5G Random Access Preamble Mapping for Adaptive AL and MCS
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Solution Overview
Problem
5G wireless networks face challenges in achieving robust and efficient random access while conserving network resources, as traditional base stations struggle to differentiate UE distance and path loss, leading to suboptimal AL and MCS selection.
Innovation Solution
A system that associates the UE's message preamble with its physical distance from the base station to dynamically adjust AL and MCS, and adjusts preamble allocation based on communication success rates and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the base station uses the highest Aggregation Level and lowest Modulation and Coding Scheme for all UEs to ensure robust communication, then communication reliability is improved, but network resource efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating AL and MCS configurations based on UE distance groups. Instead of using uniform highest AL for all UEs, the base station assigns appropriate AL levels (e.g., AL=1 for close UEs, AL=2 or AL=4 for distant UEs) based on their specific path loss characteristics. This localized adaptation ensures each UE receives the minimum necessary resources for reliable communication, improving overall network efficiency while maintaining robustness for distant UEs.
Solution Approach 2:
The patent implements dynamics by enabling the base station to dynamically adjust AL and MCS parameters based on real-time path loss measurements and UE distance estimates. The system continuously monitors RACH preamble success rates and adjusts resource allocation accordingly, transitioning from static highest-AL configuration to adaptive resource allocation that responds to changing channel conditions and UE locations.
2Reliability
If the base station uses uniform highest Aggregation Level for all UEs, then communication robustness is improved, but network resource consumption increases
Solution Approach 1:
The patent applies parameter changes by modifying AL and MCS parameters based on UE distance and path loss characteristics. The base station estimates UE distance from RACH preamble timing and signal strength, then selects appropriate AL parameters (AL=1, AL=2, or AL=4) and corresponding MCS values. This parameter adaptation reduces resource consumption for close UEs while maintaining robust parameters for distant UEs, optimizing the balance between robustness and resource efficiency.
3Device complexity
If the base station cannot differentiate UE distance during RACH phase, then system complexity is reduced, but random access efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by having UEs perform self-assessment of their distance and path loss before initiating RACH. UEs estimate their own distance from the base station using available signal measurements and select appropriate preamble types and initial AL/MCS parameters in advance. This preliminary preparation eliminates the need for complex real-time distance measurement and parameter optimization during the RACH phase, improving random access efficiency without significantly increasing system complexity.
Data Source
AI summary
Disclosed are systems and methods for coverage-based robust and efficient random access for 5G networks which uses a higher Aggregation Level (AL) and lower and Modulation and Coding Scheme (MCS) the larger the Path Loss (PL) between the user equipment (UE) and the base station. To do so, the message preamble of the Random Access Channel (RACH) message received from the UE by the base station (gNB) is associated with the PL between the base station and the UE, and is thus used to determine the AL and MCS.