Iterative MIMO Receiver Using Stored LLR and Channel Estimates
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Solution Overview
Problem
Current wireless communication systems using iterative detection and decoding in MIMO systems face challenges in reducing power consumption and optimizing reception performance due to repeated operations in the iterative detection and decoding process.
Innovation Solution
The proposed method and apparatus involve generating and storing channel estimated values and Log Likelihood Ratio (LLR) values, with iterative regeneration using feedback information, to optimize the iterative detection and decoding process, thereby reducing unnecessary operations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative detection and decoding is performed multiple times to improve reception performance, then reception reliability is improved, but power consumption increases due to repeated operations
Solution Approach 1:
The channel estimated value is calculated in advance and stored before the iterative detection and decoding process begins. This preliminary action allows the receiver to reuse the same channel estimate across multiple iterations without recalculating it each time, thereby reducing the computational load and power consumption during the iterative process while maintaining reception reliability.
2Measurement precision
If channel estimation is performed in every iteration to maintain accuracy, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The channel estimated value is computed once in advance and stored for reuse during iterative detection and decoding. This preliminary computation approach maintains measurement precision by using an accurate channel estimate while significantly reducing device complexity and processing time by avoiding repeated channel estimation operations in each iteration.
3Reliability
If complete LLR values are recalculated in each iteration to ensure decoding accuracy, then reception performance is improved, but loss of time increases due to redundant calculations
Solution Approach 1:
The Log Likelihood Ratio (LLR) calculation is separated into two parts: a first LLR value that is independent of feedback information and a second LLR value that depends on feedback. The first LLR value is calculated once and stored, while only the second part is recalculated in each iteration. This extraction approach maintains decoding accuracy by preserving the necessary feedback-dependent calculations while eliminating redundant computations, thereby reducing processing time.
Data Source
AI summary
A method of iterative detection and decoding by a receiver and a receiver for iterative detection and decoding. The method includes generating a channel estimated value using a received signal; storing the generated channel estimated value; generating a Log Likelihood Ratio (LLR) value using the received signal and the stored channel estimated value; and generating a decoded bit as feedback information using the LLR value, wherein the LLR value is iteratively regenerated using the generated feedback information, the stored channel estimated value, and the received signal.