Method and system for blind learning of volterra equalizer parameters
AssigneeCiena Corporation
InventorsRamin Babaee, Shahab Oveis Gharan
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Abstract
Aspects of the subject disclosure may include, for example, conducting blind learning of correction parameters for non-linear (NL) distortion associated with one or more components of a system, resulting in learned correction parameters, wherein the conducting is performed without a need to identify or estimate a reference input associated with the one or more components, and causing the learned correction parameters to be applied to an output signal associated with the one or more components to compensate for the NL distortion. Other embodiments are disclosed.
Claims20
A coherent optical receiver, comprising:
- one or more components configured to receive or perform processing relating to optical signals;
- and
- a correction system configured to perform operations that include
- conducting blind learning of correction parameters for non-linear (NL) distortion associated with the one or more components, resulting in learned correction parameters, wherein the conducting is performed without a need to identify or estimate a reference input associated with the one or more components, wherein the conducting involves modeling of the NL distortion using a 3rd-order Volterra series-based triplet model, and wherein the learned correction parameters comprise gain factors associated with triplet terms of the triplet model, and
- causing the learned correction parameters to be applied to an output signal associated with the one or more components to compensate for the NL distortion.
A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
- conducting blind learning of correction parameters for non-linear (NL) distortion associated with one or more components of a system, resulting in learned correction parameters, wherein the conducting is performed without a need to identify or estimate a reference input associated with the one or more components, wherein the conducting involves modeling of the NL distortion using a 3rd-order Volterra series-based triplet model, and wherein the learned correction parameters comprise gain factors associated with triplet terms of the triplet model;
- and
- causing the learned correction parameters to be applied to an output signal associated with the one or more components to compensate for the NL distortion.
A method, comprising:
- conducting, by at least one processor, blind learning of correction parameters for non-linear (NL) distortion associated with one or more components of a system, resulting in learned correction parameters, wherein the conducting is performed without a need to identify or estimate a reference input associated with the one or more components, wherein the conducting involves modeling of the NL distortion using a 3rd-order Volterra series-based triplet model, and wherein the conducting is performed in a calibration mode in which particular triplet terms of the triplet model that are of determined dominance are unknown;
- and
- causing, by the at least one processor, the learned correction parameters to be applied to an output signal associated with the one or more components to compensate for the NL distortion.
Dependencies are inferred from the claim text.