OFDM Scheduler Sub-band Prioritization Parallel Processing
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Solution Overview
Problem
Existing scheduling methods for user terminals in OFDM systems, such as LTE, face inefficiencies due to blind capping of resources and sequential processing, leading to suboptimal allocation and increased difficulty in meeting real-time constraints as the number of user terminals grows.
Innovation Solution
Implementing per sub-band prioritization to determine scheduling weights based on channel quality and service quality, allowing for parallel processing and more efficient allocation of resources, which results in a pre-allocation schedule that optimizes resource use and system capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential scheduling is used to allocate resources to user terminals, then resource allocation can be performed in real-time, but it becomes increasingly difficult to meet time constraints as the number of user terminals grows
Solution Approach 1:
The patent divides the frequency spectrum into multiple sub-bands and performs scheduling independently for each sub-band. This segmentation allows parallel processing of scheduling decisions across different sub-bands, significantly reducing the overall scheduling time while maintaining real-time constraints even as the number of user terminals increases.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by performing scheduling operations across multiple frequency sub-bands simultaneously. This dimensional expansion from sequential to parallel scheduling enables the system to handle increasing numbers of user terminals without violating time constraints.
2Ease of manufacture
If resources are capped equally among user terminals, then allocation is simple to perform, but it does not account for actual user needs and results in suboptimal resource utilization
Solution Approach 1:
The patent applies different scheduling strategies and resource caps to different user terminals based on their specific needs, channel conditions, and QoS requirements within each sub-band. This local optimization approach improves resource utilization efficiency while maintaining manageable complexity through structured weight-based scheduling.
Solution Approach 2:
The patent dynamically adjusts scheduling weights and resource caps based on user terminal characteristics, channel conditions, and QoS requirements. These parameter changes enable adaptive resource allocation that optimizes utilization efficiency while maintaining implementation feasibility through systematic weight calculation methods.
3Reliability
If resources are allocated in order of scheduling priority, then high priority users receive sufficient resources, but resources may not be efficiently used when lower priority users could benefit more from the same resources
Solution Approach 1:
The patent implements dynamic scheduling weights that can adjust priority levels based on real-time conditions. Users can transition between priority levels as channel conditions and QoS requirements change, allowing the system to optimize for both QoS satisfaction and overall system capacity through flexible, condition-based priority adjustment.
Solution Approach 2:
The patent uses QoS feedback and channel condition information to dynamically adjust scheduling weights. This feedback mechanism allows the system to identify when lower priority users could benefit more from resources and adjust allocations accordingly, optimizing both QoS satisfaction and overall system capacity.
4Device complexity
If blind resource capping is applied without considering user needs, then allocation is computationally simple, but it may allocate resources away from users who need them most
Solution Approach 1:
The patent performs preliminary calculations of scheduling weights based on user terminal characteristics, QoS requirements, and channel conditions before actual resource allocation. This preliminary action provides accurate user need assessment while maintaining computational efficiency through structured weight calculation methods that can be pre-computed or quickly updated.
Data Source
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AI summary
A scheduler performs per sub-band prioritization allocation of sub-bands to user terminals to generate a pre-allocation schedule. The prioritization is performed independently for each sub-band. The resulting pre-allocation schedule indicates the relative priorities of the user terminals for each sub-band taking into account the channel conditions and specific needs of the user terminals. Based on the pre-allocation schedule, the scheduler can more efficiently allocate the radio resources to the user terminals based on the channel conditions and the specific needs of the user terminals. The scheduling approach is suitable for parallel computing architectures. The use of a parallel computing architecture increases MIPS (million instructions per second) capacity and allows faster scheduling in order to meet stringent real-time constraints.