State dependent resource allocation is critical for improving the network efficiency. Over the past two decades, remarkable progress has been made on the design of wireless networks with maximum throughput and low latency by using state dependent resource allocation algorithms. However, most of these works assume the network is fully observable and the network state information is perfectly known. This assumption is becoming increasingly questionable because of the multi-carrier technology, which makes it extremely expensive to obtain the complete network state information, and transmission delays and measurement errors, which make it impossible to know the exact network state. With wireless networks become pervasive in our daily life, new theories and algorithms are needed for partially observable wireless networks.
In this project, the PI models a partially observable wireless network as a partially observable Markov process, and then applies the framework of Markov decision processes (MDP). While important structure properties may be discovered using the MDP framework, finding optimal solutions in general is an NP-hard problem. To overcome this difficulty, this project uses drift-based competitive analysis and drift-based large-deviations analysis for quantifying fundamental limits and deriving optimal or near optimal algorithms. The project is expected to lead to breakthroughs in managing partially observable wireless networks. Fundamental limits of partially observable wireless networks, novel resource allocation algorithms with provable throughput and latency guarantees, and implementations on a real-world test bed will have a significant impact on the design and implementation of future wireless networks.
