Packet-Delay Logical Channel Prioritization for Reliable Uplink
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently multiplexing data based on packet delay, leading to inefficiencies in data throughput, latency, and reliability, particularly in applications like extended reality and video traffic.
Innovation Solution
Implementing a prioritization procedure for logical channels based on packet delay, using parameter values configured through signaling, and employing machine learning for delay prediction to optimize data multiplexing, thereby improving data transmission reliability and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is multiplexed without considering packet delay, then device complexity is reduced, but data transmission reliability deteriorates
Solution Approach 1:
The system performs preliminary delay prediction using machine learning models before data multiplexing occurs. By predicting packet delays in advance and using these predictions to determine multiplexing parameters, the system ensures reliable data transmission without adding complex real-time decision-making procedures during the multiplexing process itself.
Solution Approach 2:
The patent introduces delay prediction values as an intermediary element between the data multiplexing function and the actual packet transmission. These prediction values, generated by machine learning models, serve as a mediator that guides the multiplexing procedure, improving reliability without requiring the multiplexing function itself to become more complex.
2Loss of time
If traditional multiplexing is used without delay consideration, then device complexity is reduced, but latency increases
Solution Approach 1:
The system performs delay prediction using machine learning models before the multiplexing procedure. By having delay predictions ready in advance, the system can make informed decisions about data prioritization and resource allocation, thereby reducing transmission latency without adding complex real-time calculations during the actual multiplexing process.
Solution Approach 2:
The patent replaces traditional rule-based or static priority multiplexing mechanisms with machine learning-based delay prediction. This substitution allows the system to dynamically adapt to varying network conditions and traffic patterns, optimizing latency performance without requiring complex manual configuration or rigid scheduling rules.
3Productivity
If packet delay-based multiplexing is implemented, then data throughput is improved, but use of energy increases
Solution Approach 1:
The system implements delay prediction and delay-based multiplexing selectively for specific data flows or logical channels that benefit most from this approach, rather than applying it universally to all traffic. This partial application improves throughput for critical data while limiting the energy overhead to only where necessary.
Solution Approach 2:
The patent dynamically adjusts multiplexing parameters based on predicted delay values, allowing the system to optimize throughput by changing how data is prioritized and scheduled. By modifying multiplexing behavior adaptively rather than using fixed parameters, the system achieves better throughput while avoiding the constant energy consumption that would result from always using the most aggressive optimization strategies.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A first device (e.g., a user equipment) may modify logical channel prioritization based on a packet delay. The first device may receive a set of parameter values for a logical channel prioritization procedure, then select a parameter value from the set based on a packet delay for data scheduled in an uplink grant. The first device may determine the parameter value, or the first device may select the parameter value based on signaling received from a second device (e.g., a base station). In some examples, the first device or the second device may determine the parameter value based on a delay prediction or machine learning.