ML-Based PUCCH Resource Allocation for Low-Latency UE Scheduling
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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 use 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 resource utilization 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 static allocation to dynamic UE-specific allocation where each UE receives customized PUCCH resources based on its service type, priority, and capabilities. The base station determines individual periodicity and offset parameters for each UE, enabling high-priority UEs to obtain shorter periodicity and reduced latency while low-priority UEs receive appropriate resource allocation, thus optimizing resource utilization across different local conditions.
Solution Approach 2:
The patent implements dynamics by changing from static cell-level resource allocation to dynamic UE-specific resource allocation. The base station continuously adjusts PUCCH resources based on real-time UE requirements, service types, and network conditions. This dynamic approach allows the system to adapt resource periodicity and offset values to match actual traffic patterns and UE priorities, improving both resource utilization and latency performance.
2Speed
If shorter periodicity and offset values are allocated to UEs, then latency is reduced and data transmission speed improves, but resource consumption increases and may lead to over utilization
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting PUCCH resource periodicity and offset values based on UE-specific factors including service type, priority level, and capabilities. High-priority UEs with urgent data transmission needs receive shorter periodicity values to achieve faster transmission speeds, while low-priority UEs receive longer periodicity values to conserve resources. This parameter optimization ensures that resource consumption is proportional to actual transmission requirements, preventing both over-provisioning and under-provisioning.
3Device complexity
If PUCCH resources are allocated based on first come first serve basis, then allocation process is simple and device complexity is low, but service type requirements and UE capabilities are not considered leading to suboptimal performance
Solution Approach 1:
The patent implements feedback by having the base station evaluate UE-specific parameters including service type, priority level, and capabilities before allocating PUCCH resources. The base station receives information about UE requirements and network conditions, processes this feedback to determine optimal periodicity and offset values, and allocates resources accordingly. This feedback mechanism enables the system to consider diverse UE requirements and optimize cell throughput performance while maintaining manageable allocation complexity through automated decision-making algorithms.
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.