Cell Shaping via Spatial Channel Prediction
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Solution Overview
Problem
Current cell shaping methods in wireless communications networks face challenges in ensuring consistent network coverage and avoiding coverage holes during adaptive antenna adjustments, particularly in advanced antenna systems where individual elements are observable at baseband, leading to potential performance degradation and slow adaptation.
Innovation Solution
The implementation of a network node and computer program that collects and utilizes spatial channel characteristics from wireless devices to evaluate and predict coverage before changing cell shapes, ensuring that network coverage is maintained and allowing for more rapid tuning by using previously stored data from both current and historical device locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cell shaping parameters are changed to adapt to traffic variations, then network performance and capacity are improved, but coverage holes may be created and network coverage reliability deteriorates
Solution Approach 1:
The system performs preliminary evaluation of candidate cell shapes by predicting coverage before actual deployment. Spatial channel characteristics are collected and used to simulate coverage patterns, allowing the system to identify and reject configurations that would create coverage holes before they are implemented, thus preventing reliability issues before they occur.
Solution Approach 2:
The system creates a safety buffer by requiring that candidate cell shapes maintain coverage above a threshold level for all predicted locations. This cushioning approach ensures that even with traffic variations, the cell shaping adjustments will not create coverage holes, protecting network reliability while still allowing performance optimization.
2Reliability
If small step changes are used when adjusting cell shapes, then coverage holes are avoided, but adaptation speed to traffic variations slows down
Solution Approach 1:
By performing preliminary coverage prediction and evaluation before deploying cell shape changes, the system can safely make larger adjustments without creating coverage holes. The prediction step acts as a safety mechanism that allows aggressive optimization while maintaining coverage continuity, eliminating the need for conservative small-step adjustments.
Solution Approach 2:
The system creates virtual copies of the network environment through simulation using spatial channel characteristics. These virtual models allow testing of large cell shape changes in a risk-free environment, enabling rapid adaptation by selecting from multiple candidate configurations without actually risking coverage holes in the real network.
3Measurement precision
If advanced antenna systems with baseband observable elements are used, then beam forming precision is improved, but system complexity increases
Solution Approach 1:
The system uses the antenna elements themselves to provide the measurement data needed for optimization. By observing the baseband signals from individual elements, the system can directly determine spatial channel characteristics and automatically optimize beam forming weights without requiring external measurement equipment or manual calibration, thus managing complexity through self-configuration.
Solution Approach 2:
The system implements a feedback loop where spatial channel characteristics measured from baseband observable elements are used to determine optimal cell shapes and beam forming parameters. These parameters are then applied and their effectiveness evaluated, creating a closed-loop system that automatically adapts to channel conditions while utilizing the precise measurement capability of the advanced antenna system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient cell shaping that prevents coverage holes, enables larger changes in cell shapes, and facilitates faster adaptation to traffic variations, ensuring reliable network performance without degrading coverage.
Implementation Method 1
With beam forming, the radiation pattern of the antenna may be controlled by transmitting a signal from a plurality of elements with an element specific gain and phase. In this way, radiation patterns with different pointing directions and transmission and/or reception beam widths in both elevation and azimuth directions may be created.
Implementation Method 2
With so called WD specific beam forming, (narrower) beams may be formed to specific WDs in order to increase the receive signal power in these specific WDs while at the same time controlling interference generated to other WDs receiving data transmission.
Data Source
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AI summary
There is provided a method for shaping cells in a wireless communications network. The method is performed by a network node. The method comprises acquiring previously stored spatial channel characteristics for wireless devices (WDs), the WDs being associated with a set of radio access network nodes (RANNs), the spatial channel characteristics for at least one WD of the WDs being measured between the at least one WD and at least two RANNs in the set of RANNs. The method comprises determining beam forming parameters for shaping cells for at least one RANN in the set of RANNs based on the acquired spatial channel characteristics such that at least a predetermined share of the WDs has a network coverage probability being higher than a predetermined threshold value. The method comprises notifying at least one of the RANNs in the set of RANNs of the determined beam forming parameters.