Wireless Neighbor List Update via Distance Calculation
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Solution Overview
Problem
Wireless networks face issues with identifying missing neighbors and updating current neighbors due to inaccuracies in neighbor lists, leading to call drops, interference, and reduced network capacity, as existing methods rely on incomplete or inaccurate distance and power criteria.
Innovation Solution
A method that uses distance and power measurements from mobile stations to identify missing neighbors by selecting the most likely sector based on calculated distances and RF propagation conditions, allowing for real-time correction of neighbor lists to prevent call drops and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use incomplete or inaccurate distance and power criteria to identify neighbors, then the neighbor list can be maintained with simpler processing, but the accuracy of neighbor identification deteriorates leading to call drops and interference
Solution Approach 1:
The patent introduces an adjunct processor as an intermediary component that performs complex distance and power calculations between mobile stations and base stations. This intermediary handles the computationally intensive tasks of calculating Euclidean distances, path losses, and received power levels, allowing the main network controller to maintain accurate neighbor lists without being overwhelmed by complexity. The adjunct processor acts as a dedicated mediator for these calculations, improving accuracy while managing system complexity.
Solution Approach 2:
The patent divides the neighbor identification process into distinct segments: (1) collecting pilot signal measurements from mobile stations, (2) calculating distances and path losses using specific formulas, (3) determining received power levels, (4) comparing against thresholds, and (5) updating neighbor lists. This segmentation allows each step to be optimized independently and facilitates parallel processing, reducing overall complexity while maintaining high measurement precision throughout the multi-stage process.
2Reliability
If neighbor lists are not updated in real-time, then system complexity and processing requirements are reduced, but call drops and interference increase due to outdated neighbor information
Solution Approach 1:
The patent implements continuous neighbor list updates by constantly monitoring pilot signal measurements from mobile stations and dynamically recalculating distances and received powers. The system continuously compares current measurements against stored neighbor information and automatically updates neighbor lists when changes are detected, ensuring real-time accuracy without requiring periodic full-rebuild processes. This continuous action maintains high call connectivity reliability while optimizing processing efficiency through event-driven updates.
Solution Approach 2:
The system employs feedback mechanisms where mobile stations continuously report pilot signal measurements to the network controller, which then recalculates neighbor relationships based on updated distance and power information. The feedback loop includes: (1) mobile station measurements, (2) controller recalculation of distances and received powers, (3) comparison with existing neighbor lists, and (4) updates when thresholds are exceeded. This feedback-driven approach ensures reliability by responding to actual network conditions while maintaining productivity through selective updating only when necessary.
3Measurement precision
If distance calculations are simplified, then processing speed increases, but the precision of determining the most likely sector deteriorates
Solution Approach 1:
The patent performs preliminary calculations of base station coordinates, path loss exponents, and reference power levels during network setup and planning phases. These pre-computed parameters are stored in databases for rapid retrieval during real-time operations. When a mobile station reports a pilot signal, the system retrieves pre-stored base station coordinates and path loss parameters instead of recalculating them, significantly reducing calculation time while maintaining sector identification precision through the use of accurate pre-computed values.
Solution Approach 2:
The system dynamically adjusts calculation parameters such as path loss exponents and threshold values based on network conditions, terrain characteristics, and signal environments. By changing parameters adaptively rather than using fixed simplified formulas, the system maintains high sector identification precision across diverse scenarios. The parameter changes allow the system to balance calculation complexity with accuracy, using more precise calculations when needed and simpler approximations when conditions permit, thereby optimizing both precision and calculation speed.
Data Source
AI summary
A method for identifying a missing neighbor in a wireless network includes receiving a report about two or more pilot signals measured by a mobile station; determining that one or more of the reported pilot signals is a missing neighbor not comprised in a current neighbor list of the mobile station; choosing one or more candidate sectors having a pilot signal with the same pilot identity as the missing neighbor; and selecting a most likely sector as the missing neighbor from the one or more candidate sectors, based on calculated distances between the mobile station and a source sector, between the mobile station and at least one of the one or more candidate sectors, and between the source sector and at least one of the one or more candidate sectors, so as to identify the missing neighbor in the wireless network.