OFDM Signal Transmission Neural Network Parameter Allocation
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Solution Overview
Problem
Current wireless communication systems face challenges in minimizing learning latency and transmit power while ensuring data rates for all users or maximizing throughput, especially in broadband systems with frequency-selective channels, where existing designs for partitioned edge learning (PARTEL) struggle with joint computation-and-communication load allocation and synchronized updates.
Innovation Solution
The method involves determining optimal subcarrier, parameter, and power allocation for neural network model updates in a neural network-based wireless communication system, using techniques such as setting intermediate latency values, channel capacity calculations, and Lagrange multipliers to allocate model parameters and subcarriers efficiently across user equipment, ensuring balanced computation and communication loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partitioned edge learning is deployed in broadband OFDM systems, then distributed data access and computation efficiency are improved, but learning latency and power consumption increase due to joint computation-communication load allocation challenges
Solution Approach 1:
The patent dynamically adjusts computation load allocation and subcarrier assignment based on real-time channel conditions and computation capabilities. The base station optimizes the partitioning of neural network model parameters across user equipment, adapting the computation-communication balance dynamically to minimize learning latency while maintaining distributed computation efficiency.
Solution Approach 2:
The patent changes key parameters including subcarrier allocation, transmission power levels, and model parameter partitioning to optimize the trade-off between computation efficiency and learning latency. By adjusting these parameters based on channel state information and device capabilities, the system achieves efficient distributed learning with reduced latency.
2Power
If more user equipment participate in distributed neural network training, then computation resources are increased, but coordination complexity and synchronization overhead increase
Solution Approach 1:
The patent segments the neural network model parameters into distinct partitions that are allocated to different user equipment. Each device trains on its assigned parameter subset independently, reducing coordination complexity. The base station manages the segmentation and reassembles the trained parameters to form the updated global model.
Solution Approach 2:
The base station acts as an intermediary that coordinates the distributed training process. It allocates model parameter partitions to user equipment, receives trained parameters from multiple devices, and performs centralized aggregation. This intermediary role simplifies the coordination complexity by providing a central management point for the distributed computation resources.
3Productivity
If optimal subcarrier and power allocation is implemented, then throughput is maximized, but system complexity and computational overhead increase
Solution Approach 1:
The base station performs preliminary optimization of subcarrier allocation and power distribution before the distributed training begins. By pre-configuring the resource allocation based on channel state information and device capabilities, the system maximizes throughput without requiring complex real-time adjustments during training, thereby reducing system complexity.
Data Source
AI summary
In a neural network (NN) based wireless communication system, a BS determines, for an one-round latency T and an overall model size L of the NN model, i) Tu that makes {circumflex over (L)}*(Tu) larger than L and ii) Tl that makes {circumflex over (L)}*(Tl)<L; repeats determining {circumflex over (L)}*(Tm), {R*k,n}, {{circumflex over (L)}*k}, and {C*k,n} by using Tm=(Tu+Tl)/2 for k=1, K, and n=1, . . . N, while Tu is different from Tl; allocates NN model parameters to user equipments 1 to K based on {R*k,n}, {L*k}, and {C*k,n} determined based on Tm when Tu=Tl; and updates the NN model based on update results of the NN model parameters received from user equipments 1 to K.