On the stability of the Foschini-Miljanic Algorithm with uncertainty over channel gains

Distributed power control in wireless networks faces challenges related to its stability. When perfect information of channel states and transmitting agents are available, previous work has shown that the stability conditions can be known. When there is uncertainty over the parameter space, stability is not well understood. In this work, we study the impact of parameter uncertainty and network structure on the stability and scalability of a well known distributed power control, namely the Foschini-Miljanic algorithm. More specifically, we derive probabilistic conditions with respect to the parameters of the channel distributions for which the system is stable. Furthermore, we study the effects of these parameters for different node distribution on the plane. Numerical examples validate our theoretical results

Power Control with Random Delays: Robust Feedback Averaging

International audienceDistributed power control schemes in wireless networks have been well-examined, but standard methods rarely consider the effect of potentially random delays, which occur in almost every real-world network. We present Robust Feedback Averaging, a novel power control algorithm that is capable of operating in delay-ridden and noisy environments. We prove optimal convergence of this algorithm in the presence of random, time-varying delays, and present numerical simulations that indicate that Robust Feedback Averaging outperforms the ubiquitous Foschini-Miljanic algorithm in several regimes

On the stability of a power control algorithm for wireless networks in the presence of time-varying delays

International audienceThis paper studies the robustness of the wellknown Foschini-Miljanic power control algorithm with respectto time-varying delays. Since delays are omnipresent in wirelessnetworks, this problem is of practical importance. It has beenproven in the past that no matter how large the delays are, theFoschini-Miljanic algorithm still converges. However, this wasbased on the assumption that the delays are constant over time,which is not always met in practice. Therefore, the problemaddressed in this paper is how the algorithm behaves underthe time-varying delays case. Firstly, we provide the conditionsunder which the system is stable by means of a Linear MatrixInequality (LMI) and using a semi-definite optimization solverwe show the validity of our results through illustrative examples