Distributed Antenna System Dynamic Capacity Allocation
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Solution Overview
Problem
Distributed antenna systems face limitations in maintaining and improving service quality due to fixed service capacity and scope, which cannot adapt effectively to changing communication service usage and user movements, leading to suboptimal service delivery in high-demand areas.
Innovation Solution
A method and system for dynamically distributing service capacity by monitoring the operating state of remote units, analyzing usage patterns, and predicting service capacity needs, allowing for adaptive capacity allocation based on movement patterns and time-specific demands, using a monitoring module, pattern analysis module, and capacity distribution module to optimize service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed service capacity is provided in distributed antenna system, then system simplicity is maintained, but service quality cannot be maintained when usage changes
Solution Approach 1:
The patent implements dynamic service capacity distribution by enabling remote units to adjust their service capacity based on real-time usage patterns. The system transitions from fixed capacity allocation to dynamic capacity adjustment, where each remote unit can independently modify its service capacity in response to changing traffic conditions, user movements, and demand patterns, thereby resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring usage patterns, traffic conditions, and service quality metrics at each remote unit. This feedback information is used to automatically adjust service capacity allocation, creating a closed-loop control system that adapts to changing conditions without requiring complex manual intervention, thus improving service quality adaptability while managing system complexity.
2Reliability
If service capacity is increased to meet high demand, then service quality improves, but system resource utilization becomes inefficient during low demand
Solution Approach 1:
The patent enables dynamic adjustment of service capacity at each remote unit based on real-time demand conditions. During high demand periods, service capacity is automatically increased to maintain service quality, while during low demand periods, capacity is reduced to prevent resource waste. This dynamic scaling resolves the contradiction between maintaining reliable service quality and avoiding energy loss.
Solution Approach 2:
The system changes operational parameters (service capacity levels) of remote units based on monitored usage patterns and traffic conditions. By adjusting capacity parameters dynamically rather than maintaining fixed high capacity, the system ensures service quality reliability when needed while minimizing resource waste during low utilization periods.
3Area of stationary object
If more remote units are deployed to expand coverage, then service scope increases, but system complexity and cost increase
Solution Approach 1:
The patent makes each remote unit universal and multi-functional by enabling them to dynamically adjust their service capacity and adapt to different usage scenarios. Rather than requiring specialized configurations for different coverage areas, each remote unit can independently optimize its performance, reducing overall system complexity while expanding effective service coverage through flexible capacity distribution.
Solution Approach 2:
The system uses dynamic capacity allocation to effectively expand service coverage without proportionally increasing the number of remote units. By enabling existing remote units to dynamically adjust and optimize their service capacity, the system achieves expanded effective coverage area while avoiding the complexity and cost of deploying additional fixed-capacity units.
4Productivity
If service capacity is dynamically adjusted, then responsiveness to demand changes improves, but control system complexity increases
Solution Approach 1:
The patent implements feedback-based automatic control where each remote unit monitors its own usage patterns and traffic conditions, then automatically adjusts its service capacity in response. This decentralized feedback mechanism achieves high service responsiveness to demand changes without requiring complex centralized control, as each unit independently makes capacity adjustment decisions based on local conditions.
Solution Approach 2:
The system enables remote units to self-adjust their service capacity based on monitored usage patterns and traffic conditions. Each remote unit performs self-service by automatically optimizing its own capacity allocation without external intervention, achieving rapid responsiveness to demand changes while minimizing control system complexity through autonomous operation.
Data Source
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AI summary
A service capacity distribution method of a distributed antenna system includes: monitoring an operating state of each of a plurality of remote units; analyzing a usage pattern for the plurality of service units based on the monitored operating state; and distributing the service capacity of each of the plurality of remote units based on the analyzed usage pattern.