Stochastic resource allocation over fading multiple access and broadcast channels

Authors:
Xin Wang;Na Gao
Affiliations:
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL;Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL
Venue:
IEEE Transactions on Information Theory
Year:
2010

Citing 12
Cited 0

Adaptive signal processing algorithms: stability and performance

Adaptive signal processing algorithms: stability and performance
A framework for opportunistic scheduling in wireless networks

Computer Networks: The International Journal of Computer and Telecommunications Networking
Convex Optimization

Convex Optimization
On the Asymptotic Optimality of the Gradient Scheduling Algorithm for Multiuser Throughput Allocation

Operations Research
Maximizing Queueing Network Utility Subject to Stability: Greedy Primal-Dual Algorithm

Queueing Systems: Theory and Applications
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Convergence of proportional-fair sharing algorithms under general conditions

IEEE Transactions on Wireless Communications
Multiaccess fading channels. I. Polymatroid structure, optimal resource allocation and throughput capacities

IEEE Transactions on Information Theory
Capacity and optimal resource allocation for fading broadcast channels .I. Ergodic capacity

IEEE Transactions on Information Theory
On the duality of Gaussian multiple-access and broadcast channels

IEEE Transactions on Information Theory
CDMA/HDR: a bandwidth efficient high speed wireless data service for nomadic users

IEEE Communications Magazine
Resource Allocation for Wireless Multiuser OFDM Networks

IEEE Transactions on Information Theory

Quantified Score

Hi-index	754.84

Visualization

Abstract

We consider the optimal rate and power allocation that maximizes a general utility function of average user rates in a fading multiple-access or broadcast channel. By exploiting the greedy structure of the capacity-achieving resource allocation for both multiple-access and broadcast channels, it is established that a utility-maximizing allocation policy can be obtained through dualbased gradient descent iterations with fast convergence and low complexity per iteration. Relying on stochastic averaging tools, we further develop a class of stochastic gradient iterations which are capable of asymptotically converging to the optimal benchmark with guarantees on the minimum average user rates, even when the fading channel distribution is unknown a priori.