Base Station Physical Layer Task Recovery Under Slot Overload
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Solution Overview
Problem
Base stations often fail to complete tasks within a time slot due to excessive workload, leading to performance degradation and potential crashes.
Innovation Solution
A self-recovering method for the physical layer of a base station that utilizes a task status vector table to identify unexecuted tasks and executes them in a load reduction mode over consecutive time slots, reducing workload and preventing crashes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the base station executes n tasks during each time slot in normal mode, then the productivity is improved, but the reliability deteriorates when excessive workload occurs
Solution Approach 1:
The base station dynamically switches between normal mode and load reduction mode based on task completion status. When tasks cannot be completed within a time slot, the system transitions to load reduction mode for the next p consecutive time slots, then returns to normal mode. This dynamic adaptation resolves the contradiction by adjusting operational intensity based on real-time workload conditions.
Solution Approach 2:
The invention changes the operational parameter (execution mode) from normal to load reduction when task completion fails. This parameter change allows the system to handle excessive workload by reducing task execution rate temporarily, preventing crashes while maintaining overall productivity through recovery to normal mode after p time slots.
2Productivity
If the base station operates in normal mode continuously, then the productivity is maintained high, but the harmful factors increase due to task accumulation and potential crashes
Solution Approach 1:
The invention converts the harmful effect of task accumulation into a beneficial trigger for mode switching. When task backlog occurs (harmful condition), the system automatically switches to load reduction mode, processes tasks at a reduced rate, and prevents system crash. This transforms the harmful backlog condition into a controlled state that ultimately protects system stability.
Solution Approach 2:
The system implements feedback by monitoring task completion status within each time slot. When tasks are not completed (detected condition), the system provides feedback by switching to load reduction mode. This feedback mechanism prevents harmful task accumulation from escalating to system crashes, resolving the contradiction between high productivity and harmful factors.
Data Source
AI summary
A processor of a base station apparatus executes steps of: (a) executing multiple first tasks, one by one, during a time period of one of m time slots in a normal mode in a thread pool; (b) when finishing executing the first tasks, recording first statuses of the first tasks into the task status vector table; (c) when finishing recording the first statuses of the first tasks, determining whether all of second statuses of second tasks of a previous kth time slot of the m time slots recorded in the task status vector table are executed statuses; if yes, returning to the step (a); if not, executing at least one of the second tasks of the previous kth time slot having an unexecuted status within next p consecutive time slots in a load reduction mode, and then returning to step (a); m, k, and p are positive integers. The processor executes the tasks in the load reduction mode for self-recovering so that the base station apparatus can avoid crashing.