Sequential Remote Encoder Training for Wireless Interference Management
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Solution Overview
Problem
Wireless communication networks face interference issues due to increased demand for mobile broadband access, leading to degraded performance on downlink and uplink transmissions.
Innovation Solution
Implement remote offline sequential network node encoder training using machine learning algorithms to enhance encoder training for wireless communication devices, enabling improved interference management and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more UEs access the wireless communication networks to meet growing demand for mobile broadband access, then network capacity and coverage are improved, but interference increases and network performance degrades
Solution Approach 1:
The patent changes the parameters of encoder training by implementing remote offline sequential training mechanisms. The base station trainer trains encoders offline using local channel state information, then provides trained encoder parameters to remote UE-side trainers. This parameter change approach allows the system to handle increased network capacity demands while managing interference through optimized encoding parameters rather than increasing transmission power or bandwidth.
2Adaptability or versatility
If encoder training is performed locally at each UE, then training adaptability is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent segments the encoder training process into two distinct phases: offline training at the base station and online fine-tuning at the UE. The base station performs the computationally intensive offline training using local channel state information, generating trained encoder parameters. These parameters are then transmitted to UEs for lightweight online adaptation. This segmentation reduces UE computational complexity while maintaining training adaptability through the two-stage approach.
Solution Approach 2:
The patent introduces trained encoder parameters as an intermediary between the base station trainer and UE-side encoder. Instead of requiring UEs to perform full encoder training computations, the base station generates trained parameters that serve as a mediator, enabling UEs to achieve adapted encoding performance with minimal computational effort through parameter transmission and lightweight fine-tuning.
3Loss of time
If encoder training is performed offline at base station, then processing time is reduced, but training information transmission overhead increases
Solution Approach 1:
The patent creates copies of trained encoder parameters from the base station trainer and transmits them to multiple UE-side trainers. Instead of requiring each UE to independently perform full offline training (which would save time but require significant information exchange), the system copies the trained parameters efficiently. This copying approach reduces training processing time while the parameter copy size is much smaller than the full training dataset, thereby managing information overhead.
Data Source
AI summary
This disclosure provides systems, methods, and devices for wireless communication that support remote offline sequential network node encoder training. In a first aspect, a method of wireless communication includes receiving, from a second network node, training information for a first encoder of the first network node, wherein the training information comprises first input information for a second encoder of the second network node and output information corresponding to the first input information, and training the first encoder of the first network node using the training information. Other aspects and features are also claimed and described.