Quality-Of-Service Provisioning in Decentralized Networks: A Satisfaction Equilibrium Approach

TL;DR

This paper proposes a satisfaction equilibrium framework for QoS provisioning in decentralized networks, enabling devices to meet performance constraints efficiently and providing a learning method for achieving equilibrium with minimal feedback.

Contribution

It introduces the satisfaction equilibrium concept for QoS in decentralized networks, formalizes conditions for existence and uniqueness, and presents a low-feedback learning algorithm.

Findings

Satisfaction equilibrium exists under certain conditions.

Efficient satisfaction equilibrium minimizes effort for satisfaction.

A learning method achieves SE with one-bit feedback.

Abstract

This paper introduces a particular game formulation and its corresponding notion of equilibrium, namely the satisfaction form (SF) and the satisfaction equilibrium (SE). A game in SF models the case where players are uniquely interested in the satisfaction of some individual performance constraints, instead of individual performance optimization. Under this formulation, the notion of equilibrium corresponds to the situation where all players can simultaneously satisfy their individual constraints. The notion of SE, models the problem of QoS provisioning in decentralized self-configuring networks. Here, radio devices are satisfied if they are able to provide the requested QoS. Within this framework, the concept of SE is formalized for both pure and mixed strategies considering finite sets of players and actions. In both cases, sufficient conditions for the existence and uniqueness of the SE are presented. When multiple SE exist, we introduce the idea of effort or cost of satisfaction and we propose a refinement of the SE, namely the efficient SE (ESE). At the ESE, all players adopt the action which requires the lowest effort for satisfaction. A learning method that allows radio devices to achieve a SE in pure strategies in finite time and requiring only one-bit feedback is also presented. Finally, a power control game in the interference channel is used to highlight the advantages of modeling QoS problems following the notion of SE rather than other equilibrium concepts, e.g., generalized Nash equilibrium.