in OFDMA-BasedWireless
WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge
IEEE Transactions on Communications
Low complexity resource allocation algorithm for IEEE 802.16 OFDMA system
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
IEEE Transactions on Mobile Computing
Cross-layer optimization for OFDM wireless networks-part I: theoretical framework
IEEE Transactions on Wireless Communications
Resource Allocation for Delay Differentiated Traffic in Multiuser OFDM Systems
IEEE Transactions on Wireless Communications
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
Transmit power adaptation for multiuser OFDM systems
IEEE Journal on Selected Areas in Communications
Cross-layer QoS Analysis of Opportunistic OFDM-TDMA and OFDMA Networks
IEEE Journal on Selected Areas in Communications
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In Orthogonal Frequency Division Multiple Access (OFDMA) systems, resources, including subcarriers, bits and power, need to be adaptively allocated to users in order to improve spectral efficiency, increase capacity, and reduce power consumption, while satisfying the Quality of Service (QoS) requirements for users. Most of the previous works concentrate on satisfying rate and power requirements, however providing delay requirement is also necessary, especially with increasing demand on delay-sensitive applications. In this paper, we model the resource allocation problem as a cross-layer optimization problem considering the constraints on bit error rate (BER), data rate, total power, as well as delay. We first develop a nonlinear optimization model, which generally requires high computation complexity. To consider a more realistic scenario, we take into account imperfect Channel State Information (CSI) due to estimation errors or channel feedback delay, and incorporate the imperfect CSI into the optimization problem formulation. We then develop the solution through a dual decomposition method. Due to the duality gap between the original and dual optimizations, we convert the non-linear optimization to an equivalent linear formulation so that an exact solution can be obtained. To further reduce the complexity, we develop a heuristic algorithm to provide a solution close to the optimum. Simulation results evaluate the performance of the proposed methods under various network parameters, providing more insights.