RACH Preamble Sequence Optimization for Random Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current communication network systems face inefficiencies in random access procedures due to manual cell planning of RACH preamble sequences, leading to potential conflicts and suboptimal performance, especially in multi-vendor scenarios and varying cell environments.
Innovation Solution
A method and communication network node that automate the optimization of RACH preamble sequences by collecting usage statistics and neighbor cell information, optimizing the sequences, and replacing initial sets with optimized ones to ensure localized conflict-free operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual cell planning of RACH preamble sequences is used, then initial network setup is simplified, but network performance is suboptimal and conflicts occur between neighboring cells
Solution Approach 1:
The system performs self-optimization by automatically collecting statistics on RACH preamble usage and neighbor cell information, then autonomously optimizing the preamble sequences without requiring manual intervention. This eliminates the trade-off by making the system both simple to deploy and high-performance through self-tuning capabilities.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring RACH preamble usage statistics and conflict information from neighboring cells, then using this feedback to iteratively optimize preamble sequence assignments. This closed-loop approach resolves the contradiction by maintaining high performance through automated adaptation while keeping initial setup simple.
2Adaptability or versatility
If standardized RACH procedures are used, then device compatibility is ensured, but adaptability to specific cell conditions is limited
Solution Approach 1:
The system transitions from static, standardized RACH procedures to dynamic, adaptive procedures that automatically adjust preamble sequences based on real-time statistics and neighbor cell conditions. This enables the system to adapt to specific cell environments while maintaining standardized interfaces, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system optimizes specific parameters of the RACH procedure, particularly the preamble sequence selection, based on collected statistics and environmental conditions. By dynamically changing these parameters while maintaining the overall standardized procedure structure, the system achieves adaptability without excessive complexity.
3Reliability
If RACH preamble sequences are optimized for specific conditions, then local performance improves, but coordination between neighboring cells becomes more difficult
Solution Approach 1:
The system merges local optimization needs with neighbor cell considerations by collecting and analyzing neighbor cell information alongside local usage statistics. This combined approach enables localized performance improvement while automatically accounting for inter-cell coordination, eliminating the trade-off between local optimization and coordination difficulty.
Data Source
AI summary
The present invention relates to a method and a communication network node for enabling auto-tuning of preamble sequences used during random access procedures when user equipments (18) are accessing a communication network system comprising radio base stations (15) each serving at least one cell (19) and with which said user equipments (18) are communicating on uplink (13) and downlink (12) channels. An initial set of RACH preamble sequences in one cell of a radio base station (15) and statistics on RACH usage and information on identified RACH preamble sequences potentially conflicting between neighbors of said radio base station (15) is collected. The collected statistics is used for optimizing said set of RACH preamble sequences, whereby the initial set of RACH preamble sequences is replaced with the optimized set of RACH preamble sequences.