Dynamic Frequency Reuse for Wireless Network Capacity
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Solution Overview
Problem
Current wireless network capacity management is inefficient due to static frequency assignments, which do not adapt to dynamic network traffic patterns, leading to suboptimal frequency reuse and increased inter-cell interference, especially in large-scale networks.
Innovation Solution
A hybrid inter-cell interference coordination (ICIC) technique that dynamically optimizes frequency reuse factors, transmit power levels, and carrier frequency component assignments across a geographical area by gathering and processing network and user endpoint device information to generate cell-specific traffic patterns and classify UEs, thereby maximizing network capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If frequencies are assigned to users in a static manner, then the network configuration is simple and stable, but the network capacity is suboptimal and cannot adapt to dynamic traffic patterns
Solution Approach 1:
The patent applies dynamics by transitioning from static frequency assignments to dynamic frequency reuse factor adjustments. The system continuously monitors network traffic patterns and dynamically modifies frequency reuse factors for different cells and time periods, allowing the network to adapt to changing conditions while optimizing capacity utilization.
Solution Approach 2:
The patent changes the parameter of frequency reuse factors from fixed values to variable parameters that can be adjusted based on network conditions. By modifying reuse factors as a variable parameter rather than a static configuration, the system optimizes network capacity without requiring complete reconfiguration of frequency assignments.
2Reliability
If a large number of cells are deployed to provide improved cellular coverage, then coverage is enhanced, but inter-cell interference increases
Solution Approach 1:
The patent applies local quality by assigning different frequency reuse factors to different cells based on their specific traffic patterns and interference conditions. Instead of using a uniform reuse factor across all cells, the system tailors the reuse factor to local conditions, allowing high-traffic cells to use higher reuse factors while low-traffic cells use lower factors, thereby reducing overall inter-cell interference.
Solution Approach 2:
The system dynamically adjusts frequency reuse factors in response to changing network conditions and traffic patterns. By making the reuse factors time-varying and condition-dependent, the network can adapt to reduce inter-cell interference as traffic demands change, maintaining coverage while minimizing harmful interference.
3Productivity
If carrier components are assigned based on network status knowledge, then the assignment is optimized for current conditions, but the assignment cannot adapt to future traffic variations
Solution Approach 1:
The patent applies preliminary action by using historical traffic data and predictive models to anticipate future traffic patterns before making frequency assignments. The system performs preliminary analysis of traffic trends and uses this information to proactively adjust frequency reuse factors, rather than reacting to changes after they occur.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual network performance and traffic patterns. This feedback information is used to refine future frequency assignments and adjust reuse factors, creating a closed-loop system that learns from past performance to optimize future assignments while adapting to traffic variations.
Data Source
AI summary
A method, computer-readable storage device and an apparatus for maximizing the capacity of a wireless network across a geographical area are disclosed. For example, the method monitors the wireless network and gathering network information and user endpoint device information for a geographical area comprising a plurality of cells of the wireless network, processes, for each cell, the network information and the user endpoint device information that is gathered and generating a cell specific traffic pattern, determines whether the capacity is below a threshold, and increases the capacity of the geographical area in accordance with the network information and the user endpoint device information that is processed for each cell, and the cell specific traffic pattern that is generated for each cell, when the capacity is below the threshold.