Interleaved ADC Receiver Equalization for 10 G Multimode Fiber
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Solution Overview
Problem
Implementing 10 Gbps optical communication systems over multi-mode fibers is challenging due to high dispersion and variability, requiring complex and costly components like 10 G ADCs, which are difficult to build and maintain, especially with the need for channel-dependent impairments compensation.
Innovation Solution
A receiver with an interleaved ADC coupled to a multi-channel equalizer, featuring a feedforward equalizer and Viterbi decoder adapted using the LMS algorithm, along with a lookahead pipelined architecture and open-loop residue amplifiers, provides compensation for channel-dependent impairments and maintains parallelism to reduce complexity and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If 10 G ADCs are used to achieve high data rate transmission, then the data rate is improved, but the device complexity and manufacturing difficulty increase significantly
Solution Approach 1:
The receiver is divided into multiple parallel channels operating at lower speeds (e.g., four 2.5 G channels instead of one 10 G channel). Each channel processes a portion of the data stream independently, reducing the speed requirement and complexity of individual components while maintaining the overall 10 G data rate through parallel processing.
Solution Approach 2:
The problem is solved by transitioning from a single high-speed serial channel to multiple parallel channels, effectively adding a spatial dimension to the data transmission. This parallelization approach distributes the processing load and reduces the complexity of each individual channel while achieving the same aggregate data rate.
2Speed
If 10 G ADCs are used to achieve high data rate transmission, then the data rate is improved, but the manufacturing cost increases
Solution Approach 1:
The receiver is divided into multiple parallel channels operating at lower speeds (e.g., four 2.5 G channels instead of one 10 G channel). Each channel processes a portion of the data stream independently, reducing the speed requirement and complexity of individual components while maintaining the overall 10 G data rate through parallel processing.
Solution Approach 2:
The patent employs multiple lower-cost, lower-speed ADC channels instead of a single expensive high-speed ADC. By using multiple cheaper components that operate at reduced speeds, the overall system achieves the required 10 G performance while significantly reducing the manufacturing cost and complexity of individual components.
3Ease of operation
If multi-mode fiber is used for transmission, then the ease of installation is improved, but the signal quality deteriorates due to high dispersion
Solution Approach 1:
The receiver is divided into multiple parallel channels operating at lower speeds (e.g., four 2.5 G channels instead of one 10 G channel). Each channel processes a portion of the data stream independently, reducing the speed requirement and complexity of individual components while maintaining the overall 10 G data rate through parallel processing.
Solution Approach 2:
The patent changes the operating parameters by using multiple lower-speed channels instead of a single high-speed channel. This parameter change allows the system to tolerate the dispersion characteristics of multi-mode fiber while still achieving the required overall data rate, thereby maintaining signal quality despite the fiber's limitations.
Data Source
AI summary
A receiver (e.g., for a 10 G fiber communications link) includes an interleaved ADC coupled to a multi-channel equalizer that can provide different equalization for different ADC channels within the interleaved ADC. That is, the multi-channel equalizer can compensate for channel-dependent impairments. In one approach, the multi-channel equalizer is a feedforward equalizer (FFE) coupled to a Viterbi decorder, for example, a sliding block Viterbi decoder (SBVD); and the FFE and/or the channel estimator for the Viterbi decoder are adapted using the LMS algorithm.


