Machine Learning Packet Buffering for Lower Wireless Latency
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing packet buffering, particularly in scenarios with varying latency and reliability requirements, leading to memory congestion and increased end-to-end latency due to fixed timer durations.
Innovation Solution
Implementing machine learning algorithms to dynamically determine packet buffering durations, allowing for early flush of buffers and optimized packet handling based on historical data, configuration parameters, and channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed timer durations are used for packet buffering, then packet handling is simplified, but memory congestion and end-to-end latency increase
Solution Approach 1:
The patent applies dynamics by transitioning from fixed timer durations to dynamic timer adjustments based on network conditions. The system continuously monitors packet loss rates, throughput, and buffer occupancy, then adapts buffer flushing timers accordingly. This allows the buffering mechanism to respond to changing network states, reducing latency when conditions permit while maintaining reliability when network conditions deteriorate.
Solution Approach 2:
The patent implements parameter changes by modifying buffer flushing timer values based on observed network performance metrics. When packet loss rates are low and throughput is high, the system reduces timer durations to minimize latency. Conversely, when network conditions worsen, timer durations are extended to maintain packet delivery reliability. This dynamic parameter adjustment resolves the contradiction between simplified handling and reduced latency.
2Reliability
If longer buffer durations are used, then packet delivery reliability is improved, but memory overhead increases
Solution Approach 1:
The system dynamically adjusts buffer durations based on real-time network condition monitoring. Rather than using static long buffer durations that consume excessive memory, the system extends buffers only when network conditions indicate potential packet loss risks. When network conditions are stable and reliable, buffer durations are reduced, thereby minimizing memory overhead while maintaining packet delivery reliability when needed.
Solution Approach 2:
The buffering system performs self-service by automatically monitoring its own performance metrics (packet loss rates, buffer occupancy levels) and adjusting its behavior accordingly. The system self-regulates buffer durations based on observed conditions, extending buffers only when necessary to maintain reliability, thus avoiding unnecessary memory consumption while ensuring packet delivery when network conditions warrant it.
3Loss of time
If dynamic buffer adjustments are implemented, then latency is reduced, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring network performance metrics including packet loss rates, throughput, and buffer occupancy. This feedback information drives dynamic adjustments to buffer flushing timers. The system uses observed packet loss patterns and network conditions to intelligently adjust buffer durations, reducing latency when conditions permit while automatically adapting to changing network states without requiring complex manual configuration.
4Adaptability or versatility
If machine learning algorithms are used for buffer management, then buffering strategy adaptability is improved, but computational overhead increases
Solution Approach 1:
The system employs machine learning to dynamically change buffer management parameters based on observed network conditions. The ML algorithm analyzes historical and real-time network metrics to predict optimal buffer flushing timings and durations. This approach provides high adaptability to varying network conditions and traffic patterns while managing computational overhead through efficient model selection and incremental learning strategies that balance accuracy with processing requirements.
Data Source
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AI summary
Certain aspects of the present disclosure provide techniques for packet buffering. A method that may be performed by a receiving node includes dynamically determining one or more time durations to buffer packets. The one or more time durations can be different than a time duration of a configured timer for buffering the packets. The receiving node may input one or more parameters to a machine learning algorithm and obtain, as output of the machine learning algorithm based on the input one or more parameters, one or more time durations to buffer packets. The receiving node buffers packets for the determined one or more time durations. The receiving node may use machine learning to dynamically determine the one or more time durations to buffer packet. The buffering may be at a radio link control (RLC) reassembling buffer and/or a packet data convergence protocol (PDCP) buffer.