Adaptive Equalizer Resource Configuration for Optical Transceivers
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Solution Overview
Problem
Optical access and aggregation networks face challenges in managing signal spreading due to chromatic dispersion, polarization mode dispersion, and wavelength shifts, leading to inefficient equalization resource usage and high energy consumption, particularly in uncontrolled outdoor environments with varying temperatures and vibrations.
Innovation Solution
A method for configuring equalization resources in transceiver optical devices by obtaining environmental parameters, estimating channel depth from past experience or look-up tables, and selectively activating resources to optimize hardware block allocation, computing resources, and energy consumption, including the use of look-up tables populated with learning data and a predetermined depth margin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If equalization resources are configured based on worst-case channel depth to ensure reliable equalization in all environmental conditions, then equalization reliability is improved, but hardware resource allocation and energy consumption increase
Solution Approach 1:
The patent applies dynamics by making the equalization resource configuration adaptive rather than static. The system continuously monitors environmental parameters (temperature, vibrations) and channel depth, then dynamically adjusts the equalization resources in real-time. This allows the system to use minimal resources when conditions are good while allocating maximum resources only when environmental conditions deteriorate, resolving the contradiction between reliability and energy consumption.
Solution Approach 2:
The patent changes the parameter of equalization resource allocation from a fixed worst-case value to a variable value based on environmental parameters and channel depth measurements. By using lookup tables that map environmental conditions to appropriate channel depth values, and then adjusting equalization resources accordingly, the system optimizes the balance between reliability and energy consumption.
2Reliability
If equalization resources are configured based on worst-case channel depth to ensure reliable equalization in all environmental conditions, then equalization reliability is improved, but hardware block allocation and computing overhead increase
Solution Approach 1:
The system dynamically adjusts hardware block allocation based on measured channel depth and environmental conditions. Instead of always allocating hardware resources for the worst-case scenario, the system scales resource allocation to match actual channel conditions, reducing hardware complexity while maintaining reliability when needed.
Solution Approach 2:
The patent segments the equalization resource allocation into different levels based on channel depth thresholds. The system divides the range of channel depths into segments and allocates appropriate hardware resources for each segment, avoiding the need to provision for the absolute worst case at all times while ensuring sufficient resources are available when conditions require them.
3Reliability
If equalization resources are configured based on worst-case channel depth to ensure reliable equalization in all environmental conditions, then equalization reliability is improved, but latency increases
Solution Approach 1:
The system dynamically adjusts equalization processing depth based on environmental conditions and measured channel depth. When environmental parameters indicate stable conditions and measured channel depth is shallow, the system reduces equalization processing latency. When conditions deteriorate, the system increases processing depth to maintain reliability, thus optimizing the time-reliability tradeoff.
4Productivity
If equalization resources are configured to support high data rates in controlled environments, then data throughput is improved, but sensitivity to environmental variations and signal spreading increases
Solution Approach 1:
The patent implements feedback by continuously monitoring environmental parameters (temperature, vibrations) and measuring actual channel depth, then using this feedback to adjust equalization resources. This closed-loop control allows the system to maintain high data throughput while compensating for environmental variations that cause signal spreading, as the system adapts its equalization capability in real-time based on actual conditions.
Solution Approach 2:
The system changes operational parameters (equalization resource allocation) based on environmental parameters and measured channel characteristics. By adjusting equalization depth according to environmental conditions and actual channel measurements rather than assuming worst-case scenarios, the system can achieve high throughput while managing signal spreading effects in real-time.
Data Source
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AI summary
A method for configuring equalization resources of an equalizer of a transceiver optical device, wherein the equalization resources are used by the equalizer for performing equalization on optical signals received via a channel of an optical line, comprises: obtaining environmental parameters values, wherein the environmental parameters values are values of environmental data about physical parameters or derivatives which have influence on the channel of the optical line; obtaining channel depth estimation from past experience in view of the environmental parameters values; selectively activating the equalization resources, for performing equalization on the optical signals received via a channel of an optical line, according to the obtained channel depth estimation.