SSB Configuration Bit Adaptation for 5G Sidelink Reliability
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Solution Overview
Problem
In 5G NR technology, sidelink communication scenarios like V2X face challenges in transmitting configuration information due to the absence of RMSI, affecting the transmission of SSB configuration information.
Innovation Solution
A method for broadcasting and receiving configuration information of SSBs in sidelink communication, where the number of bits is determined based on a relationship between the maximum number of SSBs and preset numbers, allowing efficient transmission through physical sidelink broadcast channels, even when RMSI is not available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If configuration information is transmitted using a fixed number of bits through the physical sidelink broadcast channel, then the transmission structure is simple, but the configuration information may exceed the channel capacity or fail to accurately indicate all SSBs when the maximum number of SSBs is large
Solution Approach 1:
The patent applies dynamics by making the number of bits in configuration information adaptive rather than fixed. The bit length dynamically adjusts based on the relationship between the maximum number of SSBs and the first preset number, allowing the system to optimize between coverage and channel capacity constraints in different scenarios
Solution Approach 2:
The patent changes the parameter of configuration information bit length based on system conditions. When the maximum number of SSBs exceeds the first preset number, the bit length is adjusted to match the channel capacity, ensuring reliable transmission while accurately indicating which SSBs should be transmitted
2Measurement precision
If the number of bits in configuration information is increased to indicate more SSBs, then the indication accuracy improves, but the transmission may exceed the channel capacity of the physical sidelink broadcast channel
Solution Approach 1:
The patent adjusts the bit length parameter of configuration information based on the comparison between the maximum number of SSBs and the first preset number. This ensures the bit quantity matches both the indication precision requirements and the channel capacity constraints
Solution Approach 2:
The patent uses partial indication by setting certain bits to default values (e.g., all 1s or all 0s) when the maximum number of SSBs exceeds the first preset number. This partial indication approach, combined with the two-stage indication mechanism, achieves accurate SSB identification without transmitting excessive bit information
3Productivity
If the configuration information bit length is reduced to fit channel capacity, then the transmission efficiency improves, but the ability to accurately indicate all SSBs is compromised
Solution Approach 1:
The patent segments the SSB indication into two stages: first indicating whether each SSB group should be transmitted, then indicating which specific SSBs within selected groups should be transmitted. This segmentation allows efficient use of bit resources while maintaining complete and accurate SSB indication
Solution Approach 2:
The patent employs partial indication by using default bit values to represent certain SSB transmission states. When the maximum number of SSBs exceeds the first preset number, not all SSB indications are explicitly transmitted, but the combination of explicit indications and default values ensures complete information reconstruction at the receiver
Data Source
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AI summary
The present application discloses a method for building a ranking model, a query auto-completion method and corresponding apparatuses, which relates to the technical field of intelligent search. An implementation includes: acquiring from a POI query log a query prefix input when a user selects a POI from query completion suggestions, POIs in the query completion suggestions corresponding to the query prefix and the POI selected by the user in the query completion suggestions; constructing positive and negative example pairs using the POI selected by the user and the POIs not selected by the user in the query completion suggestions corresponding to the same query prefix; and performing a training operation using the query prefix and the positive and negative example pairs corresponding to the query prefix to obtain the ranking model; wherein the ranking model has a training target of maximizing the difference between the similarity of vector representation of the query prefix and vector representation of the corresponding positive example POI and the similarity of the vector representation of the query prefix and vector representation of the corresponding negative example POIs.