Remote Antenna Usefulness Assessment in Distributed Systems
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Solution Overview
Problem
Current Distributed Antenna Systems (DAS) face challenges in determining the effectiveness of antenna point deployments, leading to inefficient coverage and costly reconfigurations due to inaccurate propagation predictions and lack of feedback on antenna placement.
Innovation Solution
A method to monitor and assess the usefulness of each remote unit in a DAS by measuring received signal strength and data traffic distribution, allowing for the identification of optimally placed and needed antenna points, and enabling efficient reconfiguration and future deployment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If detailed propagation prediction and planning are performed to improve antenna placement accuracy, then manufacturing precision of antenna deployment is improved, but loss of time and increased cost occur due to requiring detailed wall-material and obstacle information
Solution Approach 1:
The system performs preliminary propagation prediction and planning before actual antenna deployment by creating a digital building model with wall materials and obstacles. This preliminary action allows optimization of antenna placement without requiring detailed on-site measurements during installation, thus improving placement accuracy while reducing deployment time.
Solution Approach 2:
The system creates a digital copy or model of the building environment including wall materials, obstacles, and spatial layout. This digital model serves as a virtual planning environment where propagation predictions can be made without physical measurements, eliminating the time-consuming process of collecting detailed building information on-site.
2Area of stationary object
If more antenna points are installed to maximize coverage, then coverage area is improved, but device complexity and cost increase
Solution Approach 1:
The system monitors actual signal propagation and coverage performance after deployment, then uses this feedback to identify under-covered areas that truly need additional antenna points. This feedback mechanism prevents unnecessary installation of excess antenna points, optimizing the balance between coverage area and system complexity.
Solution Approach 2:
The system uses propagation prediction models to evaluate different antenna placement scenarios and configurations before deployment. By changing parameters such as antenna positions, orientations, and types in the simulation, the system identifies the optimal configuration that achieves maximum coverage with the minimum number of antenna points required.
3Ease of operation
If trial and error methods are used for reconfiguration, then ease of operation is maintained, but loss of time and productivity decrease
Solution Approach 1:
The system provides automated feedback on coverage performance and identifies specific areas needing improvement, then suggests optimal antenna relocation or addition positions. This feedback-driven approach replaces trial-and-error reconfiguration with a systematic method that achieves the same ease of operation while dramatically improving reconfiguration productivity.
Solution Approach 2:
Before performing physical reconfiguration, the system performs preliminary simulations and predictions to determine the optimal reconfiguration plan. This preliminary action ensures that the actual reconfiguration process is efficient and productive, as it follows a pre-planned optimal solution rather than relying on trial and error.
4Productivity
If cell split is performed to enable resource reuse, then productivity of wireless network is improved, but device complexity increases due to difficulty in determining balanced antenna point allocation
Solution Approach 1:
The system uses propagation prediction models to evaluate different antenna point allocation scenarios for cell splitting. By simulating various allocation configurations and their impact on coverage and load balance, the system automatically determines the optimal allocation that achieves balanced cells, simplifying the complex decision-making process.
Solution Approach 2:
The system monitors traffic load and coverage performance in the split cells, then provides feedback on allocation balance. This feedback mechanism allows automatic adjustment of antenna point allocation to maintain balanced load between cells, reducing the complexity of manual allocation while maximizing network productivity through resource reuse.
Data Source
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AI summary
A method (10) performed in a wireless communications system (10) for establishing usefulness of remote units is provided. The wireless communications system (10) monitors one or more remote units (2i, 2j,..., 2N ) of a distributed antenna system (l). The method (10) comprises: establishing (11), per remote unit (2i, 2j,..., 2N ), received signal strength of a wireless communication; establishing (12), per remote unit (2i, 2j,..., 2N ), a quantity related to the obtained received signal strength; and establishing (13) a measure on usefulness of each of two or more remote units (2i, 2j,..., 2N ) based on the received signal strength and on the quantity. A corresponding wireless communications system, computer programs, and computer program products are also provided.