Neural Network Input Sizing for Adaptive Wireless Decoding
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing the complexity of neural network-based communications due to varying channel conditions, leading to resource wastage and decoding failures.
Innovation Solution
The system employs an indication of input size and structure for a transmitting-side neural network, allowing dynamic adjustment of complexity based on channel conditions, thereby optimizing resource usage and improving decoding success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed-complexity neural network is used for wireless communication, then device simplicity is maintained, but resource wastage occurs under varying channel conditions
Solution Approach 1:
The patent implements dynamic neural network complexity adjustment by allowing the transmitting device to indicate different input sizes and structures based on current channel conditions. The receiving device adapts its neural network processing accordingly, transforming a static system into a dynamic one that optimizes resource consumption while maintaining communication reliability.
Solution Approach 2:
The patent changes the parameters of the neural network input (size and structure) based on channel conditions. By varying these parameters dynamically, the system adjusts the computational complexity and resource consumption to match the actual communication environment, preventing both resource wastage and decoding failures.
2Reliability
If neural network complexity is increased to improve decoding success, then reliability improves, but computing and power resource consumption increases
Solution Approach 1:
The system dynamically adjusts neural network complexity based on real-time channel conditions. When channel conditions are poor, higher complexity is applied to improve decoding success. When conditions are good, lower complexity suffices, reducing computing and power consumption. This dynamic adaptation resolves the contradiction between reliability and resource usage.
Solution Approach 2:
The patent changes the input size and structure parameters of the neural network to control its effective complexity. By adjusting these parameters according to channel quality, the system achieves the minimum necessary computational effort for reliable decoding without unnecessary resource consumption.
3Reliability
If neural network input size is increased to handle poor channel conditions, then decoding reliability improves, but processing time and computational load increase
Solution Approach 1:
The patent implements dynamic adjustment of neural network input size based on channel conditions. During poor channel conditions, larger input sizes are used to improve decoding reliability. During good conditions, smaller input sizes reduce processing time. This temporal variation in processing requirements resolves the contradiction between reliability and time loss.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of one or more of an input size or an input structure for a transmitting-side neural network, wherein the input structure indicates one or more of non-zero values or locations for the non-zero values within an input, having the input size, to the transmitting-side neural network. The UE may communicate with a base station based at least in part on the indication of the one or more of the input size or the input structure of the transmitting-side neural network. Numerous other aspects are provided.