Autocorrelation Matrix Calculation for Narrowband IoT Memory Optimization
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Solution Overview
Problem
Traditional methods for calculating autocorrelation in 5G wireless communication systems require large data sets and cumbersome memory access, leading to increased memory consumption and inefficiencies, particularly in miniaturized devices like those used in the Internet of Things (IoT), where faster signal processing is needed.
Innovation Solution
The system calculates and stores autocorrelation for specific time periods, allowing for faster processing and reduced memory usage by updating and combining autocorrelation matrices in real-time, using a stored matrix to incorporate new data without needing to store entire datasets from multiple time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods calculate autocorrelation using large datasets from multiple time periods, then measurement precision is improved, but memory consumption increases and processing speed decreases
Solution Approach 1:
The patent pre-calculates and stores autocorrelation values for each individual time period before the final autocorrelation calculation is needed. By performing this preliminary action, the system avoids the need to retrieve and process large datasets from multiple time periods during the final calculation, thereby improving processing speed while maintaining accuracy through the use of pre-computed values
Solution Approach 2:
The patent divides the autocorrelation calculation into segments by time period, calculating and storing autocorrelation values for each individual time period separately. This segmentation allows the system to process smaller data portions independently and combine results efficiently, improving both processing speed and memory utilization compared to handling large datasets as a single block
2Measurement precision
If traditional methods store datasets from multiple time periods for autocorrelation calculation, then measurement precision is improved, but memory consumption increases
Solution Approach 1:
The patent extracts only the essential autocorrelation values from each time period's dataset and stores these extracted values for later use. By taking out only the necessary computational elements rather than storing entire datasets, the system significantly reduces memory consumption while preserving the accuracy needed for final autocorrelation calculations
Solution Approach 2:
The system performs preliminary autocorrelation calculations for each time period and stores only the resulting autocorrelation values rather than the raw datasets. This preliminary processing reduces the quantity of data that needs to be stored in memory, freeing up memory resources while maintaining the precision required for accurate autocorrelation measurements
3Volume of moving object
If devices are miniaturized for IoT applications, then device size is reduced, but memory capacity and processing power are limited
Solution Approach 1:
The patent extracts and stores only the essential autocorrelation values needed for signal processing rather than storing complete datasets. This extraction approach minimizes memory capacity requirements, enabling accurate autocorrelation calculations in miniaturized IoT devices with limited memory resources
Solution Approach 2:
The system performs preliminary autocorrelation calculations and stores the results in a compact format suitable for memory-constrained environments. By preparing data in advance and storing only processed results, the patent enables efficient operation of autocorrelation functions in small IoT devices without requiring large memory capacities
Data Source
AI summary
Examples described herein include systems and methods which include wireless devices and systems with examples of an autocorrelation calculator. An electronic device including an autocorrelation calculator may be configured to calculate an autocorrelation matrix including an autocorrelation of symbols indicative of a first narrowband Internet of Things (IoT) transmission and a second narrowband IoT transmission. The electronic device may calculate the autocorrelation matrix based on a stored autocorrelation matrix and the autocorrelation of symbols indicative of the first narrowband IoT transmission and symbols indicative of the second narrowband IoT transmission. The stored autocorrelation matrix may represent another received signal at a different time period than a time period of the first and second narrowband IoT transmission. Examples of the systems and methods may facilitate the processing of data for wireless and may utilize less memory space than a device than a scheme that stores and calculates autocorrelation from a large dataset computed from various time points.