The emerging interest in cognitive networks, smart grids, and self-organizing networks is motivating heightened research on distributed and collaborative signal processing strategies that enable networks to adapt and respond to information in real-time.
Cognitive networks consist of spatially distributed nodes that are linked together through a connection topology. The nodes are generally isotropic without any particular node taking a central control role. The nodes cooperate with each other through local interactions and adapt their states in response to both local data collected at the nodes and data received from their immediate neighbors. Information arriving at any particular node creates a ripple effect that propagates throughout the network by means of a diffusive process. The diffusion of information results in a form of collective intelligence as evidenced by improved adaptation, learning, tracking, and convergence behavior relative to non-cooperative networks. The edges linking the nodes can be assigned adjustable weights in accordance with the quality of the information that is exchanged over these edges. In this manner, cognitive networks are able to adjust their topologies as well. Distributed processing techniques over such adaptive networks contrast favorably with classical centralized fusion methods; central fusion approaches limit the autonomy of the network and add a critical point of failure due to the presence of a central node.
Interestingly, it has been observed in the social and biological sciences in studies on animal flocking behavior that while each individual agent in an animal colony is not capable of complex behavior, it is the combined coordination among multiple agents that leads to the manifestation of regular patterns of behavior and swarm intelligence. In a similar manner, cognitive networks should benefit from local cooperation among the nodes, and from a closer interaction between the physical and networking layers, in a manner that leads to enhanced performance in terms of improved learning, tracking, robustness, and convergence abilities.
This talk provides an overview of our recent research work on cognitive networks.