Beam Density Neighbor Lists for Mobile Network Handover
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Solution Overview
Problem
Existing mobile networks struggle to optimize handover processes in beamforming scenarios, particularly when multiple base stations with varying beam configurations and densities are involved, leading to inefficient throughput and increased network overhead due to frequent handovers.
Innovation Solution
A system and method that enhances neighbor lists by incorporating beam density information for base stations, allowing servers to select receiving base stations based on beam density, mobility, and throughput requirements, thereby optimizing handover decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If base stations use narrow beams with high beam density to improve throughput, then user devices located in beam centers achieve higher throughput, but handover frequency increases and network overhead increases
Solution Approach 1:
The system performs preliminary assessment of beam density and beam width characteristics for neighboring base stations before handover decisions are made. The network node collects and stores information about the number of beams and beamwidth of each base station in advance, enabling proactive handover optimization rather than reactive responses to poor performance
Solution Approach 2:
The invention changes the parameters used for handover decision-making by incorporating beam density (number of beams) and average beamwidth as key selection criteria. Instead of using only traditional signal strength metrics, the system evaluates the geometric characteristics of beams to predict throughput performance and handover stability
2Productivity
If base stations use narrow beams with high beam density, then user devices can achieve higher throughput when centered in beams, but the complexity of beam management and neighbor list evaluation increases
Solution Approach 1:
The invention extracts and isolates the critical beam characteristics (number of beams and average beamwidth) from the complex beamforming configuration data. By focusing only on these two key parameters, the system simplifies the evaluation process while maintaining the ability to predict throughput performance accurately
Solution Approach 2:
The derived parameters of beam density and average beamwidth serve multiple functions: they predict throughput performance, estimate handover frequency, and guide neighbor list optimization. This multi-functional use of simplified parameters reduces the overall complexity of beam management across different base stations with varying configurations
3Ease of operation
If traditional handover selection based only on signal strength is used, then implementation is simple, but throughput is not optimized and handover frequency increases
Solution Approach 1:
The system pre-calculates and stores beam density and average beamwidth metrics for all neighboring base stations before handover decisions are required. This preliminary preparation allows the network node to quickly evaluate multiple base stations using the derived parameters without complex real-time calculations, maintaining operational simplicity while improving throughput outcomes
Data Source
AI summary
Aspects of the subject disclosure may include, for example, providing, over a mobile network, first instructions to a first serving base station to indicate to a first communication device communicatively coupled to the first serving base station to obtain a beam bitmap of a group of base stations in proximity to the first serving base station. Further embodiments can include receiving, over the mobile network, the beam bitmap from the first serving base station, and determining a beam density for each of the group of base stations. Additional embodiments can include adjusting a neighbor list to include the beam density for each of the group of base stations resulting in an adjusted neighbor list. Other embodiments are disclosed.