Adaptive PRACH Preamble Configuration for Cellular Interference Reduction
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Solution Overview
Problem
Existing cellular network systems face challenges in configuring random access parameters, particularly in larger cells where manual configuration is cumbersome, error-prone, and results in suboptimal performance due to interference and increased complexity, leading to reduced detection accuracy and resource reuse.
Innovation Solution
The solution involves automatically tuning parameters associated with the construction of Physical Random Access Channel (PRACH) preambles using stored statistics from previous transmissions, allowing for optimized cyclic shift lengths and root sequence allocation tailored to individual cells, reducing interference and improving detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of PRACH parameters is used, then configuration flexibility is maintained, but configuration accuracy and performance optimization deteriorate due to human error and suboptimal parameter selection
Solution Approach 1:
The base station automatically configures PRACH parameters by storing statistics from previous transmissions and autonomously tuning parameters such as cyclic shift length and root sequence allocation based on observed cell conditions, eliminating the need for manual configuration while achieving optimal performance
Solution Approach 2:
The system uses stored statistics from previous PRACH transmissions as feedback to continuously optimize parameter configuration, analyzing historical data on detection performance and interference patterns to automatically adjust parameters for improved accuracy
2Measurement precision
If cyclic shift length is increased to reduce interference, then detection accuracy improves, but the number of available unique preambles decreases
Solution Approach 1:
The system dynamically adjusts the cyclic shift length parameter based on stored statistics and current cell conditions, allowing the parameter to vary over time rather than being fixed, thus optimizing detection accuracy while maintaining sufficient preamble availability through adaptive tuning
Solution Approach 2:
The base station changes the cyclic shift length parameter based on observed interference patterns and detection performance from stored statistics, automatically selecting optimal parameter values that balance detection accuracy with the need for sufficient unique preambles
3Quantity of substance
If more root sequences are used to increase unique preambles, then preamble availability increases, but interference and detection complexity increase
Solution Approach 1:
The system automatically adjusts the number of root sequences parameter based on stored statistics regarding interference levels and detection performance, dynamically selecting the optimal number of root sequences to use in different cell conditions, thereby balancing preamble availability with interference management
4Area of stationary object
If cell size increases, then coverage area expands, but the number of available cyclic shifts decreases due to longer propagation delays
Solution Approach 1:
The system dynamically adapts the cyclic shift configuration based on the actual cell size and propagation delay characteristics observed in stored statistics, allowing larger cells to use longer cyclic shifts while maintaining sufficient unique preambles through automatic parameter tuning rather than being constrained by fixed configurations
Data Source
AI summary
The present invention enables automatic configuration of random access parameters. The base station is configured to control PRACH transmission of UEs, wherein the UE PRACH transmission comprises transmission of PRACH preambles constructed of root sequences. This is achieved by storing statistics associated with previous transmissions. A parameter associated with the construction of the PRACH is tuned based on the stored statistics. The base station controls the UE PRACH transmission by transmitting information to the UEs relating to the tuned parameter. This information is used by the UE when constructing the PRACH preambles. In this way, the cell parameters relating to random access can be improved to fit better to individual cells resulting in improved resource usage.


