Wireless Traffic Redistribution via Probabilistic Carrier Reselection
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Solution Overview
Problem
In wireless networks, especially in multi-carrier deployments, load imbalances between carriers lead to access rejections due to overload, particularly in areas with varying UE density, where hotspots require more carriers, causing inefficient spectrum utilization and potential service interruptions.
Innovation Solution
The implementation of a probabilistic method for user equipment (UE) to reselect carriers based on randomly generated or network-assigned priority values, allowing for dynamic distribution of idle UEs across carriers, thereby proactively managing traffic load and balancing carrier loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple carriers are deployed at hot spots with high UE density, then the capacity to handle high traffic load is improved, but load imbalance among carriers occurs leading to access rejections
Solution Approach 1:
The patent implements dynamic carrier reselection priority mechanisms that allow UEs to adaptively select carriers based on real-time load conditions. The network can dynamically adjust priority values and S-curve parameters to redistribute UEs from overloaded carriers to underloaded carriers, ensuring reliable access while maintaining high capacity utilization across multiple carriers at hot spots.
Solution Approach 2:
The patent changes the parameter of carrier selection by introducing S-curve based probabilistic reselection priorities instead of deterministic selection. By adjusting the S-curve parameters (such as threshold and slope), the network can control the distribution of UEs across carriers, balancing load while maintaining overall system capacity.
2Productivity
If carriers are deployed to cover different areas with varying UE density, then spectrum resource utilization is improved, but load imbalance occurs among carriers
Solution Approach 1:
The patent applies local quality by allowing different S-curve parameters and priority configurations for different geographic areas and carrier frequencies. UEs in different locations (hot spots vs. normal areas) receive area-specific reselection priorities, enabling localized load balancing that adapts to varying UE density patterns while maximizing spectrum utilization across all deployed carriers.
Solution Approach 2:
The patent implements feedback mechanisms where the network monitors carrier load conditions and adjusts S-curve parameters and priority values accordingly. This closed-loop control allows the system to respond to changing traffic patterns, maintaining load balance across carriers deployed for different geographic coverage areas while optimizing overall spectrum utilization.
3Reliability
If probabilistic reselection with random priority values is implemented, then UE distribution across carriers is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling UEs to autonomously generate random priority values and perform probabilistic carrier reselection based on broadcast S-curve parameters. This distributed decision-making approach achieves uniform UE distribution across carriers without requiring complex centralized control, as each UE independently executes the probabilistic selection algorithm using simple random number generation and threshold comparison.
Data Source
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AI summary
The apparatus includes a processor. The processer is configured to determine whether to redistribute traffic, generate a message upon determining traffic is to be redistributed, the message including cell priority values, the cell priority values including a priority value for each of a plurality of carriers, and transmit the message to the one or more user equipment.