Wireless Relay Node Network Throughput Optimization
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Solution Overview
Problem
Ad-hoc networks face challenges in maintaining stable network throughput due to dynamic environmental conditions and limited energy sources in relay nodes, leading to inefficient energy use and connectivity issues.
Innovation Solution
The method involves each relay node collecting state information about its relative distance and angle to neighboring nodes, using reinforcement learning to determine actions such as changing transmission range, moving, or rotating, and calculating rewards based on energy consumption and network throughput to optimize network configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If relay nodes continuously collect state information and perform reinforcement learning to optimize network configuration, then network throughput is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of relay node positions and transmission parameters through reinforcement learning. The system continuously learns optimal configurations based on changing network conditions, allowing the network to adapt dynamically while balancing throughput optimization with energy consumption constraints through the reward function design.
Solution Approach 2:
The patent changes key parameters including transmission range radius, relay node positions, and rotation angles based on learned policies. By adjusting these parameters dynamically through reinforcement learning, the system optimizes network throughput while the reward function ensures energy consumption remains within acceptable bounds.
2Reliability
If relay nodes move and change positions to optimize network connectivity, then network stability is improved, but energy consumption increases
Solution Approach 1:
The patent implements feedback through the reinforcement learning mechanism where relay nodes continuously monitor network state (connectivity, throughput, energy levels) and adjust their positions based on received rewards. This closed-loop control ensures that movement decisions are made only when they improve overall network performance while accounting for energy costs.
Solution Approach 2:
Relay nodes autonomously determine their own movement decisions based on local state information and learned policies without requiring centralized control. Each node independently optimizes its position to maintain network connectivity while minimizing unnecessary energy expenditure on movement.
3Productivity
If transmission range is increased to improve network coverage, then network throughput is improved, but energy consumption increases
Solution Approach 1:
The patent dynamically adjusts transmission range radius based on learned policies and current network conditions. Instead of maintaining a fixed or maximally extended transmission range, the system adaptively modifies transmission parameters to achieve sufficient coverage with minimal energy expenditure, balancing throughput requirements against energy consumption.
Data Source
AI summary
A method of building an ad-hoc network of a wireless relay node and an ad-hoc network system are disclosed. The method includes verifying state information representing a relative distance and angle to a neighboring node capable of receiving data when each of relay nodes transmits the data, determining an action representing a change over time of each relay node, determining, based on an amount of change in a network throughput determined according to the state information and an amount of energy consumption according to the action, a reward corresponding to the action, and building a network including a source node, a destination node, and a plurality of relay nodes by generating, based on a reward of each of the relay nodes, a policy that allows a cumulative reward to be maximized.