Radio Resource Allocation Using Context-Aware Heat Maps
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Solution Overview
Problem
Conventional wireless communication networks fail to optimize radio resource allocation effectively, as they do not consider user-specific quality of experience (QoE) or context-sensitive needs, leading to suboptimal allocation of resources and neglecting the value users place on communication.
Innovation Solution
Implementing a system that allocates and schedules radio resources based on per-user use context (UCX) to maximize aggregate user QoE, using a radio resource control point facility that tracks user devices, mediates bidding processes, and generates heat maps to optimize resource allocation in space, time, and frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional network-based radio resource allocation criteria are used, then network management complexity is reduced, but user-specific quality of experience (QoE) optimization is lost
Solution Approach 1:
The patent segments the radio resource allocation process into two distinct phases: a network-controlled scheduling phase that manages overall resource distribution, and a user-device optimized allocation phase that handles user-specific QoE parameters. This segmentation allows the network to maintain manageable complexity while enabling fine-grained user-specific optimization through device-side intelligence and feedback mechanisms.
Solution Approach 2:
The patent implements feedback mechanisms where user devices report their actual QoE experience and resource allocation needs back to the network. This feedback loop enables the network to adjust scheduling decisions based on real user experience data, resolving the contradiction by allowing continuous optimization of user-specific QoE while maintaining overall network management through automated adaptive algorithms.
2Productivity
If dynamic allocation strategies with multiple parameters are implemented, then radio resource allocation optimization is improved, but computational complexity increases
Solution Approach 1:
The patent divides the computationally intensive optimization tasks into segments handled at different levels: the network performs high-level scheduling based on aggregate parameters, while device-side algorithms handle user-specific optimizations using locally available information. This segmentation reduces the computational burden on any single component while maintaining overall optimization effectiveness.
Solution Approach 2:
The patent applies partial optimization actions by focusing computational resources on the most critical allocation decisions rather than optimizing all parameters simultaneously. The system performs sufficient optimization to achieve acceptable QoE without exhaustively analyzing all possible allocation scenarios, thereby reducing computational complexity while maintaining productive resource distribution.
3Manufacturing precision
If per-user context-aware allocation is implemented, then user quality of experience (QoE) is improved, but network infrastructure complexity increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of enhanced user device capabilities that locally process QoE parameters and allocation decisions. This intermediary function resides at the device level rather than requiring complex network infrastructure, allowing per-user context-aware allocation to be achieved through distributed intelligence while keeping network infrastructure relatively simple.
Solution Approach 2:
The patent enables user devices to autonomously manage their own resource allocation based on their contextual needs and QoE requirements. This self-service capability allows devices to make locally optimized allocation decisions without requiring complex network-side processing, thereby improving user-specific QoE while minimizing the increase in network infrastructure complexity.
4Ease of operation
If conventional scheduling algorithms are used, then ease of operation is maintained, but adaptability to user-specific needs is reduced
Solution Approach 1:
The patent introduces dynamic adaptability into the scheduling system through mechanisms that automatically adjust allocation strategies based on changing user needs and network conditions. The system maintains ease of operation by using automated adaptive algorithms that respond to contextual changes without requiring manual intervention, while simultaneously improving adaptability to user-specific needs through continuous learning and adjustment.
Data Source
AI summary
Methods and systems described herein relate to more optimally allocating and scheduling radio resources simultaneously in space, time and frequency to enhance user quality of experience (QoE) according to use context within the constraints of maximizing long term service provider revenue expectation for a given investment in radio access network infrastructure for improved radio resource management, such methods optionally including use of heat maps of user trajectories as a factor in a process for resource allocation.