5G SSS Detection Combining Secondary Spreading Sequences
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Solution Overview
Problem
In 5G-NR systems, the detection of secondary spreading sequences (SSS) is challenging due to high noise and interference levels, particularly in mmWave frequencies, leading to significant false alarms and miss detections, which affects the reliability of cell identification and link maintenance between base stations and user equipment.
Innovation Solution
An apparatus and method that utilize a processor to identify timing of synchronization signals based on primary spreading sequences (PSS), group tentative SSSs, cross-correlate them with weights, and determine the SSS with maximum combined cross-correlation output to enhance cell identification, especially in noise-dominant environments by combining SSS signals from multiple SS blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional SSS detection methods are used in mmWave environments, then the detection process is simple, but the detection reliability deteriorates due to high noise and interference levels causing false alarms and miss detections
Solution Approach 1:
The patent combines multiple SSS signals from different SS blocks to form a composite detection signal. By merging these signals through correlation processing and combining operations, the system improves detection reliability in noisy mmWave environments. The combination of multiple signals enhances the signal-to-noise ratio and reduces false alarms and miss detections.
2Measurement precision
If multiple SSS signals are combined to improve detection sensitivity, then the detection precision improves, but the computational complexity increases
Solution Approach 1:
The patent segments the detection process into distinct stages: initial correlation of received signals with local SSS sequences, grouping of correlation results from multiple SS blocks, and final combination processing. This segmentation allows the system to handle complex computations in a structured manner, improving detection precision while managing computational complexity through organized processing steps.
Solution Approach 2:
The patent performs preliminary correlation operations between received signals and local SSS sequences before final detection. By pre-processing the signals through correlation and grouping operations, the system prepares optimized data structures that reduce the computational burden of final detection while maintaining high precision.
3Reliability
If SSS detection is performed in noisy environments, then the cell identification can be achieved, but the false alarm rate increases
Solution Approach 1:
The patent converts the harmful effect of noise into a benefit by using signal combining techniques. Multiple SSS signals received in noisy environments are combined through correlation processing, where the desired signal components reinforce each other while noise components tend to average out. This transforms the challenging noisy environment into an opportunity for improved detection through diversity combining.
Data Source
AI summary
An apparatus is provided. The apparatus includes a processor configured to receive a synchronization signal (SS) and identify timing of the SS based on a primary spreading signal (PSS) in the SS, identify tentative secondary spreading signals (SSSs) based on the identified timing, and group the tentative SSSs; a register configured to receive the SS; and a memory configured to store the tentative SSSs for each group of the tentative SSSs; wherein the processor is further configured to cross correlate the tentative SSSs, combine cross correlated outputs with weights by group, determine an SSS as the tentative SSS with a maximum combined cross correlation outputs, and determine a cell identification based on the determined SSS.