ML-Based Uplink Control Encoding for CSI Feedback Overhead
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Solution Overview
Problem
Existing wireless communication networks face inefficiencies in managing uplink control signaling due to the high computational and resource demands of neural network-based autoencoders (AEs) for channel state information (CSI) feedback, leading to suboptimal precoding in Multi-User MIMO (MU-MIMO) operations and increased signaling overhead.
Innovation Solution
Implementing a dual-sided machine learning (ML) model architecture with an encoder at the user equipment (UE) and a decoder at the base station, where the base station broadcasts supported ML models, allowing UEs to select and potentially download matching models for efficient encoding and decoding of uplink control signaling, reducing overhead and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based autoencoders are used for encoding uplink control signaling, then CSI resolution is improved, but computational resource demands and signaling overhead increase
Solution Approach 1:
The dual-sided ML model is divided into an encoder part at the UE and a decoder part at the gNodeB, with each side containing multiple specialized models (e.g., AE-CSI, AE-BI, AE-PMI) that can be selectively activated based on channel conditions and requirements
Solution Approach 2:
The system dynamically selects and switches between different ML models based on real-time channel conditions, allowing the UE and gNodeB to adapt the encoding/decoding approach to current network state, optimizing the balance between CSI resolution and computational resources
2Adaptability or versatility
If multiple ML models are supported at the base station, then adaptability is improved, but device complexity increases
Solution Approach 1:
The gNodeB is designed with a universal capability to support multiple ML models (AE-CSI, AE-BI, AE-PMI) that can handle different types of uplink control signaling, allowing a single base station to adapt to various channel conditions and signaling requirements without requiring separate dedicated systems
Solution Approach 2:
The base station provides feedback information indicating which ML models are supported, enabling the UE to make informed selections about which models to use, creating a coordinated system that manages complexity through mutual awareness
3Adaptability or versatility
If UE downloads and stores multiple ML models, then adaptability is improved, but storage requirements and complexity increase
Solution Approach 1:
The UE is configured with a set of supported ML models in advance, allowing it to immediately utilize these models when connecting to a gNodeB without requiring real-time downloads or storage of multiple model variants
Solution Approach 2:
The system allows UEs to selectively activate and deactivate ML models based on current needs, potentially discarding unused models from memory while retaining the capability to load them when required, optimizing the balance between adaptability and storage usage
Data Source
AI summary
A wireless device (10) receives control data provided by a node (100) of the wireless communication network. The control data indicates a set of one or more supported machine-learning models for encoding and decoding of uplink control signaling from the wireless device (10) to the node (100). The wireless device (10) selects one or more of the machine-learning models from the set. Based on at least one of the one or more selected machine-learning models, the wireless device (10) encodes uplink control signaling from the wireless device (10) to the node (100).