Bio-inspired Signal Processing

There is strong trend, in current research on communication and sensor networks, to study self-organizing, self-healing systems. This poses great challenges to the research on decentralized systems, but at the same offers great potentials for future developments, especially in view of the current trend towards miniaturized systems. Even if the development of self-organizing systems is probably at the beginning, biological systems offers many examples of self-organization and self-healing. This is as testified, for example, by swarming behaviors, brain activity, and so on. It is then of great interest to derive mathematical models of biological systems and see how they can suggest novel design tools for engineers. Signal processing can play a big role in this cross-fertilization, as it can help to find out manageable mathematical problems, study their behavior and test the performance in the presence of disturbances. The challenge is to establish a cross-fertilization of ideas from biological to artificial systems, as well as to help understanding biological systems as such. In our recent works, inspired by biological models of social foraging swarms, we have formulated the problem of radio resource allocation in cognitive networks as the search for food by a flock of birds flocking in a cooperative manner, but without any centralized control. The interference distribution in the time-frequency plane takes the role of the food spatial distribution: The birds (radio nodes) fly (allocate their resources) over the regions (time-frequency domain) where there is more food (less interference). During the flight, the birds move (choose their time-frequency slots) in a coordinated way, even in the absence of any central control, in order to avoid collisions (conflicts over common radio resources), yet maintaining the swarm cohesion (i.e., avoiding unnecessary spread in the occupancy of the time-frequency plane and enforcing spatial reuse of the channels).

Selected papers:

• P. Di Lorenzo and S. Barbarossa, Swarming Algorithms for Distributed Radio Resource Allocation, IEEE Signal Processing Magazine, May 2013.
• P. Di Lorenzo and S. Barbarossa, A bio-inspired swarming algorithm for decentralized access in cognitive radio, IEEE Transactions on Signal Processing, Dec. 2011.
• P. Di Lorenzo, S. Barbarossa, and Ali H. Sayed, Decentralized resource assignment in cognitive networks based on swarming mechanisms over random graphs, IEEE Transactions on Signal Processing, July 2012.
• P. Di Lorenzo, S. Barbarossa, and Ali H. Sayed, Bio-Inspired Decentralized Radio Access based on Swarming Mechanisms over Adaptive Networks, IEEE Transactions on Signal Processing, June 2013.