On performance optimization for multi-carrier MIMO ad hoc networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
IEEE/ACM Transactions on Networking (TON)
IEEE Transactions on Wireless Communications
Control-theoretic, mission-driven, optimization techniques for wireless sensor networks
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
Harnessing Traffic Uncertainties in Wireless Mesh Networks--A Stochastic Optimization Approach
Mobile Networks and Applications
Optimal traffic splitting in multi-hop cognitive radio networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
IEEE Transactions on Signal Processing
Stability of primal-dual gradient dynamics and applications to network optimization
Automatica (Journal of IFAC)
A stochastic multiobjective optimization framework for wireless sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Stochastic approximation with long range dependent and heavy tailed noise
Queueing Systems: Theory and Applications
Convergence rate control for distributed multi-hop wireless mesh networks
Computers and Electrical Engineering
Optimal self-adaptive QoS resource management in interference-affected multicast wireless networks
IEEE/ACM Transactions on Networking (TON)
Optimization Decomposition for Scheduling and System Configuration in Wireless Networks
IEEE/ACM Transactions on Networking (TON)
| Hi-index | 754.84 |
The implementation of distributed network utility maximization (NUM) algorithms hinges heavily on information feedback through message passing among network elements. In practical systems the feedback is often obtained using error-prone measurement mechanisms and suffers from random errors. In this paper, we investigate the impact of noisy feedback on distributed NUM. We first study the distributed NUM algorithms based on the Lagrangian dual method, and focus on the primal-dual (P-D) algorithm, which is a single time-scale algorithm in the sense that the primal and dual parameters are updated simultaneously. Assuming strong duality, we study both cases when the stochastic gradients are unbiased or biased, and develop a general theory on the stochastic stability of the P-D algorithms in the presence of noisy feedback. When the gradient estimators are unbiased, we establish, via a combination of tools in Martingale theory and convex analysis, that the iterates generated by distributed P-D algorithms converge with probability one to the optimal point, under standard technical conditions. In contrast, when the gradient estimators are biased, we show that the iterates converge to a contraction region around the optimal point, provided that the biased terms are asymptotically bounded by a scaled version of the true gradients. We also investigate the rate of convergence for the unbiased case, and find that, in general, the limit process of the interpolated process corresponding to the normalized iterate sequence is a stationary reflected linear diffusion process, not necessarily a Gaussian diffusion process. We apply the above general theory to investigate stability of cross-layer rate control for joint congestion control and random access. Next, we study the impact of noisy feedback on distributed two time-scale NUM algorithms based on primal decomposition. We establish, via the mean ODE method, the convergence of the stochastic two time-scale algorithm under mild conditi- ons, for the cases where the gradient estimators in both time scales are unbiased. Numerical examples are used to illustrate the finding that compared to the single time-scale counterpart, the two time-scale algorithm, although having lower complexity, is less robust to noisy feedback.