Abstract: The wavelength supervisory channel is a mechanism for transmitting and receiving control and management information about the wavelength in a multi-wavelength communication system. A plurality of these channels, each being carried on their respective wavelengths can enable a redundant, robust and fault tolerant method of transmitting and receiving the aforementioned control information for the entire communications network comprised of many such optical devices replacing the traditional and more expensive optical supervisory channel or digital wrapper techniques. The invention described below details one embodiment of the wavelength supervisory channel implemented as a sub-carrier modulated orthogonal channel, riding along with the main payload data channel, in an all-electronic, low cost and unobtrusive methodology.

Abstract: An optical transceiver circuit pack in which specialized components, i.e., optical components associated with transmitting and receiving at a first wavelength or first band of wavelengths, are arranged on a first optical transceiver card section constituting a first module, and wherein generic components, i.e., electronic components which are arranged and operable to electrically interact with any first module regardless of its particular wavelength or band of wavelengths, are arranged on a second optical transceiver card section constituting a second module, the respective modules being detachably coupled to one another such that the first module may be replaced with either an identical module (i.e., one being configured to transmit and receive at said first wavelength or first band of wavelengths) or a different module (i.e., one being configured to transmit at a wavelength or band of wavelengths different than said first wavelength or first band of wavelengths.

Abstract: A channel equalizer is formed using a self-learning neural network. During a training period, the neural network is taught the channel response function. The network is then used to equalize distortions introduced into signals by the channel. The neural network may be a Boltzmann Machine type of neural network comprising neurons arranged in an input layer, a hidden layer, and an output layer. The neurons are interconnected by bidirectional symmetric weighted synapses. Each neuron is preferably implemented by an analog integrated circuit. Direct communication between the input and output layers helps in faster channel acquisition. The scheme can very easily be extended to multilevel and multisymbol modulation schemes such as QAM and PSK.

Abstract: A neuron for use in a self-learning neural network comprises a current input node at which a plurality of synaptic input currents are summed using Kirchoff's current law. The summed input currents are normalized using a coarse gain current normalizer. The normalized summed inputs current is then converted to a voltage using a current to voltage converter. This voltage is then amplified by a gain controlled cascode output amplifier. Gain control inputs are provided in the output amplifier so that the neuron can be settled by the Mean Field Approximation. A noise input stage is also connected to the output amplifier so that the neuron can be settled using simulated annealing. The resulting neuron is a variable gain, bi-directional current transimpedance neuron with a controllable noise input.