Channel Equalization via Direct Symbol-Payload Mapping
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Solution Overview
Problem
Conventional channel equalization methods face limitations in reliability, computational complexity, and cost, especially in wireless and optical fiber communication systems, where they struggle to effectively handle impairments in multi-carrier signals and require sophisticated algorithms that are resource-intensive.
Innovation Solution
A method and device for channel equalization that uses a direct association between sampled symbols and payloads, allowing for efficient extraction of payload data from received signals without the need for complex cascaded modules or prior knowledge of the channel model, leveraging a learning model to map sampled symbols directly to payloads, thereby reducing computational complexity and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional channel modeling and compensation methods are used, then channel equalization can be performed, but computational complexity and costs increase significantly
Solution Approach 1:
The patent extracts only the essential mapping relationship between transmitted signals and received signals, discarding the complex intermediate channel modeling process. By directly learning the input-output mapping from training data, the system eliminates the need for sophisticated channel models and cascaded compensation modules, thereby reducing computational complexity while maintaining equalization effectiveness
Solution Approach 2:
The patent uses training data that copies the actual transmission process to learn the channel characteristics indirectly. Instead of explicitly modeling the channel, the system learns from copied examples of transmitted and received signals, enabling it to infer the effective mapping relationship without requiring detailed channel knowledge, thus simplifying the equalization process
2Measurement precision
If accurate channel models are developed to describe the entire communication link, then channel equalization accuracy improves, but implementation costs and computational requirements increase
Solution Approach 1:
The system performs self-learning by automatically extracting the signal-to-payload mapping relationship from training data without requiring external channel model inputs. The neural network adapts to the specific communication link characteristics through self-supervised learning, achieving accurate equalization while avoiding the costs associated with developing and maintaining complex channel models
Solution Approach 2:
The patent changes the approach from explicit channel parameter estimation to implicit mapping learning. By transforming the problem from estimating channel parameters to directly learning the input-output mapping, the system achieves equivalent or superior accuracy with reduced computational requirements and implementation costs
3Reliability
If complex cascaded modules are used for channel modeling and compensation, then equalization performance improves, but device complexity and resource consumption increase
Solution Approach 1:
The patent merges multiple separate functions (channel modeling, parameter estimation, and signal compensation) into a single integrated neural network. This unified structure learns the complete mapping relationship from received signals to transmitted payloads in one process, eliminating the need for cascaded modules and reducing overall system complexity while maintaining equalization performance
Data Source
AI summary
Embodiments of the present disclosure provide a method, device, and computer readable medium for channel equalization. The method comprises receiving, at a first device, a first signal from a second device via a plurality of subcarriers over a communication channel; sampling the first signal to obtain sampled symbols; and generating a second signal based on the obtained sampled symbols using a direct association between sampled symbols and payloads, the second signal indicating a payload of the first signal carried on an effective subcarrier of the plurality of subcarriers. Through the use of the direct association between sampled symbols and payloads, it is possible to achieve channel equalization in a less complicated, more reliable, and cost-effective manner, so as to extract the payload in the received signal.