The increasing amount of bandwidth requirements and quality of service needs for the next-generation access networks has boosted extensive research in the fiber-optics communication field. In this light, passive optical networks (PONs) combined with optical code division multiple access (OCDMA), provide a potentially cost-effective solution to meet such bandwidth demands. This work proposes an optical transparent architecture which enables all-optical communication between the network nodes. The encoded data streams are multiplexed at a merging point which results in multiple user interference (MUI), thus significantly reducing the network throughput. The networking nodes are able to monitor and record user activity in the PON, and further register the (past) state of activity at the merging point. In this work, we study the coherence of state between the networking nodes and the merging point, for different packet size distributions, in order to predict an optimal transmission instant of each node's data packets. We note that the states are coherent depending on the packet size distribution.

In this paper, theoretical results are presented on the modular construction of orthogonal tree and cascade encoders/decoders of any size for truly asynchronous and cost-efficient spectral amplitude encoded OCDMA on a passive optical network. Three modular building blocks are proposed and described in detail. The tree structure is introduced that enables parallel code processing with a single device. The orthogonality of the code set is evaluated whereafter a revised construction method is proposed. Analysis and simulation confirm that a tree constructed via this method is orthogonal.

An all-optical neural network is presented that is based on coupled lasers. Each laser in the network lases at a distinct wavelength, representing one neuron. The network status is determined by the wavelength of the network's light output. Inputs to the network are in the optical power domain. The nonlinear threshold function required for neural-network operation is achieved optically by interaction between the lasers. The behavior of the coupled lasers is explained by a simple laser model developed in the paper. In particular, the winner take all (WTA) neural-network behavior of a system of many lasers is described. An experimental system is implemented using single mode fiber optic components at wavelengths near 1550 nm. A number of functions are implemented to demonstrate the practicality of the new network. The neural network is particularly robust against input wavelength variations.

We present an all-optical neuron by use of a multimode laser diode that is subjected to external optical feedback and light injection. The shape of the threshold function, that is needed for neural operation, is controlled by adjusting the external feedback level for two longitudinal cavity modes of the laser diode individually. One of the two modes corresponds to the output of the neuron, light injection at the wavelength of this mode provides excitatory input. Light injection in the other mode provides inhibitory input. When light corresponding to two input signals is injected in the same mode, summation of input signals can be achieved. A rate-equation model is used to explain the operating principle theoretically. The proposed injection seeding neuron is built using free-space optics to demonstrate the concept experimentally. The results are in good agreement with the predictions from the rate-equation model. Some experimental results show threshold functions that are preferable from a neural-network point of view. These results agree well with injection locking theory and experiments reported in literature.