Link Adaptation Parameter Adjustment for Channel Load
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Solution Overview
Problem
Existing link adaptation methods in communication systems face challenges in achieving a balance between spectral efficiency and delay, particularly in multi-user systems where different traffic scenarios require tailored approaches, and current methods are not optimized for varying channel load states.
Innovation Solution
A method for optimizing link adaptation by updating parameters based on the load of data and control channels, determining the cell's state as Non Limited, Control Channel Limited, or Data Channel Limited, and adjusting the link adaptation accordingly to achieve a better trade-off between spectral efficiency and delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a too aggressive MCS is used to maximize spectral efficiency, then data rate is improved, but packet loss increases severely
Solution Approach 1:
The patent applies dynamics by making the MCS selection adaptive rather than fixed. The system dynamically adjusts the MCS based on real-time channel conditions, load state (CCL/DCL/NL), and historical performance data. This allows the system to optimize between data rate and packet loss by selecting the appropriate MCS level (aggressive, moderate, robust) according to current network conditions, resolving the contradiction between maximizing spectral efficiency and maintaining reliability.
Solution Approach 2:
The patent implements feedback mechanisms through multiple channels: HARQ feedback for immediate packet loss detection, BLER target adjustment based on actual performance, and load state feedback from the scheduler. This feedback loop enables the system to learn from past performance and adjust future MCS selections, thereby optimizing the trade-off between data rate and packet loss by continuously adapting to changing channel conditions.
2Reliability
If a too robust MCS is used to minimize packet loss, then reliability is improved, but spectral efficiency decreases and delay increases
Solution Approach 1:
The system dynamically adjusts MCS based on load state detection. When the cell is in CCL state (control channel limited), the system uses more robust MCS to ensure reliable control information delivery. When in DCL state (data channel limited), the system switches to more aggressive MCS to maximize spectral efficiency. This dynamic adaptation resolves the contradiction by selecting the appropriate robustness level according to current network conditions rather than using a fixed robust MCS.
Solution Approach 2:
The patent changes the MCS parameter dynamically based on load state and channel conditions. The system adjusts modulation order and coding rate parameters to match current network conditions, transitioning between aggressive, moderate, and robust MCS configurations. This parameter adaptation allows the system to optimize spectral efficiency when conditions permit while maintaining reliability when needed, resolving the contradiction between these two objectives.
3Reliability
If retransmissions are increased to improve reliability, then packet loss is reduced, but delay increases
Solution Approach 1:
The patent applies preliminary action by adjusting MCS before data transmission based on predicted load state and channel conditions. By pre-selecting an appropriate MCS level that accounts for potential retransmission needs, the system avoids the delay associated with failed transmissions and subsequent retransmissions. This proactive approach optimizes the trade-off between reliability and delay by preventing packet loss before it occurs rather than relying on post-transmission retransmission.
Solution Approach 2:
The system uses feedback from HARQ acknowledgments and BLER measurements to continuously refine MCS selection. This feedback mechanism allows the system to learn from actual transmission performance and adjust future MCS choices to minimize retransmission needs. By adapting MCS based on real performance data, the system reduces unnecessary retransmissions and the associated delay while maintaining adequate reliability.
4Productivity
If link adaptation is optimized for spectral efficiency, then data rate is improved, but delay increases in multi-user systems
Solution Approach 1:
The patent applies local quality by tailoring link adaptation parameters to specific local conditions within the cell. The system detects local load state (CCL, DCL, NL) and applies different MCS strategies to different users and channels based on their individual conditions. This localized adaptation allows the system to optimize spectral efficiency for individual users without imposing excessive delay, as each user receives customized MCS selection based on their specific channel conditions and queue status.
Solution Approach 2:
The system dynamically adjusts link adaptation behavior based on real-time load state detection and historical performance data. The MCS selection is made adaptive rather than static, allowing the system to optimize between spectral efficiency and delay based on current network conditions. This dynamic approach resolves the contradiction by enabling the system to prioritize spectral efficiency when conditions permit while switching to delay-optimal strategies when load conditions require it.
Data Source
AI summary
A method for performing link adaptation in association with scheduling a data channel for a cell in a base station of a cellular communication system, and an arrangement, suitable for performing such a method. The link adaptation method has link adaptation optimization purpose which is achieved by updating a link adaptation parameter on the basis of the load on the data channel and on the basis of the load of a control channel controlling the data channel in the cell. The method obtains a better trade-off between the spectral efficiency and the delay, due to re-transmissions.