Delay-Sensitive Resource Management in Multi-Hop Cognitive Radio Networks

Abstract

Dynamic resource management by the various cognitive nodes fundamentally changes the passive way that wireless nodes are currently adapting their transmission strategies to match available wireless resources, by enabling them to consciously influence the wireless system dynamics based on the gathered information about other network nodes. In this paper, we discuss the main challenges of performing such dynamic resource management by emphasizing the distributed information in the dynamic multi-agent system. Specifically, the decisions on how to adapt the aforementioned resource management at sources and relays need to be performed in an informationally-decentralized manner, as the tolerable delay does not allow propagating information back and forth throughout the multi-hop infrastructure to a centralized decision maker. The term "cognitive" refers in our paper to both the capability of the network nodes to achieving large spectral efficiencies through exploitation and mitigation of channel and interference variability by dynamically using different frequency bands as well as their ability to learn the "environment" (channel conditions and source characteristic) and the actions of competing nodes through the designed information exchange. We propose our dynamic resource management algorithms performed at each network nodes integrated with multi-agent learning that explicitly consider the timeliness and the cost of such information exchange. The results show that our dynamic resource management approach improves the PSNR of multiple video streams by more than 3 dB as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited.