Dynamic Channel Allocation for Low-Latency Mobile Edge Data
Solution Overview
Problem
In mobile edge computing, determining an effective channel for data marshalling and transmission is challenging due to unknown channel transfer rates and mutual coupling among nodes, leading to potential system instability and inefficiencies.
Innovation Solution
A computer-implemented method using Lyapunov optimization to identify and select an effective channel based on current capacity states and transmission schedules, considering both data marshalling and transmission scheduling, without requiring a-priori knowledge of capacity rates or interpretation probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic channel allocation is implemented without a-priori knowledge of capacity rates, then system adaptability is improved, but system stability deteriorates due to unknown channel transfer rates and mutual coupling among nodes
Solution Approach 1:
The patent implements feedback mechanisms where nodes continuously monitor channel capacity states and transmission schedules, using this information to dynamically adjust channel allocation decisions. The system observes actual transmission outcomes and uses this feedback to refine future allocation choices, enabling stable adaptation without requiring a-priori knowledge of channel characteristics.
Solution Approach 2:
The channel allocation system transitions from static pre-configured allocation to dynamic real-time allocation. The system continuously adapts channel assignments based on current capacity states and transmission schedules, allowing the allocation strategy to evolve with changing network conditions while maintaining stability through controlled adaptation mechanisms.
2Device complexity
If channel selection is made without a-priori knowledge of interpretation probabilities, then system complexity is reduced, but measurement precision deteriorates in identifying effective channels
Solution Approach 1:
The system employs self-service mechanisms where nodes autonomously identify effective channels through direct observation and learning from transmission outcomes. Rather than relying on external guidance or pre-computed probability distributions, the system uses its own transmission experiences to refine channel identification accuracy, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The system performs preliminary exploratory transmissions across multiple channels to gather capacity state information before making definitive allocation decisions. This preliminary action allows the system to build an accurate understanding of channel characteristics without requiring complex a-priori probability models, thereby reducing complexity while maintaining identification accuracy.
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can identify an effective channel on a transmission stream framework comprising a plurality of channels and nodes using current capacity states and current transmission schedules. Embodiments of the present invention can then determine whether to continue marshalling on a current channel or to marshal to the effective channel based on transmission demands between nodes and channel providing a minimum latency between available options within the transmission stream framework