Distributed DNN Coordination for Cell-Edge UE Communications
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining consistent quality of service due to varying signal strength and interference, particularly for user equipment (UE) located at the edge of a cell coverage area, which can degrade as the UE moves within the cell.
Innovation Solution
Implementing a deep neural network (DNN) processing for a user-equipment-coordination set (UECS) that dynamically adjusts end-to-end machine-learning configurations to improve signal quality by coordinating multiple UEs for joint transmission and reception, allowing for flexible adaptation to changing factors such as signal quality and device participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional base station configurations are used, then system complexity is low, but quality of service degrades for UEs at cell edge
Solution Approach 1:
The patent segments the QoS improvement task into multiple DNN modules distributed across different UEs in the coordination set. Each UE executes a portion of the overall DNN processing, dividing the complex task of maintaining edge UE connectivity into manageable segments that can be processed in parallel across multiple devices
Solution Approach 2:
The patent implements dynamic DNN configuration where the neural network parameters and processing pathways are continuously adapted based on real-time channel conditions, UE locations, and coordination set composition. This dynamic reconfiguration allows the system to optimize for current network states while maintaining manageable complexity through adaptive rather than static structures
2Measurement precision
If DNN processing is distributed across multiple UEs, then processing resolution improves, but coordination complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where UEs in the coordination set exchange channel state information, processing results, and performance metrics. This feedback enables each UE to adjust its local DNN processing parameters based on the overall system state, achieving high processing resolution through coordinated feedback-driven optimization rather than complex centralized control
Solution Approach 2:
The patent designs universal DNN modules that can be executed by any UE in the coordination set regardless of specific role or position. These multi-functional modules handle various processing tasks (signal enhancement, interference cancellation, channel estimation) in a unified manner, reducing coordination complexity by eliminating the need for specialized processing paths for different UE types
3Adaptability or versatility
If DNN configuration is dynamically adjusted, then adaptability to network conditions improves, but training and update time increases
Solution Approach 1:
The patent performs preliminary DNN training during periods of stable network conditions or using historical data, preparing optimized configurations in advance. When network conditions change, pre-trained modules can be quickly swapped or reconfigured using stored weight matrices and processing parameters, achieving rapid adaptation without requiring extensive real-time training that would cause significant time loss
Data Source
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AI summary
Techniques and apparatuses are described for deep neural network, DNN, processing for a user-equipment-coordination set, UECS. A network entity selects (910) an end-to-end, E2E, machine-learning, ML, configuration that forms an E2E DNN for processing UECS communications. The network entity directs (915) each device of multiple devices participating in a UECS to form, using at least a portion of the E2E ML configuration, a respective sub-DNN of the E2E DNN that transfers the UECS communications through the E2E communication link, where the multiple devices include at least one base station, a coordinating user equipment, UE, and at least one additional UE. The network entity receives (940) feedback associated with the UECS communications and identifies (945) an adjustment to the E2E ML configuration. The network entity then directs at least some of the multiple devices participating in a UECS to update the respective sub-DNN of the E2E DNN based on the adjustment.