Beam Density-Aware Neighbor Lists for Mobile Handover
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Solution Overview
Problem
Existing mobile networks struggle to optimize handovers between base stations using beamforming due to the lack of consideration for beam density and mobility, leading to inefficient throughput and increased network overhead.
Innovation Solution
A system that enhances neighbor lists by incorporating beam density information and mobility data to select the most suitable receiving base station for handover, using a server to adjust neighbor lists based on beamforming configurations and device requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If base stations use beamforming with multiple beams to improve coverage and throughput, then signal energy and throughput are improved, but the complexity of neighbor list management and handover optimization worsens due to varying beam densities
Solution Approach 1:
The patent changes the parameters of neighbor lists by incorporating beam density information and mobility data. The system adjusts neighbor lists dynamically based on beamforming configurations, device mobility patterns, and handover performance metrics, transforming static neighbor lists into adaptive structures that optimize throughput while managing complexity through parameter-based differentiation.
Solution Approach 2:
The patent segments the neighbor list management by creating multiple neighbor lists with different configurations tailored to specific beam density scenarios and mobility patterns. Instead of a single uniform neighbor list, the system divides management into segmented categories (e.g., high mobility vs. low mobility, high beam density vs. low beam density) to reduce overall complexity while maintaining optimized performance for each segment.
2Manufacturing precision
If base stations increase the number of beams to improve coverage granularity, then coverage precision and throughput are improved, but each beam becomes narrower making device placement more critical
Solution Approach 1:
The patent applies dynamics by making beam configurations and neighbor lists adaptive rather than static. The system dynamically adjusts beamforming parameters, neighbor list compositions, and handover thresholds based on real-time device mobility detection and channel conditions. This dynamic adaptation allows the system to maintain high coverage precision with narrow beams while compensating for placement sensitivity through real-time adjustments.
Solution Approach 2:
The patent implements feedback mechanisms that monitor device position, mobility patterns, and handover performance to continuously optimize beam configurations. The system uses feedback from device measurements and network performance metrics to adjust beamwidth, beam directions, and neighbor list parameters, thereby maintaining coverage precision while improving ease of operation through adaptive compensation for device placement variations.
3Productivity
If the network uses traditional neighbor lists without beam density consideration, then system simplicity is maintained, but handover efficiency and throughput optimization deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple neighbor lists with different beam density characteristics and mobility optimizations before handover events occur. The system prepares adaptive neighbor lists in advance based on predicted device mobility patterns and network conditions, enabling faster and more efficient handovers without requiring complex real-time calculations during the actual handover process.
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.