Dynamic Resource Grid and ML-Based CSI Compression
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Solution Overview
Problem
Current wireless communication systems, such as those in the LTE and NR standards, face limitations in communication flexibility and efficiency, particularly in managing resources and channel access in multi-user and multi-service scenarios.
Innovation Solution
The proposed solution involves configuring a resource grid with adjustable subcarrier-spacing, OFDM symbol configuration, and cyclic prefix settings, allowing for dynamic resource allocation and improved channel access mechanisms, including the use of machine learning models for CSI compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed communication structure is used, then system simplicity is maintained, but communication flexibility and efficiency are limited
Solution Approach 1:
The patent implements dynamic resource allocation where the base station configures resource grids with adjustable subcarrier spacing and OFDM symbol structures based on traffic conditions. The system transitions from static to dynamic configuration, allowing resource parameters to change adaptively according to service requirements and channel conditions, thereby improving communication flexibility without permanently increasing system complexity.
Solution Approach 2:
The invention changes key communication parameters including subcarrier spacing configurations, cyclic prefix lengths, and OFDM symbol structures. By providing multiple configurable parameter sets that can be selected and adjusted based on service types and channel conditions, the system achieves enhanced adaptability while maintaining manageable complexity through standardized parameter options.
2Productivity
If traditional resource allocation is used, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent segments the resource grid into flexible units with configurable subcarrier spacing and OFDM symbol structures. This segmentation allows independent optimization of different resource portions for different services, improving overall resource utilization efficiency by allocating resources according to specific service requirements rather than using a one-size-fits-all approach.
Solution Approach 2:
The invention creates a universal resource allocation framework that can handle multiple service types (eMBB, mMTC, URLLC) within a single configurable structure. The same resource grid mechanism serves diverse functions by adjusting parameters, reducing the need for separate specialized systems and improving efficiency without proportionally increasing complexity.
3Loss of time
If standard channel access mechanisms are used, then system simplicity is maintained, but latency is increased
Solution Approach 1:
The patent implements preliminary configuration of resource grids and channel access parameters before actual data transmission. By pre-configuring resource allocations and access mechanisms based on predicted traffic patterns and service requirements, the system reduces waiting time and preparation delays, thereby lowering latency without significantly increasing operational complexity.
Data Source
AI summary
Terminal device receives a reference signal, a first machine learning model, and a second machine learning method, and transmits a CSI report. The terminal device compresses a CSI related data obtained from the reference signal into a first compressed CSI data and a second compressed CSI data, by the first machine learning model and the second machine learning method, respectively. The terminal device includes the first compressed CSI data and the second compressed CSI data in the CSI report.