Mobile Blind Decoding Using Hypothesis Correlation for PBCH MIB
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Solution Overview
Problem
In LTE compliant systems, the process of blind decoding for the Master Information Block (MIB) contained in the Physical Broadcast Control Channel (PBCH) is computationally extensive due to the need to test multiple hypotheses for the number of transmit antennas and transmission frame number, leading to increased power and time consumption.
Innovation Solution
A method that reduces blind decoding time by testing hypotheses for the number of transmit antennas and timing, using a local channel coded reference sequence to determine the best hypothesis through Euclidean distance correlation, allowing channel decoding to be performed only once for the most likely hypothesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blind decoding is performed by testing all possible hypotheses for the number of transmit antennas and transmission frame number, then the MIB can be detected with high reliability, but the computational load and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing hypothesis testing for the number of transmit antennas and transmission frame number before conducting the computationally intensive channel decoding. The mobile device tests all possible hypotheses (3 antenna hypotheses × 4 frame number hypotheses = 12 hypotheses) by decoding the PBCH with each hypothesis and checking CRC, then only performs full channel decoding on the hypothesis that passes CRC verification. This preliminary filtering avoids unnecessary full decoding operations and reduces overall blind decoding time.
2Measurement precision
If convolutional decoding is performed for all 12 hypotheses, then the most likely hypothesis can be identified, but power consumption increases due to repeated computation
Solution Approach 1:
The patent extracts and performs only the necessary channel decoding operation on the single hypothesis that passes CRC verification, rather than performing decoding on all 12 hypotheses. By separating the hypothesis testing phase (which uses lighter computational operations) from the final decoding phase, the patent eliminates 11 unnecessary decoding operations, significantly reducing power consumption while maintaining accurate hypothesis identification through the CRC check.
Data Source
AI summary
The present disclosure relates to a method for blind decoding a signal in a decoder of a mobile device. The method comprises receiving a channel coded signal; testing a plurality of hypotheses for the decoder that are not related to channel decoding by decoding the channel coded signal to generate a hypothesis specific decoded, channel coded signal and correlating a local channel coded reference sequence against a presumed corresponding section of the hypothesis specific decoded, channel coded signal for each of the plurality of hypotheses to generate a plurality of test results. The method further comprises determining a best hypothesis based on the plurality of test results and channel decoding a hypothesis specific decoded, channel coded signal for the best hypothesis to generate a channel decoded signal.