Blind Decoding Pruning for Wireless Grant Signaling
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Solution Overview
Problem
In wireless communication systems, blind decoding often generates multiple hypotheses for resource grants, leading to false positives that can adversely impact device performance and overall system efficiency due to the lack of advance knowledge of configuration parameters by devices.
Innovation Solution
Implementing a method that includes a candidate grant identification module to perform blind decoding and a grant discard module to discard invalid grants based on RNTI values and parameter validity checks, reducing false alarm probabilities by constraining RNTI values and applying sanity checks on payload contents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If blind decoding is performed to detect grant messages without advance knowledge of configuration parameters, then the device can receive resource allocations dynamically, but false positives occur leading to incorrect detection of valid control signaling
Solution Approach 1:
The system performs preliminary actions by pre-configuring the device with a subset of possible parameter values before blind decoding occurs. The network indicates which parameter sets are valid through higher-layer signaling, allowing the device to prepare valid hypothesis combinations in advance, thereby reducing false positives during actual grant detection
Solution Approach 2:
The invention changes the approach from testing all possible parameter combinations to testing only a constrained subset. By dynamically adjusting which parameter values are considered valid based on network indications, the system modifies the parameter search space to eliminate combinations that cannot be valid, thus reducing false alarm rates while maintaining adaptability
2Measurement precision
If a device tests multiple hypotheses for grant messaging without parameter constraints, then it can detect valid grants, but the complexity of the decoding process increases
Solution Approach 1:
The hypothesis testing process is segmented into two stages: first, the network indicates valid parameter sets through higher-layer signaling; second, the device only tests hypotheses combining these indicated parameters with possible grant values. This segmentation divides the overwhelming search space into manageable subsets, reducing decoding complexity while maintaining detection accuracy
Solution Approach 2:
The valid parameter sets are determined in advance through network signaling before the blind decoding process begins. This preliminary action provides the device with a filtered list of parameter combinations to test, significantly reducing the number of hypotheses that need to be evaluated during grant detection without compromising accuracy
Data Source
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AI summary
Systems and methodologies are described herein that facilitate processing and pruning of blind decoding results (e.g., associated with grant signaling) within a wireless communication environment. As described herein, blind decoding results associated with grant signaling and/or other suitable signaling can be pruned in various manners, thereby reducing false alarm probabilities associated with such results. For example, techniques are provided herein for constraining respective decoding candidates to possible radio network temporary identifier (RNTI) values, performing validity checking on payload of respective decoding candidates, and selecting a most likely decoding candidate from a previously pruned set of candidates. Further, techniques are described herein for generating filler bits (e.g., padding bits, reserved bits, etc.) in a grant message according to a predefined pattern, thereby enabling checking of such bits to further reduce false alarm rates.