Feedback-Driven Interactive Learning in Dynamic Wireless Resource Management for Delay-Sensitive Users

Abstract

In this paper, we study the problem of dynamic resource management for delay-sensitive users over wireless networks. We focus on a decentralized setting, where autonomous users make self-interested decisions to maximize their utility functions as evaluated based on information feedback. In this paper, two types of information feedback are discussed. One is the private information feedback between a transmitter-receiver pair. The other is the public information feedback among users (i.e., different transmitter-receiver pairs). Due to the informationally decentralized nature of the wireless network, a user cannot have complete information about the transmission actions of its interfering neighbors. However, the user can implicitly or explicitly model the transmission strategies of its major interference sources based on the information feedback. In this paper, we provide an interactive learning framework for distributed power control of delay-sensitive users over multicarrier wireless networks. Specifically, the user can adopt corresponding interactive learning schemes to explicitly model the other users' strategies if public information feedback is available or to implicitly model the impact of other users' actions on its utility if only private information is available. Based on these models, the user creates beliefs and is able to strategically adapt its decisions to maximize its utility. We determine the performance upper bounds for the user's utility when learning from private or public information feedback and investigate the cost-performance tradeoffs resulting from the information feedback gathered with different frequencies and from various users. The simulation results show that the proposed adaptive interactive learning approach significantly improves the energy efficiency of delay-sensitive users compared with schemes that perform myopic best response.