Compact LFSR Timestamp Encoding for Bandwidth-Limited Packets
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Solution Overview
Problem
In resource-limited systems, there is a trade-off between communication speed and processor time for timestamping, and larger LFSR states are unsuitable for timestamping due to limited space in smaller data packets, affecting communication bandwidth.
Innovation Solution
A compact timestamping method using a linear feedback shift register (LFSR) encoder and decoder that reduces the LFSR state size by sampling and XOR operations, allowing for efficient recovery of tick counts from shorter LFSR state samples, and utilizing a binary mask and run-length encoding to simplify hardware and software implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a larger LFSR state is used for timestamping, then the timestamp period is extended, but the communication bandwidth is reduced due to limited space in smaller data packets
Solution Approach 1:
The LFSR state is segmented into multiple individual bit transmissions rather than transmitting the entire state at once. Each bit is transmitted separately over time, allowing the system to achieve extended timestamp periods while using minimal bandwidth per transmission cycle.
Solution Approach 2:
The system uses periodic transmission of individual LFSR bits at regular intervals. This periodic action allows the timestamp period to be extended beyond the packet transmission interval, enabling long timestamp periods while maintaining efficient bandwidth utilization through sparse, periodic transmissions.
2Measurement precision
If more bytes are used for timestamping, then the timestamp precision is improved, but fewer bytes are available for actual data, affecting communication bandwidth
Solution Approach 1:
The timestamp precision is achieved through segmentation of the LFSR state into individual bits transmitted periodically. This allows high precision timestamps to be constructed from multiple small transmissions rather than requiring large contiguous data blocks, thereby preserving communication bandwidth for actual data payload.
Solution Approach 2:
The LFSR acts as an intermediary that transforms a compact state into extended timestamp information. By transmitting individual LFSR bits periodically and reconstructing the state at the receiver, the system achieves high timestamp precision without directly transmitting large timestamp values, thus conserving bandwidth.
3Productivity
If there is a gap in LFSR state samples, then the communication efficiency is improved, but the LFSR state recovery becomes more difficult
Solution Approach 1:
The system incorporates feedback mechanisms where the receiver attempts to recover the LFSR state from received bits and uses this recovered state to predict and verify subsequent transmissions. This feedback loop allows the system to tolerate gaps in sampling while maintaining reliable state recovery, as the feedback information helps bridge missing samples.
Solution Approach 2:
The system performs preliminary LFSR state recovery attempts using available samples before encountering gaps. By establishing the state as early as possible and using it to predict future states, the system prepares for potential gaps in advance, enabling more robust recovery even when samples are missing.
Data Source
AI summary
Compact timestamps and related methods, systems and devices are described. An encoder is configured to generate compact timestamps of the disclosure by sampling states of linear feedback shift registers (LFSRs). A decoder may be configured to determine timing information responsive to the compact timestamps.