18404080. MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK simplified abstract (Nokia Solutions and Networks Oy)
Contents
- 1 MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Mobility Management
- 1.13 Original Abstract Submitted
MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK
Organization Name
Nokia Solutions and Networks Oy
Inventor(s)
Abhilash Acharya of Bangalore (IN)
Ghanender Pahuja of Bangalore (IN)
MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18404080 titled 'MOBILITY ROBUSTNESS OPTIMIZATION IN WIRELESS COMMUNICATION NETWORK
Simplified Explanation
The patent proposes a technical solution to enhance mobility experience in wireless communication networks by adjusting Cell Individual Offset (CIO) and Time-To-Trigger (TTT) parameters based on Handover (HO) attempts data.
Key Features and Innovation
- Collects data on HO attempts and HO problems between network nodes.
- Aggregates data at carrier and inter-node levels.
- Uses Machine-Learning models to predict parameter adjustments.
Potential Applications
This technology can be applied in various wireless communication networks to improve mobility management and handover performance.
Problems Solved
Addresses issues related to inefficient handover processes, network congestion, and poor mobility experience for users in wireless communication networks.
Benefits
- Enhanced mobility experience for users.
- Improved network efficiency and performance.
- Optimized handover processes for seamless connectivity.
Commercial Applications
- Telecom companies can use this technology to optimize their network performance and enhance user experience.
- Network equipment manufacturers can integrate this solution into their products to offer advanced mobility management features.
Prior Art
Readers can explore existing patents related to mobility management, handover optimization, and Machine-Learning applications in wireless communication networks.
Frequently Updated Research
Stay updated on advancements in Machine-Learning algorithms for network optimization, mobility management strategies, and performance enhancement techniques in wireless communication networks.
Questions about Mobility Management
How does this technology impact network efficiency?
This technology improves network efficiency by optimizing handover processes and reducing network congestion, leading to better overall performance.
What are the potential challenges in implementing this solution in existing networks?
Implementing this solution may require network infrastructure upgrades, compatibility testing with existing systems, and training for network operators to effectively utilize the parameter adjustments suggested by the Machine-Learning models.
Original Abstract Submitted
A technical solution is proposed, which allows mobility experience to be improved by properly tuning Cell Individual Offset (CIO) and/or Time-To-Trigger (TTT) parameters in a wireless communication network. More specifically, information about a number of Handover (HO) attempts made between a source network node and each of its neighboring network nodes for a predefined period of time and a number of occurrences of each type of HO problems during the HO attempts are collected as a data vector. Then, for each “source network node—neighboring network node” pair, two data sub-vectors are obtained by aggregating the numbers from the data vector at a carrier level and an inter-node level, respectively. After that, the two data sub-vectors are used by an ensemble of two Machine-Learning models to predict whether the TTT and/or CIO parameters need to be changed.
