UE-Centric Clustering for CoMP Scheduling
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G NR and LTE, face challenges in efficiently managing scheduling conflicts and resource allocation for user equipment (UE) in coordinated multipoint (CoMP) operations, leading to suboptimal performance and interference issues at cell edges due to static clustering.
Innovation Solution
The implementation of UE-centric clustering and efficient scheduling techniques, where a central entity determines a UE conflict graph to schedule UEs based on signal quality and interference management, ensuring independent sets are scheduled simultaneously and orthogonal resources are assigned to conflicting CoMP clusters, thereby optimizing resource allocation and minimizing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static clustering is used for CoMP operations, then device complexity is reduced, but network efficiency and interference management performance deteriorate
Solution Approach 1:
The patent implements dynamic UE-centric clustering where transmission points are dynamically associated with UEs based on current channel conditions and scheduling needs, replacing static clustering configurations. This allows the system to adapt cluster compositions in real-time to optimize interference management and resource allocation while maintaining manageable complexity through UE-centered organization.
Solution Approach 2:
The system dynamically adjusts clustering parameters such as association between transmission points and UEs, cluster identifiers, and resource allocation based on real-time channel quality measurements and scheduling requirements. This parameter adaptation enables optimal interference coordination without requiring complex static reconfiguration.
2Productivity
If dynamic UE-centric clustering is implemented, then network efficiency and interference management improve, but scheduling complexity increases
Solution Approach 1:
The scheduling problem is segmented into UE-specific cluster management and cluster-level resource allocation. Each UE maintains its own dedicated cluster association, allowing independent scheduling decisions per UE while coordinating across clusters. This segmentation reduces overall scheduling complexity by breaking down the global optimization problem into manageable local decisions.
Solution Approach 2:
The patent introduces cluster-level scheduling entities that act as intermediaries between individual UE schedulers and the overall resource allocation system. These intermediaries coordinate resource assignments across multiple UEs sharing common transmission points, resolving conflicts and optimizing allocation without requiring direct complex interactions between all UEs.
3Object-affected harmful factors
If orthogonal resources are assigned to conflicting CoMP clusters, then interference is minimized, but resource allocation efficiency decreases
Solution Approach 1:
The system applies different resource allocation strategies to different spatial locations and UE clusters based on local interference conditions. Rather than uniformly assigning orthogonal resources to all clusters, the system identifies specific conflict scenarios and applies orthogonal resource assignment only where necessary, allowing more efficient resource utilization in low-interference scenarios while maintaining interference protection where needed.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for user equipment (UE)-centric clustering and efficient scheduling for coordinated multipoint (CoMP). A method for UE-centric clustering and central scheduling includes determining a UE conflict graph. Vertices in the UE conflict graph are UEs for which there is a transmission and edges between vertices are UEs that have a scheduling conflict. The method includes transmitting signaling to schedule the UEs with resources for the transmission based on the UE conflict graph. A method for UE-centric clustering and cluster scheduling includes determining a cluster graph. Vertices in the cluster graph are CoMP clusters for one or more UEs for which there is a transmission and edges between vertices are CoMP clusters that have a scheduling conflict. The method includes transmitting signaling to assign resources to the CoMP clusters based on the cluster graph.