Sidelink Coordination via Clustered and Peer-to-Peer Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems, particularly in 5G NR, face challenges in optimizing sidelink communication parameters among devices due to lack of coordination, leading to sub-optimal network performance and increased congestion.
Innovation Solution
Implementing federated learning (FL) architectures, including clustered and peer-to-peer approaches, to coordinate sidelink communication parameters using machine learning models, allowing UEs to form clusters or peer-to-peer networks for optimized parameter selection and reduced communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented to coordinate sidelink communication parameters, then packet reception rates and throughput are enhanced, but device complexity and computational overhead increase
Solution Approach 1:
The system segments the federated learning implementation into clustered FL and peer-to-peer FL architectures. In clustered FL, devices are grouped into clusters with cluster heads coordinating parameter updates, reducing individual device complexity. In peer-to-peer FL, devices directly exchange model updates with neighboring devices, distributing the computational load and avoiding centralized coordination overhead.
Solution Approach 2:
The federated learning system enables devices to autonomously train and update their own machine learning models using local sidelink communication data. Each device independently performs local model training and contributes updates to the global model, eliminating the need for centralized model training and reducing overall system complexity while improving packet reception rates through localized optimization.
2Productivity
If federated learning is implemented to coordinate sidelink communication parameters, then throughput is maximized, but use of energy increases due to distributed ML model training
Solution Approach 1:
The system divides the network into clusters for federated learning, where cluster heads aggregate model updates from member devices. This segmentation reduces the communication distance and number of transmissions required for each device, lowering energy consumption while maintaining high throughput through coordinated parameter optimization.
Solution Approach 2:
The federated learning implementation uses periodic model update cycles where devices perform local training and exchange updates at intervals. This periodic action allows devices to operate efficiently between updates using existing model parameters, reducing continuous energy consumption while maintaining optimized throughput through regular model improvements.
3Productivity
If clustered federated learning is used to ensure homogeneous computational resources, then training efficiency is improved, but device complexity increases
Solution Approach 1:
Devices are segmented into clusters based on computational resource characteristics and training data homogeneity. Cluster heads are selected from devices with representative computational capabilities, enabling efficient coordination of model training within homogeneous groups. This segmentation improves training efficiency by reducing variability in convergence rates while distributing coordination complexity to specific cluster heads rather than requiring all devices to handle full system complexity.
4Reliability
If peer-to-peer federated learning is implemented for distributed ML model training, then security and privacy are improved, but communication overhead increases
Solution Approach 1:
The peer-to-peer federated learning system segments the global model into local model components that are distributed across devices. Each device trains and maintains a local model version, exchanging only model updates rather than raw data or complete models with neighboring devices. This segmentation improves security and privacy by keeping sensitive data localized while reducing communication overhead through efficient update exchange protocols.
Data Source
AI summary
Aspects are provided which allow a UE to achieve sidelink parameter coordination through various signaling in clustered FL or peer-to-peer FL. The UE provides a first ML model information update and a measurement associated with the update. The UE obtains an aggregated ML model information update aggregating the first update with a second ML model information update of either a first network node in a FL cluster including the UE or a second network node in a second FL cluster. The UE provides a sidelink communication parameter in SCI to a network node, which parameter is an output of an ML model associated with the aggregated ML model information update. As a result, UEs may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency in communication.