Averaged Iterative Water-Filling Algorithm: Robustness and Convergence

The convergence properties of the Iterative water-filling (IWF) based algorithms have been derived in the ideal situation where the transmitters in the network are able to obtain the exact value of the interference plus noise (IPN) experienced at the corresponding receivers in each iteration of the algorithm. However, these algorithms are not robust because they diverge when there is it time-varying estimation error of the IPN, a situation that arises in real communication system. In this correspondence, we propose an algorithm that possesses convergence guarantees in the presence of various forms of such time-varying error. Moreover, we also show by simulation that in scenarios where the interference is strong, the conventional IWF diverges while our proposed algorithm still converges

Joint Distributed Access Point Selection and Power Allocation in Cognitive Radio Networks

Spectrum management has been identified as a crucial step towards enabling the technology of the cognitive radio network (CRN). Most of the current works dealing with spectrum management in the CRN focus on a single task of the problem, e.g., spectrum sensing, spectrum decision, spectrum sharing or spectrum mobility. In this work, we argue that for certain network configurations, jointly performing several tasks of the spectrum management improves the spectrum efficiency. Specifically, we study the uplink resource management problem in a CRN where there exist multiple cognitive users (CUs) and access points (APs), with each AP operates on a set of non-overlapping channels. The CUs, in order to maximize their uplink transmission rates, have to associate to a suitable AP (spectrum decision), and to share the channels belong to this AP with other CUs (spectrum sharing). These tasks are clearly interdependent, and the problem of how they should be carried out efficiently and distributedly is still open in the literature. In this work we formulate this joint spectrum decision and spectrum sharing problem into a non-cooperative game, in which the feasible strategy of a player contains a discrete variable and a continuous vector. The structure of the game is hence very different from most non-cooperative spectrum management game proposed in the literature. We provide characterization of the Nash Equilibrium (NE) of this game, and present a set of novel algorithms that allow the CUs to distributively and efficiently select the suitable AP and share the channels with other CUs. Finally, we study the properties of the proposed algorithms as well as their performance via extensive simulations.Comment: Accepted by Infocom 2011; Infocom 2011, The 30th IEEE International Conference on Computer Communication