Wireless TSN Traffic Scheduling with RU and AC Queue Timing
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Solution Overview
Problem
Existing Wi-Fi systems struggle to guarantee deterministic packet delivery within bounded delay intervals for Time-Sensitive Networking (TSN) traffic in industrial IoT networks, as they lack dedicated resource allocation and are prone to latency and jitter due to non-dedicated radio resource allocation and link adaptation algorithms.
Innovation Solution
Implementing a TSN controller device that manages access points to multiplex resource units (RUs) and access categories (AC queues) to generate timing boundaries, using IEEE 802.1Qbv lookahead timing rules, and switch between RUs and AC queues to ensure timely delivery, with forward error correction and machine learning for optimal RU selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Wi-Fi systems use non-dedicated radio resource allocation, then resource utilization is improved, but latency and jitter increase making deterministic packet delivery impossible
Solution Approach 1:
The patent segments radio resources into dedicated TSN resource units and shared regular Wi-Fi resources. The TSN AP segments traffic into TSN packets (requiring deterministic delivery) and regular packets (flexible delivery), allocating specific RUs exclusively for TSN traffic to guarantee latency bounds while maintaining overall resource utilization.
Solution Approach 2:
The patent implements dynamic RU allocation where the TSN AP continuously monitors queue depths, packet arrival patterns, and timing requirements to adjust the size, number, and timing of dedicated TSN RUs in real-time, optimizing both deterministic delivery guarantees and resource utilization efficiency.
2Productivity
If link adaptation algorithms are used to optimize data rates, then throughput is improved, but timing predictability deteriorates causing jitter
Solution Approach 1:
The patent segments the data transmission path into a deterministic TSN portion and a best-effort Wi-Fi portion. Link adaptation and rate optimization are applied only to the Wi-Fi portion, while the TSN portion uses fixed, pre-negotiated rates to ensure timing predictability. This isolation allows throughput optimization without introducing jitter into time-sensitive streams.
Solution Approach 2:
The TSN AP acts as an intermediary that decouples the link adaptation process from TSN traffic. It receives TSN packets, applies deterministic scheduling and fixed-rate transmission, then handles Wi-Fi-specific optimizations for non-TSN traffic, preventing algorithm-induced jitter from affecting time-sensitive packets.
3Reliability
If dedicated resource units are allocated for TSN traffic, then deterministic delivery is improved, but resource allocation complexity increases
Solution Approach 1:
The patent merges TSN deterministic scheduling with Wi-Fi OFDMA resource allocation frameworks. By integrating TSN traffic handling into the existing Wi-Fi RU allocation structure rather than creating a separate mechanism, it achieves deterministic delivery while leveraging proven Wi-Fi resource management protocols to minimize complexity.
Solution Approach 2:
The TSN AP performs self-configuration of dedicated RUs based on incoming TSN traffic characteristics, timing requirements, and available channel conditions. Rather than requiring complex external configuration, the system automatically negotiates and allocates appropriate RUs, reducing operational complexity while maintaining deterministic guarantees.
4Measurement precision
If timing boundaries are generated through multiplexing RUs and AC queues, then scheduling precision is improved, but processing complexity increases
Solution Approach 1:
The patent implements periodic timing boundary generation aligned with Wi-Fi beacon intervals and OFDMA transmission opportunities. Rather than continuous complex calculations, the system generates timing boundaries at regular intervals based on pre-configured RU patterns and AC queue priorities, achieving precise scheduling with reduced processing overhead through periodic rather than continuous operation.
Data Source
AI summary
In one embodiment, a method includes receiving a request from an access point to transmit to a TSN data payload to a wireless TSN station, identifying resource units (RUs) in a downlink channel, each RU comprising a set of RU tones, identifying access category (AC) queues, multiplexing the RUs and AC queues to generate RU and AC queue pairs, generating timing boundaries of the pairs, wherein each timing boundary represents a combination of an average airtime of each RU and an average wait time of each AC queue for transmitting a size of the TSN data payload, iteratively validating the timing boundaries with a TSN lookahead time, and determining a first RU tone from a first RU associated with a first timing boundary less than the TSN lookahead time to transmit the TSN data payload in a first AC queue to the wireless TSN station.