UE Coordination Set Federated Learning for Edge Signal Reliability
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Solution Overview
Problem
The quality of wireless connections between user equipment (UE) and base stations varies due to factors like signal strength and bandwidth limitations, leading to degraded service as UE moves within a cell coverage area, particularly for UEs at the edge.
Innovation Solution
Implementing user equipment-coordination sets (UECS) for federated learning of deep neural networks (DNNs), where coordinating UEs share and aggregate updated ML configuration information to dynamically adjust DNNs based on current operating conditions, improving signal quality and reducing traffic with the base station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UEs at the edge of cell coverage area operate with base station, then service coverage is provided, but signal strength is weak and quality of service degrades
Solution Approach 1:
The patent combines multiple UEs into a UECS (User Equipment Coordination Set) where edge UEs with weak signals are grouped with other UEs. The coordinating UE aggregates signals and coordinates communication with the base station, effectively combining the weak signals from multiple UEs to achieve sufficient signal strength and improve quality of service for edge users.
Solution Approach 2:
The coordinating UE acts as an intermediary between base station and other UEs in the UECS. It receives and processes base station signals, then distributes them to member UEs, and aggregates uplink signals from member UEs before transmitting to the base station. This intermediary role strengthens the effective signal path for edge UEs without requiring direct strong connections to the base station.
2Reliability
If base station processes all UE communications, then centralized control is maintained, but network traffic increases and resources are consumed
Solution Approach 1:
The patent segments the network communication function by dividing UEs into UECS groups with a coordinating UE for each group. The coordinating UE handles local signal aggregation and coordination tasks, segmenting the overall network traffic into group-level communications rather than individual UE-to-base-station communications. This reduces total network traffic while maintaining centralized control through the base station's oversight of coordinating UEs.
Solution Approach 2:
The patent introduces a new dimension of coordination at the UE level by creating UECS hierarchies. Instead of a flat one-to-one UE-to-base-station communication model, it adds an intermediate coordination layer that operates in a different organizational dimension, allowing multiple UEs to be managed through a single coordinating UE, thereby reducing network traffic overhead.
3Stability of the object's composition
If DNNs are trained centrally at base station, then model consistency is maintained, but training data transmission requires high bandwidth and time
Solution Approach 1:
The patent applies local quality by allowing each UE in the UECS to perform federated learning using its own local data without transmitting the data to the base station. Each UE trains local model instances with locally available data, maintaining model updates locally while contributing to the overall federated learning process. This eliminates the need to transmit training data to the base station, saving time and bandwidth while still achieving model consistency through coordinated updates.
Solution Approach 2:
The patent uses copying by distributing the base DNN model to multiple UEs in the UECS, where each UE creates and trains local copies of the model using its own data. These local model copies are then aggregated through federated learning to update the base model. This copying approach allows parallel training across multiple devices without centralizing data, significantly reducing transmission time while maintaining model consistency through coordinated updates.
4Loss of information
If federated learning is performed peer-to-peer in UECS, then local data privacy is preserved, but coordination and aggregation require additional communication overhead
Solution Approach 1:
The coordinating UE serves as an intermediary that simplifies peer-to-peer federated learning coordination within the UECS. Instead of requiring complex direct peer-to-peer communication between all member UEs, the coordinating UE centralizes the aggregation function locally, collecting model updates from member UEs and managing the federated learning process. This intermediary approach preserves data privacy by keeping training local while reducing coordination complexity compared to fully distributed peer-to-peer approaches.
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for User Equipment-Coordination Set (UECS) Federated for Deep Neural Networks. A coordinating user equipment (UE) of the UECS communicates, to a second UE in the UECS and using one or more side links, one or more update conditions that indicate when to generate updated machine learning (ML) configuration information for one or more deep neural networks (DNNs) that are configured to perform some or all of a transmitter or a receiver processing functionality to process communications at the second UE. The coordinating UE receives, from the second UE over the one or more side links, one or more reports, each report including the updated ML configuration information determined by the second UE using a training procedure and input data local to the second UE.