A Genetic Algorithm Based Source Encoding Scheme for Distinguishing Incoming Signals in Large-scale Space-invariant Optical Networks

Free-space optical interconnection networks can be classified into two types, space variant and space invariant, according to the degree of space variance. In terms of physical implementations, the degree of space variance can be interpreted as the degree of sharing beam steering optics among the nodes of a given network. This implies that all nodes in a totally space-invariant network can share a single beam steering optics to realize the given network topology, whereas, in a totally space variant network, each node requires a distinct beam steering optics. However, space invariant networks require mechanisms for distinguishing the origins of incoming signals detected at the node since several signals may arrive at the same time if the node degree of the network is greater than one. This paper presents a signal source encoding scheme for distinguishing incoming signals efficiently, in terms of the number of detectors at each node or the number of unique wavelengths. The proposed scheme is solved by developing a new parallel genetic algorithm called distributed asynchronous genetic algorithm (DAGA). Using the DAGA, we solved signal distinction schemes for various network sizes of several topologies such as hypercube, the mesh, and the de Brujin.