CBS-Aware RAN Scheduling for Distributed TSN Traffic
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Solution Overview
Problem
In fully distributed time sensitive networking (TSN) models, there is no central controller to manage resource reservation and scheduling, leading to challenges in optimizing radio resource utilization and efficiency due to the scheduling bottleneck at the RAN, especially in heavily loaded scenarios.
Innovation Solution
Implementing credit-based shaper (CBS) queue management with real-time assistant information (CBSAI) reporting from the UPF to the gNB for uplink TSC traffic and using CBS simulators in the gNB for downlink TSC traffic to optimize radio resource scheduling based on CBS behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fully distributed time sensitive networking model is used, then device autonomy and flexibility are improved, but radio resource scheduling efficiency deteriorates due to lack of central coordination
Solution Approach 1:
The patent introduces a credit-based shaper queue management mechanism that acts as an intermediary between distributed devices and the radio network. The credit-based shaper queue receives TSN packets, manages their transmission timing based on credit accumulation and depletion, and provides structured information to the radio scheduler. This intermediary structure enables distributed devices to maintain autonomy while achieving coordinated resource scheduling through the standardized credit-based interface.
2Measurement precision
If real-time credit-based shaper assistant information is reported to the network, then radio resource scheduling accuracy is improved, but signaling overhead and processing complexity increase
Solution Approach 1:
The patent transforms complex queue state information into simplified credit-based parameters (credit value, credit accumulation rate, queue depth) that are easier to process and transmit. By changing the representation parameters from detailed packet-level information to aggregated credit metrics, the system achieves accurate scheduling decisions while reducing signaling overhead and processing complexity at both device and network sides.
3Reliability
If credit-based shaper queue management is implemented for TSN packets, then real-time transmission performance is improved, but queue management complexity and computational requirements increase
Solution Approach 1:
The credit-based shaper queue operates autonomously using self-service mechanisms where credit accumulates automatically based on configured rates and depletes based on packet transmissions. The queue management logic is standardized and rule-based, requiring minimal external control or complex decision-making. This self-service operation ensures reliable real-time transmission performance while keeping device complexity manageable through automated, deterministic behavior.
Data Source
AI summary
Resource reservation and/or scheduling may take credit-based shaper algorithm/queue behavior, in time sensitive communication, into account. A base station may schedule resources for transmission of time sensitive networking packets based on the state of the credit based shaper queue. The state of the credit based shaper queue may be determined based on real-time credit-based simper assistant information, in an uplink scenario, or output of a simulator of a real-time credit-based shaper queue, in a downlink scenario, or a combination of real-time credit-based shaper assistant information and simulator output.