Carrier Resource Allocation in LTE-Advanced Networks
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Solution Overview
Problem
Current cellular networks face challenges in efficiently allocating resources across multiple carriers due to varying user utility functions and the need for revised resource allocation algorithms that account for carrier aggregation, shadow prices, and differing application demands, particularly in LTE-Advanced networks.
Innovation Solution
A modified Frank Kelly algorithm is developed to generate user utility functions representing a sum of application utility functions, iteratively determining optimal rate allocations and shadow prices across primary and secondary carriers, allowing for carrier aggregation and prioritizing resource allocation based on user bids and network costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional resource allocation algorithms are used, then implementation is simple, but they cannot account for varying user utility functions and carrier aggregation scenarios
Solution Approach 1:
The patent segments the resource allocation problem by separating primary carrier allocation from secondary carrier allocation. The algorithm first determines rate allocations for the primary carrier, then uses those results to inform secondary carrier allocations. This segmentation allows the system to handle complex multi-carrier scenarios with varying utility functions while maintaining a structured, manageable approach to the overall allocation process.
Solution Approach 2:
The patent implements dynamic resource allocation by iteratively adjusting rate allocations based on user utility functions and shadow prices. The algorithm dynamically adapts to different carrier aggregation scenarios and varying user demands by continuously optimizing rate allocations across multiple carriers, rather than using static allocation rules.
2Productivity
If carrier aggregation is implemented, then network capacity and user data rates increase, but resource allocation complexity and shadow price determination become more difficult
Solution Approach 1:
The patent introduces shadow prices as intermediary variables that mediate between user utility functions and resource allocation decisions. Shadow prices serve as signals that capture the marginal value of additional resources, enabling the algorithm to determine optimal rate allocations across multiple carriers without directly solving the complex optimization problem at each step. This intermediary mechanism simplifies the determination of allocations in carrier aggregation scenarios.
Solution Approach 2:
The patent implements feedback mechanisms where shadow prices derived from utility function evaluations are fed back into the allocation process to adjust rate allocations. This feedback loop allows the system to iteratively converge on optimal allocations across primary and secondary carriers, balancing network capacity utilization with user utility maximization in carrier aggregation scenarios.
3Measurement precision
If utility functions with inelastic regions are considered, then user demand accuracy improves, but solution uniqueness is lost
Solution Approach 1:
The patent applies partial action by focusing on finding rate allocations that are sufficient to meet user demands within inelastic regions, rather than requiring a unique optimal solution. The algorithm accepts that multiple solutions may exist but seeks to find allocations that adequately satisfy user utility requirements, particularly in regions where demand is inelastic and small changes in allocation do not significantly impact user satisfaction.
Data Source
AI summary
A process for selecting an optimal individual user solution including an optimal rate allocation and associated price for a predetermined bandwidth of cellular network resources includes application of an iterative process and selection from multiple proposed user solutions. The inputs to the iterative process include at least an initial user bid, an initial network rate allocation, a generated user utility function and the cost data. The user utility function includes multiple application user functions. Optimal solution selection includes comparing each multiple proposed user rate allocation with the initial network rate allocation for the user and selecting the closest multiple proposed user rate allocation to the initial network rate allocation. The process may be applied to aggregated cellular carrier scenarios.