Blind Decoding Buffer Management for Wireless Receiver Efficiency
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Solution Overview
Problem
Traditional wireless communication receivers face inefficiencies in blind decoding due to the lack of knowledge about decoding configuration, requiring large buffers to store multiple hypotheses and struggling to quickly identify the correct decoder configuration.
Innovation Solution
The method involves storing decision metrics corresponding to periodic decoding information in a series of combining buffers, where each buffer is associated with different timing information, allowing for efficient decoding by combining and descrambling these metrics based on the periodicity of the signal, thereby reducing the need for extensive buffer storage and speeding up the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional blind decoding techniques are used to search every combination of transmission configuration and timing, then the receiver can identify the correct decoder configuration, but large buffers are required to store decoding information for each hypothesis and the decoding process becomes slow and inefficient
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing only the essential periodic components of decoding information in buffers, rather than storing complete decoding data for every possible hypothesis. The receiver prepares timing information and periodic patterns in advance, then uses these pre-prepared elements to efficiently evaluate multiple hypotheses without requiring large buffers for each scenario.
Solution Approach 2:
The patent uses copying by creating multiple versions of the same buffer structure, where each buffer stores decoding information for a different timing hypothesis. Instead of one large buffer storing all possible hypotheses, the system creates several smaller buffer copies, each dedicated to a specific timing hypothesis. This allows parallel processing of multiple hypotheses while reducing total storage requirements.
2Measurement precision
If traditional blind decoding techniques are used to search every combination of transmission configuration and timing, then the receiver can identify the correct decoder configuration, but the decoding process becomes slow and inefficient
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing only the essential periodic components of decoding information in buffers, rather than storing complete decoding data for every possible hypothesis. The receiver prepares timing information and periodic patterns in advance, then uses these pre-prepared elements to efficiently evaluate multiple hypotheses without requiring large buffers for each scenario.
Solution Approach 2:
The patent applies segmentation by dividing the blind decoding process into distinct stages: (1) extracting periodic decoding information from the received signal, (2) generating timing hypotheses based on the periodicity, (3) evaluating each hypothesis using stored buffer information, and (4) identifying the correct configuration. This segmentation allows the receiver to process hypotheses more efficiently by reusing computed results across multiple evaluation steps.
3Reliability
If large buffers are used to store decoding information for each hypothesis, then complete decoding information is available for every scenario, but the system complexity and resource requirements increase
Solution Approach 1:
The patent applies universality by designing buffer structures that serve multiple functions: they store timing information, hold periodic decoding patterns, and provide reference data for hypothesis evaluation. The same buffer infrastructure supports multiple timing hypotheses and can be reused across different decoding scenarios, reducing overall system complexity while maintaining information completeness.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting buffer size and allocation based on the detected periodicity of the signal and the number of timing hypotheses required. Instead of allocating fixed large buffers for all scenarios, the system adapts buffer parameters to match the actual signal characteristics, reducing memory requirements while maintaining adequate information storage for valid hypotheses.
Data Source
AI summary
Systems and methods for performing efficient blind decoding. A first plurality of decision metrics corresponding to a first repetition of periodic decoding information is stored. The first plurality of decision metrics is grouped into sequential portions. A plurality of combined versions of the sequential portions is stored into combining buffers arranged in sequence. Each combined version is associated with a different sequence of timing information. A first of the plurality of combined versions stored in a first of the combining buffers is combined with a second version of a second plurality of decision metrics that corresponds to a second repetition of the periodic decoding information. The second version is associated with timing information adjacent in the timing information sequence to the timing information associated with the first combined version. The data is decoded based on information in the combining buffers.