Machine Learning PUCCH Allocation for UE-Specific Latency Control
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Solution Overview
Problem
Conventional Radio Resource Management (RRM) systems allocate PUCCH resources to UEs statically at the cell level without considering UE-specific requirements, leading to under or over utilization and increased latency, especially for high-priority UEs.
Innovation Solution
A method and resource allocation entity using a machine learning model to identify PUCCH resource pools and resources for each UE based on cell and UE parameters, optimizing the allocation of PUCCH resource sets to improve efficiency and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If PUCCH resources are allocated statically at cell level using first come first serve basis, then resource allocation is simple and fast, but resource utilization becomes suboptimal and latency increases for high priority UEs
Solution Approach 1:
The patent applies local quality by transitioning from uniform cell-level resource allocation to UE-specific resource allocation. Each UE receives customized PUCCH resource configurations based on its individual characteristics such as service type, channel conditions, and priority level. This allows high-priority UEs to obtain optimized resource allocations with shorter periodicities while lower-priority UEs receive appropriate resource configurations, thereby improving overall resource utilization efficiency and reducing latency for critical services.
Solution Approach 2:
The patent implements dynamics by making PUCCH resource allocation adaptive rather than static. The base station dynamically adjusts resource allocation parameters including periodicity, offset, and resource block assignments based on real-time UE requirements, channel conditions, and network load. This dynamic allocation mechanism enables the system to respond to changing traffic patterns and prioritize high-priority UEs when needed, resolving the contradiction between simple allocation and optimal resource utilization.
2Loss of time
If shorter periodicity and offset values are allocated to UEs, then latency is reduced and data transmission is faster, but resource consumption increases and may lead to over utilization
Solution Approach 1:
The patent applies parameter changes by adjusting PUCCH resource allocation parameters (periodicity, offset, resource blocks) based on UE-specific conditions. For high-priority UEs or those experiencing poor channel conditions, the system allocates shorter periodicities and larger resource blocks to reduce latency and improve reliability. For UEs with good channel conditions and lower priority, longer periodicities and smaller resource blocks are allocated to conserve resources. This parameter adaptation resolves the contradiction between reducing latency and managing resource consumption.
Solution Approach 2:
The patent implements partial or excessive action by allocating resources selectively based on UE priority and requirements. High-priority UEs receive excessive resources (shorter periodicity, more resource blocks) to ensure low latency and high reliability, while standard or low-priority UEs receive partial resources with longer periodicities. This differentiated approach ensures that critical services receive the necessary resources to minimize latency without causing overall system resource exhaustion.
3Device complexity
If PUCCH resources are allocated based on first come first serve basis, then allocation process is simple, but service type and UE specific capabilities are not considered leading to suboptimal allocation
Solution Approach 1:
The patent implements feedback mechanisms where UEs provide information about their service requirements, channel conditions, and priority levels to the base station. The base station uses this feedback to make informed resource allocation decisions, matching PUCCH resource configurations to UE-specific needs. This feedback-driven approach enables the system to consider service type and UE capabilities while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The patent applies self-service by enabling UEs to indicate their own requirements and preferences through uplink transmissions. UEs autonomously provide information about their service type, priority level, and channel conditions, allowing the base station to perform UE-specific resource allocation without complex centralized control. This self-service mechanism simplifies the allocation process while improving optimality by incorporating UE-specific information into the decision-making process.
Data Source
AI summary
The present disclosure relates to method and resource allocation entity for allocating Physical Uplink Control Channel (PUCCH) resources to User Equipments (UEs) in a communication network. The method comprises identifying one or more PUCCH resource pools to be allocated to cell camped on by one or more UEs, based on one or more first parameters associated with cell, using machine learning model. Further, the method comprises identifying one or more PUCCH resources to be allocated to each UE, based on one or more PUCCH resources pools and one or more second parameters associated with corresponding UE, using machine learning model. The method comprises allocating one or more PUCCH resource sets from plurality of PUCCH resource sets to each UE, based on one or more PUCCH resources identified for each UE and one or more parameters of plurality of PUCCH resource sets.