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 3dB as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited.Keywords -dynamic resource management, cognitive radio networks, multi-hop wireless networks, multi-agent learning, delay sensitive applications.

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In this paper, we investigate the problem of multi-user resource management in multi-hop cognitive radio networks for delay-sensitive applications. Since the tolerable delay does not allow propagating global information back and forth throughout the multi-hop network to a centralized decision maker, the source nodes and relays need to adapt their actions (transmission frequency channel and route selections) in a distributed manner, based on local network information. We propose a distributed resource management algorithm that allows network nodes to exchange information and that explicitly considers the delays and cost of exchanging the network information over the multi-hop cognitive radio networks. The term "cognitive" refers in our paper to both the capability of the network nodes to achieve large spectral efficiencies by dynamically exploiting available frequency channels as well as their ability to learn the "environment" (the actions of interfering nodes) based on the designed information exchange.Note that the node competition is due to the mutual interference of neighboring nodes using the same frequency channel. Based on this, we adopt a multi-agent learning approach, adaptive fictitious play, which uses the available interference information. We also discuss the tradeoff between the cost of the required information exchange and the learning efficiency. The results show that our distributed resource management approach improves the PSNR of multiple video streams by more than 3dB as opposed to the state-of-the-art dynamic frequency channel/route selection approaches without learning capability, when the network resources are limited.Index Terms: distributed resource management, cognitive radio networks, multi-hop wireless networks, multi-agent learning, delay sensitive applications.

To cope with the time-varying network conditions, various error-protection and channel adaptation strategies have been proposed at different layers of the protocol stack. However, these cross-layer strategies can be efficiently optimized only if they act on accurate information about the network conditions and hence, are able to timely adapt to network changes. We analyze the impact of such information feedback on the video quality performances of the collaborative multimedia users sharing the same multi-hop wireless infrastructure. Based on the information feedback, we can estimate the risk that packets from different priority and deadline classes will not arrive at their destination before their decoding deadline. Subsequently, cross-layer optimization strategies such as packet scheduling, retransmission (due to transmission error) limit are adapted to jointly consider the estimated risk as well as the impact in terms of distortion of not receiving different priority packets. Our results quantify the risk estimation and its benefit in different network conditions and for various video applications with different delay constraints.

In this paper, we propose a distributed, end-to-end, integrated cross-layer scheme to maximize the decoded video quality of multiple users engaged in simultaneous real-time streaming sessions over a multi-hop wireless network. Our algorithm explicitly considers the distortion impact and delay constraints in assigning priorities to the various packets and then relies on priority queuing to drive the optimization of the various users' transmission strategies across the multi-hop network. The proposed solution is enabled by the scalable coding of the video content and the design of cross-layer optimization strategies including a dynamic routing algorithm, which allow priority-based adaptation to varying channel conditions. Our proposed delay-driven, packet-based transmission is superior in terms of both network scalability and video quality to previous static flow-based solutions based on predetermined paths and rate requirements.