Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
Adaptive signal processing algorithms: stability and performance
Adaptive signal processing algorithms: stability and performance
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
Hop-by-hop congestion control over a wireless multi-hop network
IEEE/ACM Transactions on Networking (TON)
Distributed scheduling and resource allocation for cognitive OFDMA radios
Mobile Networks and Applications
Robust Stochastic Approximation Approach to Stochastic Programming
SIAM Journal on Optimization
Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods
SIAM Journal on Optimization
On multicast beamforming for minimum outage
IEEE Transactions on Wireless Communications
Separation principles in wireless networking
IEEE Transactions on Information Theory
Transmit beamforming for physical-layer multicasting
IEEE Transactions on Signal Processing - Part I
Opportunistic power scheduling for dynamic multi-server wireless systems
IEEE Transactions on Wireless Communications
Dynamic power allocation and routing for time-varying wireless networks
IEEE Journal on Selected Areas in Communications
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
Joint congestion control, routing, and MAC for stability and fairness in wireless networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Separation principles in wireless networking
IEEE Transactions on Information Theory
| Hi-index | 35.74 |
Ergodic stochastic optimization (ESO) algorithms are proposed to solve resource allocation problems that involve a random state and where optimality criteria are expressed in terms of long term averages. A policy that observes the state and decides on a resource allocation is proposed and shown to almost surely satisfy problem constraints and optimality criteria. Salient features of ESO algorithms are that they do not require access to the state's probability distribution, that they can handle nonconvex constraints in the resource allocation variables, and that convergence to optimal operating points holds almost surely. The proposed algorithm is applied to determine operating points of an orthogonal frequency division multiplexing broadcast channel that maximize a given rate utility.