Deep Reinforcement Learning for Wireless Power Control
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Solution Overview
Problem
Conventional wireless network communication systems struggle to balance power consumption and data transmission throughput, relying on heuristic and linear programming approaches that require detailed a posteriori models of the network environment, are not fully distributed, and fail to consider total power consumption.
Innovation Solution
The implementation of adaptably-applied reinforcement-based machine learning, specifically using deep reinforcement learning, to dynamically adjust transmit power levels in wireless networks, eliminating the need for a posteriori models and improving the balance between throughput and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If heuristic-based optimization approaches are used, then power consumption and throughput can be balanced, but detailed a posteriori models of the network environment are required, increasing system complexity
Solution Approach 1:
The patent replaces traditional heuristic optimization algorithms and linear programming methods with a deep reinforcement learning agent. This substitution eliminates the need for complex a posteriori models of network topology and environment, as the RL agent learns optimal power control policies through interaction with the environment, directly resolving the contradiction between achieving power-throughput balance and reducing system complexity
Solution Approach 2:
The invention changes the fundamental approach from model-based optimization to model-free reinforcement learning. By using a neural network to approximate the value function and policy, the system transforms the power control problem from requiring detailed environmental models to learning optimal behaviors through trial-and-error interaction, thereby reducing complexity while maintaining effectiveness
2Reliability
If conventional optimization approaches are used, then power control can be achieved, but they are not fully distributed and lack adaptability to actual network environment changes
Solution Approach 1:
The patent implements a distributed architecture where each node in the wireless network is equipped with its own deep reinforcement learning agent. Each agent independently learns and executes power control decisions for its associated transceiver based on local observations, eliminating the need for centralized control while improving adaptability to local environmental changes. This segmentation enables full distribution and enhanced reliability
Solution Approach 2:
Each node's RL agent autonomously learns optimal power control strategies through self-interaction with the network environment, without requiring external training or centralized coordination. The agents adapt to changing network conditions in real-time based on their own experiences and observations, providing self-service adaptability that enhances reliability while maintaining distributed operation
3Adaptability or versatility
If traditional power control methods are used, then basic transmission can be maintained, but they fail to consider total power consumption and do not provide training or adaptability
Solution Approach 1:
The deep reinforcement learning agent implements continuous feedback loops where it observes network state (including throughput and power consumption), receives rewards based on performance metrics, and adjusts power control policies accordingly. This feedback mechanism enables the system to adapt to environmental changes while optimizing throughput, resolving the contradiction between adaptability and productivity
Solution Approach 2:
The invention transforms static power control settings into dynamic, adaptive policies that continuously adjust transmit power based on real-time network conditions. The RL agent learns to modulate power levels dynamically in response to changing channel conditions, traffic patterns, and interference levels, thereby achieving both environmental adaptability and high throughput simultaneously
Data Source
AI summary
Wireless networks with plural nodes having a respective transceiver and a processor configured to, and methods to, obtain current state data, calculate a reward, store such state data and rewards in a collected parameters database, provide such current state data and data from such collected parameters database to a reinforced neural network, select an action using the reinforced neural network, and output the action to the respective transceiver so as to selective modify its transmit power level.