Wireless Sensing Resource Allocation by Service State and KPI
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Solution Overview
Problem
Existing technologies lack effective methods for efficiently allocating wireless sensing resources to reduce interference in communication/sensing nodes, particularly in integrated sensing and communication systems, without adequate consideration for service requirements.
Innovation Solution
The proposed solution involves decomposing sensing service requirements into multiple states, associating each state with specific resource allocation strategies, and using state management information to determine wireless sensing resources based on sensing procedure and key performance indicators (KPIs), allowing for efficient resource utilization and interference reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If wireless sensing resources are allocated without considering service requirements, then resource allocation can be simplified, but resource utilization efficiency deteriorates and interference increases
Solution Approach 1:
The patent segments sensing service requirements into multiple states (e.g., initial state, sensing state, tracking state) with different KPI requirements. Each state is associated with specific resource allocation strategies, allowing the system to transition between states and allocate resources appropriately. This segmentation resolves the contradiction by maintaining simplicity through state-based classification while improving efficiency through targeted resource allocation for each state.
Solution Approach 2:
The patent implements dynamic resource allocation by associating different resource configurations with different sensing service states. As the sensing service transitions between states (e.g., from initial to sensing to tracking), the allocated resources dynamically adjust to match the current KPI requirements. This dynamic approach improves resource utilization efficiency while maintaining manageable complexity through state-driven automation.
2Reliability
If more wireless sensing resources are allocated, then sensing service KPIs can be improved, but interference to other communication/sensing nodes increases
Solution Approach 1:
The patent applies local quality by allocating different resource configurations to different sensing service states based on their specific KPI requirements. For example, the initial state may use fewer resources with relaxed KPIs, while the sensing state uses more resources with stricter KPIs. This localized resource allocation ensures that resources are concentrated where needed, improving sensing service reliability without unnecessarily increasing interference across all states.
Solution Approach 2:
The patent changes resource allocation parameters (e.g., bandwidth, power, time slots) based on the current sensing service state. When transitioning from initial state to sensing state, parameters such as allocated bandwidth and power are adjusted to meet the stricter KPI requirements of the sensing state. This parameter adaptation allows the system to improve sensing service reliability only when necessary, thereby minimizing overall interference.
3Productivity
If state-based resource allocation is implemented, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-defining multiple sensing service states with associated KPI requirements and resource allocation strategies before actual sensing operations begin. Each state is pre-configured with appropriate resource parameters, so when the sensing service transitions between states, the system can quickly apply the pre-determined resource allocation without complex real-time calculations. This preliminary preparation improves resource allocation efficiency while keeping the runtime system relatively simple.
Solution Approach 2:
The patent uses feedback mechanisms where the sensing service state and KPI performance are continuously monitored, and resource allocation is adjusted based on this feedback. The system evaluates whether current resource allocation meets the KPI requirements of the current state and makes adjustments accordingly. This feedback-driven approach improves resource allocation efficiency by responding to actual service needs while maintaining manageable complexity through automated control loops.
Data Source
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AI summary
Embodiments of the present disclosure relate to the technical fields of communications and sensing, and provide a sensing method and device. The method comprises: determining a wireless sensing resource for a sensing service, wherein the wireless sensing resource is associated with the state of a sensing process and/or a sensing service KPI; and receiving or sending a sensing signal or channel on the basis of the wireless sensing resource.