Predictive MCS Table Selection for Spectral Efficiency Fluctuation
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Solution Overview
Problem
In telecommunications systems, incorrect or suboptimal assignment of modulation and coding scheme (MCS) tables to user equipment (UE) can lead to suboptimal data throughput, high error rates, unstable connections, and decreased system capacity due to inefficient resource utilization.
Innovation Solution
A system utilizing machine learning-based spectral efficiency prediction and fluctuation models to dynamically select an MCS table that maximizes spectral granularity, adapting to UE behavior and channel conditions, thereby ensuring optimal resource allocation and reducing retransmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a higher-order MCS table (e.g., 256 QAM) is assigned to a UE, then data throughput is improved, but error rate increases and connection stability deteriorates
Solution Approach 1:
The system dynamically switches between different MCS tables (256 QAM, 64 QAM, QPSK) based on real-time channel conditions and UE behavior patterns. The gNB adjusts the MCS table assignment adaptively rather than using a fixed high-order modulation, allowing the system to maximize throughput when conditions permit while maintaining reliability when channel quality degrades.
Solution Approach 2:
The system performs preliminary analysis of UE behavior patterns and channel conditions before assigning an MCS table. By predicting future channel quality based on historical data and current trends, the system pre-selects the appropriate MCS table to avoid subsequent errors and retransmissions, thereby maintaining both high throughput and reliability.
2Reliability
If a lower-order MCS table is assigned to a UE, then error rate is reduced and connection stability is improved, but data throughput decreases
Solution Approach 1:
The system dynamically adjusts MCS table selection based on real-time channel conditions. When channel quality is good, higher-order tables (256 QAM) are used to maximize throughput. When channel quality degrades, the system transitions to lower-order tables (64 QAM, QPSK) to maintain connection stability, thus optimizing the trade-off between throughput and reliability according to actual conditions.
Solution Approach 2:
The system changes the modulation and coding parameters by selecting different MCS tables based on channel conditions. The gNB adjusts key parameters such as modulation order and code rate dynamically, switching between 256 QAM, 64 QAM, and QPSK tables to match the current radio environment, thereby achieving optimal performance for each condition.
3Adaptability or versatility
If MCS table switching is performed frequently, then adaptability to channel conditions is improved, but spectral efficiency temporarily degrades during switching periods
Solution Approach 1:
The system performs preliminary prediction of channel conditions and UE behavior patterns before initiating MCS table switching. By analyzing historical data and current trends, the system forecasts future channel quality and pre-selects the optimal MCS table, reducing the need for frequent switching and minimizing spectral efficiency degradation during transitions.
Solution Approach 2:
The system uses feedback from channel quality indicators and UE performance metrics to guide MCS table selection. By continuously monitoring spectral efficiency and error rates, the gNB adjusts switching frequency and timing based on actual system performance, reducing unnecessary switches that would degrade spectral efficiency while maintaining adequate adaptability to channel changes.
4Quantity of substance
If the wrong MCS table is assigned to multiple UEs, then system capacity decreases due to congestion, but resource utilization efficiency is reduced
Solution Approach 1:
The system applies local quality optimization by customizing MCS table assignment for each UE based on its specific behavior patterns, channel conditions, and service requirements. Instead of using a uniform MCS table for all UEs, the gNB individually optimizes the assignment for each user, thereby maximizing overall system capacity and resource utilization efficiency while accommodating diverse UE needs.
Solution Approach 2:
The system performs preliminary analysis of each UE's behavior patterns and channel conditions before assigning MCS tables to multiple UEs. By predicting future resource requirements and channel quality for each user, the gNB pre-allocates appropriate MCS tables that prevent congestion and optimize resource utilization, thereby maintaining high system capacity and efficiency even with multiple concurrent users.
Data Source
AI summary
Spectral efficiency aware predictive modulation and coding scheme table selection (e.g., using a computerized tool), is enabled. For example, a method can comprise, based on spectral efficiency key performance indicator data, determining, by network equipment comprising a processor, a predicted spectral efficiency applicable to a user equipment, based on fluctuation key performance indicator data, determining, by the network equipment, predicted spectral efficiency fluctuation applicable to the user equipment, based on the predicted spectral efficiency and the predicted spectral efficiency fluctuation, determining, by the network equipment, a predicted spectral efficiency range, and based on the predicted spectral efficiency range, selecting, by the network equipment, a modulation and coding scheme table, for the user equipment, determined to modify a spectral granularity within the predicted spectral efficiency range to satisfy a spectral granularity criterion within the predicted spectral efficiency range.