Distributed Resource Allocation and Decision Making under Uncertainty: A Cooperation Perspective

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Device-to-device (D2D) communications underlaying a cellular infrastructure is one of the key technology enablers for future wireless networks. The basic idea consists in enabling suitably-selected nearby device pairs to reuse the cellular spectrum for direct data transfer, while ensuring that there is no detrimental impact on traditional cellular transmissions via base stations (BS). Despite its great potential for performance gains, D2D communications poses some fundamental challenges to system designers. These challenges, which include resource allocation and transmission mode selection, are exacerbated by the lack of timely and accurate channel state information for direct D2D links at the level of BSs and wireless devices. Therefore, in order to avoid a significant increase in the feedback and signaling overhead, there is a strong need for D2D resource allocation solutions that (i) are amenable to distributed implementation; (ii) are capable of dealing with uncertainty; (iii) can beneficially exploit any available side-information. The core objective of this project is to develop and study a novel theoretical framework for network-assisted D2D resource allocation that incorporates game theory and reinforcement learning. We model a distributed D2D wireless network as a multi-agent system, in which a set of smart agents with bounded rationality share limited resources, by taking actions according to some decision making strategy. Every joint action profile is associated with some reward for each agent, and agents' actions evolve over time as a function of past outcomes and (possibly) observed side-information. In a general multi-agent system, agents behave either competitively or cooperatively. In this project, the focus shall be on studying cooperative users' behavior under uncertainty and lack of prior knowledge. Being cooperative requires agents to share the cost of the learning process, for instance by exchanging the information, so that users' incentives and truthfulness play the major roles. Moreover, due to lack of prior knowledge on utility functions, as well as other agents' actions, incentives and types, agents' strategies are subject to change, as a consequence of continuous information acquisition and subsequent learning. Therefore, traditional solutions developed for full-information games are not applicable to games with incomplete-information, and it is imperative to look for new solution concepts. In brief, the objectives are (i) to generalize conventional cooperative game-theoretical models and solution concepts to games with incomplete information, (ii) to generalize single-agent learning models to learning scenarios that include multiple cooperative learning agents, (iii) to investigate the applications of generalized models and developed solutions in solving resource allocation and mode selection problems in D2D wireless networks.