Receiver Convergence via Adaptive Gain Stages
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Solution Overview
Problem
High-speed communication systems, such as Gigabit Ethernet and DSL, face challenges in maintaining signal quality due to decreased signal-to-noise ratios, leading to higher error rates and requiring additional error correction overhead, as well as power management issues that cause components to lose convergence and necessitate retraining.
Innovation Solution
The proposed solution involves an architecture that maintains convergence of receiver mechanisms like equalizers and echo cancellers by using gain stages to compensate for changing operating conditions, allowing for seamless operation without the need for retraining, through adaptive gain adjustments and automatic gain control algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If forward error correction techniques are used to decrease error rates, then reliability is improved, but device complexity increases due to additional components and overhead
Solution Approach 1:
The system uses the existing training sequence signals already present in the communication protocol to simultaneously perform receiver convergence and channel estimation, eliminating the need for separate dedicated training sequences. This self-service approach allows the same signal to serve multiple functions, reducing overhead and complexity while maintaining reliability
Solution Approach 2:
The training sequence is designed to serve multiple purposes: it enables receiver convergence (equalizer and echo canceller training) and simultaneously provides channel estimation for error correction. This multi-functionality reduces the need for separate signaling overhead and components, addressing the complexity issue while maintaining error rate performance
2Use of energy by moving object
If power backoff scheme is used to control transmission power levels, then energy consumption is reduced, but convergence stability deteriorates requiring retraining
Solution Approach 1:
The system performs receiver convergence and channel estimation simultaneously during the initial training phase before power backoff is fully implemented. By completing the convergence process while the channel conditions are still stable and known (during training sequence reception), the system prepares the receiver in advance for the upcoming power changes, preventing the need for retraining when power levels change
Solution Approach 2:
The system uses the received training sequence to estimate the channel and adjust receiver parameters (equalizer coefficients, echo canceller settings) based on feedback from the actual received signal quality. This feedback mechanism ensures the receiver is optimally configured before power backoff begins, maintaining stability during power transitions
3Reliability
If retraining is performed under new power conditions, then convergence is restored, but time loss increases due to limited retraining timeframes
Solution Approach 1:
The system maintains continuous channel estimation and receiver convergence by using ongoing training sequences and pilot signals embedded in the data stream. Rather than performing discrete retraining events that interrupt data transmission, the convergence process continues continuously, eliminating gaps and time losses while maintaining reliability under changing power conditions
Data Source
AI summary
In one embodiment, the present invention includes an apparatus having an automatic gain control (AGC) stage to receive an input signal from a communication channel physical medium, a first local gain stage coupled to an output of the AGC stage, an equalizer coupled to an output of the first local gain stage, an echo canceller to receive local data to be transmitted along the communication channel physical medium, and a second local gain stage coupled to an output of the echo canceller. Other embodiments are described and claimed.