Aerial UE Grouping for Overlapping Coverage Load Balancing
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Solution Overview
Problem
Existing techniques for load balancing in aerial communication networks, particularly for Low-Altitude Platform (LAP)-based aerial cells, fail to consider the unique challenges of interdependence between UEs in overlapping coverage areas and optimal resource utilization within limited hovering time, leading to inefficiencies in user association and resource management.
Innovation Solution
A method and system for managing data traffic in aerial communication networks by predicting UE data traffic, creating a time-variant adjacency scheme based on inter-UE communication weights, forming groups of UEs with high communication weights, and allocating these groups to aerial base stations based on predicted traffic, coverage, and capacity, using a Fractional Group Multiple Knapsack Problem approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LAP-based aerial cells are deployed to enhance coverage and capacity, then network coverage and capacity are improved, but operational complexity and resource management difficulty increase
Solution Approach 1:
The patent dynamically changes key parameters including UE grouping based on inter-UE communication weights, aerial cell trajectory positions, and resource allocation strategies. By adapting these parameters in real-time based on predicted data traffic and coverage requirements, the system manages the complexity of multiple LAPs while maximizing coverage and capacity benefits
Solution Approach 2:
The system implements dynamic UE association and load balancing where UEs are continuously reassigned to aerial cells based on predicted traffic patterns, current load conditions, and coverage requirements. This dynamic approach allows the network to adapt to changing conditions, managing operational complexity through automated real-time adjustments rather than static configurations
2Quantity of substance
If the fleet size of LAPs is minimized to reduce CAPEX, then capital expenditure is reduced, but resource utilization efficiency decreases
Solution Approach 1:
The system performs preliminary prediction of data traffic for each UE before making association decisions. By predicting future traffic requirements and pre-planning UE associations with aerial cells, the system ensures that each LAP is fully utilized for its hovering duration, maximizing resource utilization efficiency with a minimized fleet size
Solution Approach 2:
The patent optimizes the trajectory parameters and hovering positions of LAPs based on predicted traffic distributions and UE locations. By dynamically adjusting flight paths and停留 positions, the system ensures maximum coverage and resource utilization from each LAP, achieving high productivity with minimal fleet size
3Productivity
If UEs are optimally associated with aerial cells to maximize resource utilization, then network capacity is improved, but trajectory planning complexity increases
Solution Approach 1:
The patent formulates trajectory planning as an optimization problem where key parameters (trajectory points, hovering durations, speeds) are dynamically adjusted to maximize resource utilization. The system changes trajectory parameters in real-time based on predicted traffic patterns and UE association requirements, achieving optimal resource utilization through automated parameter optimization
Solution Approach 2:
The system implements a feedback mechanism where predicted data traffic and actual network conditions are continuously monitored, and trajectory plans are adjusted accordingly. This closed-loop approach optimizes UE association and trajectory planning together, maximizing resource utilization while managing complexity through automated feedback-based adjustments
4Productivity
If load balancing techniques are applied to distribute users across cells, then network efficiency is improved, but management complexity in overlapping coverage areas increases
Solution Approach 1:
The patent introduces time-variant adjacency schemes that dynamically change UE associations based on predicted data traffic and inter-UE communication weights. By adjusting association parameters in real-time rather than using static load balancing rules, the system improves network efficiency while managing the complexity of overlapping coverage areas through automated adaptive adjustments
Solution Approach 2:
The system implements dynamic load balancing where UE associations are continuously optimized based on predicted traffic patterns, current cell loads, and coverage conditions. This dynamic approach automatically adapts to changing conditions in overlapping coverage areas, improving network efficiency without requiring complex manual management interventions
Data Source
AI summary
A method includes: predicting data traffic for user equipment (UEs) for an aerial scheduling period (ASP), wherein the UEs are connected to aerial base stations (UXNBs); creating a time-variant adjacency scheme for the UEs for the asp, wherein the time-variant adjacency scheme indicates an inter-UE communication weight between each pair of UEs; forming a plurality of group of UEs based on the time-variant adjacency scheme, wherein the inter-UE communication weight between each pair of UEs in the plurality of group of UEs exceeds a predefined threshold value; and allocating each group of UEs to an aerial base station (UXNB), based on at least one of the predicted data traffic, coverage information of each UE in a corresponding group of UEs, and a predefined capacity of the UXNB, wherein the coverage information of each UE matches with a coverage zone of the UXNB.