Utility Maximization for Delay Constrained QoS in Wireless

TL;DR

This paper presents a novel utility maximization framework for delay-constrained QoS in wireless networks, using a game-theoretic approach and an efficient scheduling policy to optimize client utilities while ensuring fairness.

Contribution

It introduces a new method to allocate delivery ratios as tunable parameters, decomposes the problem into simpler subproblems, and proves the equilibrium maximizes total utility.

Findings

The proposed policy achieves higher utility than existing policies.

The equilibrium in the bidding game maximizes total utility.

The scheduling policy approaches optimal utility and maintains fairness.

Abstract

This paper studies the problem of utility maximization for clients with delay based QoS requirements in wireless networks. We adopt a model used in a previous work that characterizes the QoS requirements of clients by their delay constraints, channel reliabilities, and delivery ratio requirements. In this work, we assume that the utility of a client is a function of the delivery ratio it obtains. We treat the delivery ratio for a client as a tunable parameter by the access point (AP), instead of a given value as in the previous work. We then study how the AP should assign delivery ratios to clients so that the total utility of all clients is maximized. We apply the techniques introduced in two previous papers to decompose the utility maximization problem into two simpler problems, a CLIENT problem and an ACCESS-POINT problem. We show that this decomposition actually describes a bidding game, where clients bid for the service time from the AP. We prove that although all clients behave selfishly in this game, the resulting equilibrium point of the game maximizes the total utility. In addition, we also establish an efficient scheduling policy for the AP to reach the optimal point of the ACCESS-POINT problem. We prove that the policy not only approaches the optimal point but also achieves some forms of fairness among clients. Finally, simulation results show that our proposed policy does achieve higher utility than all other compared policies.