Wireless Network Coverage Optimization via Dynamic Antenna Tilt
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless network optimization systems fail to continuously optimize coverage and capacity by automatically reconfiguring transmit power and antenna tilt of multiple network elements without compromising network performance, especially due to interference between antenna cells and discrepancies between planned and actual network conditions.
Innovation Solution
A processor-readable medium stores code to identify network performance issues using key performance indicators, classify them as coverage holes, and adjust antenna power and tilt dynamically to mitigate these issues while minimizing interference and maintaining network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated systems adjust radio resources and network parameters to improve overall network performance, then network performance is improved, but the systems fail to continuously optimize coverage and capacity by automatically reconfiguring transmit power and antenna tilt of multiple network elements
Solution Approach 1:
The system dynamically reconfigures transmit power and antenna tilt parameters of multiple network elements in real-time based on changing network conditions. The automated system continuously monitors coverage and capacity metrics and adjusts radio resources adaptively, transforming static network configuration into a dynamic optimization process that responds to actual network performance requirements.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where network performance metrics (coverage and capacity) are continuously monitored, analyzed, and used to automatically adjust transmit power and antenna tilt parameters. This feedback-driven approach enables the system to learn from actual network conditions and continuously optimize performance without manual intervention.
2Area of stationary object
If transmit power and antenna tilt of multiple network elements are automatically reconfigured to improve coverage and capacity, then coverage and capacity are improved, but interference between two antenna cells increases
Solution Approach 1:
The system applies different transmit power and antenna tilt configurations to different network elements based on their specific local conditions and coverage requirements. Each antenna cell is optimized independently with tailored parameters that account for local geography, traffic density, and interference environment, rather than applying uniform settings across the entire network.
Solution Approach 2:
The system continuously adjusts key parameters including transmit power levels and antenna tilt angles of multiple network elements to optimize coverage and capacity. By dynamically changing these parameters based on real-time network conditions, the system expands coverage areas while managing interference through coordinated parameter optimization across the network.
3Ease of operation
If known network optimization systems are used, then some network parameters are adjusted, but the systems fail to suitably react and readjust to differences between planned coverage and capacity and actual (in use) coverage and capacity
Solution Approach 1:
The system performs self-optimization by automatically monitoring its own network performance, identifying coverage and capacity deficiencies, and adjusting its configuration parameters without external intervention. The automated system compares planned versus actual network performance and autonomously implements corrective adjustments, enabling the network to self-correct and adapt to changing conditions.
Solution Approach 2:
The system implements continuous feedback monitoring that compares planned coverage and capacity against actual measured performance. This feedback mechanism detects discrepancies between expected and actual network conditions and triggers automatic readjustment of radio resources and network parameters to align actual performance with planning objectives.
Data Source
AI summary
A non-transitory processor-readable medium stores code to cause a processor to receive a performance indicator associated with a first mode. The code causes the processor to calculate, using the performance indicator associated with the first mode, a first metric value associated with a first metric and an objective of the first mode. The code causes the processor to calculate, using the first metric value, a second metric value associated with a second metric and an objective of a second mode. The second metric value partially compensates for a change in a performance indicator associated with the second mode when the first metric value is implemented. The code causes the processor to send a signal associated with the first metric value and a signal associated with the second metric value to an antenna module such that the antenna module implements the first metric value and the second metric value.