Adaptive Receiver Signal Processing for Energy and Delay Tradeoffs
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Solution Overview
Problem
Baseline receiver methods in wireless communication networks have fixed complexity that does not adapt to signal quality or hardware quality, leading to low energy efficiency, high processing delay, and reduced battery life in downlink scenarios and increased size and weight in uplink scenarios.
Innovation Solution
Adaptive signal processing capability is implemented by a transmitter and receiver node to adjust based on estimated signal quality, hardware quality, and energy source status, using neural network models to optimize complexity and processing delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed complexity receiver methods are used, then device structure is simple, but energy efficiency is low and processing delay is high
Solution Approach 1:
The receiver dynamically adapts its signal processing capability based on estimated signal quality, hardware quality, and energy source status. The complexity of the receiver is adjusted in real-time by selecting different neural network models or configurations, transforming a static system into a dynamic one that optimizes energy efficiency according to current operating conditions.
Solution Approach 2:
The receiver changes operational parameters such as neural network model selection, processing depth, and algorithm complexity based on estimated signal quality and energy status. By varying these parameters adaptively, the system achieves better energy efficiency without requiring a completely different device structure.
2Device complexity
If fixed complexity receiver methods are used, then device structure is simple, but processing delay is high
Solution Approach 1:
The receiver dynamically adjusts its processing capability to match signal conditions, enabling faster processing when signal quality is good and reducing unnecessary processing steps. This dynamic adaptation directly reduces processing delay while maintaining simple device structure.
Solution Approach 2:
The receiver performs partial signal processing based on estimated quality - using simplified processing for good signal conditions and more extensive processing only when necessary. This avoids excessive processing actions that would increase delay while maintaining adequate performance.
3Use of energy by moving object
If adaptive signal processing capability is implemented, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The receiver implements a universal architecture that can operate in multiple modes with different complexity levels. By using a family of neural network models with varying complexities, the system achieves multi-functionality - adapting to different energy and signal conditions without requiring entirely separate hardware for each mode.
Solution Approach 2:
The adaptive receiver structure uses nested or hierarchical neural network models where simpler models are embedded within or can be selected from more complex models. This nesting allows the system to achieve adaptive energy efficiency while keeping the overall device complexity manageable through structured organization.
4Use of energy by moving object
If higher signal quality estimation and adaptation is performed, then energy efficiency is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary estimation of signal quality, hardware quality, and energy status before selecting the appropriate processing mode. This preliminary action allows the receiver to avoid computationally intensive operations when they are not needed, improving energy efficiency while keeping actual computational complexity low during operation.
Solution Approach 2:
The receiver performs computational adaptation actions only to the extent necessary based on estimated conditions. For good signal quality, partial or simplified adaptation is performed, avoiding excessive computational complexity while still achieving energy efficiency improvements through selective optimization.
Data Source
AI summary
Embodiments herein relate to, for example, a method performed by a transmitter node (12) for adapting a signal processing capability of a receiver of a receiver node (10) in a communication network (1). The transmitter node (12) obtains an estimated signal quality of a signal, an indication of a hardware quality, a status of an energy source associated with the receiver, and/or a target KPI. The transmitter node (12) further initiates an adaption of the signal processing capability of the receiver based on the estimated signal quality, the indicated hardware quality, the status of the energy source, and/or the target KPI.