Two-Sided Model Node Training via Segmentation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently training new nodes in a two-sided model, particularly when adding new User Equipment (UE) or base stations, as existing methods require retraining all nodes, leading to high complexity and overhead.
Innovation Solution
The proposed method involves sending a request for training information from a new node to existing network nodes, receiving and combining this information to train the new node's encoder or decoder model, allowing it to operate effectively within the existing two-sided model without requiring full retraining of all nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If full retraining of all nodes is performed when adding a new node to a two-sided model, then the new node can be integrated into the system, but the complexity and overhead of the training process increases significantly
Solution Approach 1:
The training process is segmented into two independent parts: (1) training the encoder model at the first node using received training information, and (2) maintaining the pre-trained decoder model at the second node. This segmentation allows the new node to be trained independently without requiring retraining of existing nodes, thereby reducing training complexity while enabling system adaptability.
Solution Approach 2:
The decoder model at the second node is pre-trained beforehand using available data. When a new first node needs to be added, the pre-trained decoder outputs are used as training targets for the new encoder, eliminating the need to retrain the decoder. This preliminary action significantly reduces the training overhead for integrating new nodes.
2Reliability
If all nodes are retrained when adding new User Equipment or base stations, then the model performance can be maintained, but the time and resources required for training increase
Solution Approach 1:
The training approach copies the functional behavior of the existing decoder model by using its pre-trained outputs as target labels for training the new encoder. Instead of retraining the entire two-sided model, the new encoder is trained to replicate the decoder's expected behavior, maintaining model performance while significantly reducing training time.
Solution Approach 2:
The essential training requirement is extracted and applied only where needed: the new first node's encoder model is trained using the pre-trained decoder's outputs, while the existing decoder model remains unchanged. This extraction of the minimal necessary training action maintains performance without the time cost of full retraining.
Data Source
AI summary
Various aspects of the present disclosure relate to training a node of a two-sided model. When a new user equipment (UE) side node is added to a two-sided model, training information representing a trained model is received (e.g., from the network side). An encoder model for the UE is trained based at least in part on the training information and, once trained, the UE transmits data encoded at the UE using the trained encoder model. When a new network (e.g., base station) side node is added to the two-sided model, training information associated with one or more UEs is received. A decoder model for the base station is trained based at least in part on the training information and, once trained, the base station uses the trained decoder model to decode data encoded at and received from UE.