This research goes beyond the physical layer in defining and analyzing cooperative techniques for wireless networks. By incorporating higher layer properties such as traffic dynamics and access control, the investigators develop a new theoretical framework for analyzing and designing cooperative networking algorithms across the layers, which includes existing cooperative techniques such as cooperative relaying and network coding. The basis of this research rests on two major points:: first, the realization that cooperative communication at the physical layer cannot be viewed in isolation, since it has implications at the access and network layers, and second, the recognition . that cooperation at the higher layers, in its own right, can significantly impact overall network performance.
This project has three inter-related thrusts: The first thrust studies resource allocation policies which stabilize the queues within various classes of cooperative networks, if stability is indeed attainable. The second thrust determines a family of scheduling algorithms which maximize the volume of traffic served by a cooperative network within a finite horizon. Finally, game theoretic models are developed for cooperative networks where nodes in the network are allowed to pursue differing objectives, and come to a distributed agreement on the (locally) optimal operating point for the overall network. This research also considers non-stationary and non-ergodic environments that are more appropriate representations of the wireless channel in a network.
The goal of our work during the course of this effort has been to build a strong bridge between the physical and network layers in enabling cooperative communication/networking, particularly in a wireless network setting (as depicted in the figure attached). Traditionally, the notions of physical layer cooperative communication have remained largely separated from their networking counterparts. There is substantial body of literature on cooperative communication in each domain, but almost none at their intersection. We solve this problem by bringing the understanding of physical layer schemes such as amplify & forward, decode & forward in the networking context. In particular, we show that the physical layer achievable region can be obtained by algorithms at the network layer. Basically, the rate region within which the network is stable (also called the stability region for the network) is the same as the physical layer achievable rate region, using a clever algorithms for adapting your transmission policy based on queue (and channel) state. This result implies that the best performance, jointly from a physical and network layer perpsective, can be obtained if we were to combine resources - algorithmic principles at the higher layers with coding ones at the lower layers. Overall, the impass in analysis between the two domains can be bridged effectively using a combination of tools and principles.
