Opportunistic Contention-Based Feedback Protocol for Downlink OFDMA Systems with Mixed Traffic
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Optimal feedback allocation algorithms for multi-user uplink
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Fairness-aware resource allocation in downlink OFDMA systems with partial feedback CSI
APCC'09 Proceedings of the 15th Asia-Pacific conference on Communications
Resource allocation for OFDMA systems with guaranteed outage probabilities
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Optimal resource management in OFDMA wireless networks to support multimedia traffic
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
Limited-rate channel state feedback for multicarrier block fading channels
IEEE Transactions on Information Theory
Optimizing average performance of OFDM systems using limited-rate feedback
IEEE Transactions on Wireless Communications
A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks
Journal of Network and Computer Applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Optimal tone allocation in downlink OFDMA networks is a non-convex NP-hard problem that requires extensive feedback for channel information. In this paper, two constant complexity limited-feedback algorithms are proposed to achieve near-optimal performance. First, using opportunistic feedback, the proposed schemes are shown to reduce feedback overhead by requiring only users likely to be allocated resources to feedback. There are differences between the two proposed schemes for implementation of the feedback protocol. One scheme requires less feedback but is contention-based, while the other scheme is sequential and thus avoids possible collisions leading to slightly higher performance, but needs more feedback. Second, complexity is reduced for resource allocation by solving the optimization problem in a distributed manner, rather than centrally at the base station. As shown both analytically and through numerical results, these distributed algorithms reduce the required feedback overhead significantly, and achieve constant computational complexity with little performance loss compared to the optimal solution.