Underwater Communication Mode Selection for Range-Stealth Tradeoffs
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Solution Overview
Problem
Existing underwater communication technologies face challenges in maximizing effective transmission range, deployment efficiency, energy consumption, communication sensitivity to obstacles, and environmental compatibility, leading to operational contradictions that require a compromise in communication mode selection.
Innovation Solution
A method and device using a neural network-based system that dynamically selects between acoustic, electromagnetic, and optical communication modes, considering environmental parameters, channel characteristics, and network feedback to optimize transmission criteria such as stealth, range, data rate, and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If acoustic communication mode is used to maximize transmission range, then effective transmission range is improved, but energy consumption increases and detection probability by fauna increases
Solution Approach 1:
The system dynamically adapts communication mode selection based on real-time environmental conditions (temperature, salinity, depth, fauna presence) rather than using a fixed mode. The neural network continuously adjusts between acoustic, electromagnetic, and optical modes to optimize the range-energy tradeoff according to current ocean conditions.
Solution Approach 2:
The system changes operational parameters by selecting different communication modes (acoustic, electromagnetic, optical) with distinct transmission characteristics. Each mode offers different range and energy consumption profiles, allowing the system to adjust parameters dynamically based on environmental feedback and transmission requirements.
2Length of stationary object
If acoustic communication mode is used to maximize transmission range, then effective transmission range is improved, but detection probability by third party or fauna increases
Solution Approach 1:
The system introduces electromagnetic and optical communication modes as intermediary alternatives to acoustic communication. These alternative modes can achieve transmission objectives with lower detection probability by fauna and third parties, especially in environments where acoustic signals would be highly detectable.
Solution Approach 2:
The system changes the physical parameter of signal type (acoustic to electromagnetic/optical) to alter detection characteristics. Different communication modes have different detectability profiles, allowing the system to select parameters that minimize detection while maintaining effective transmission range.
3Productivity
If optical communication mode is used to maximize data rate, then emission duration is reduced, but transmission reliability deteriorates due to environmental sensitivity
Solution Approach 1:
The system dynamically switches between optical and other communication modes based on real-time environmental conditions such as water turbidity, temperature, and salinity. When optical conditions are favorable, the system uses optical mode for high data rate transmission; when conditions deteriorate, it transitions to more reliable acoustic or electromagnetic modes.
Solution Approach 2:
The system uses sensor feedback from the environment (turbidity, temperature, salinity measurements) to adjust communication mode selection. This feedback mechanism allows the system to maintain transmission reliability by avoiding optical communication when environmental conditions indicate poor optical channel quality.
4Object-affected harmful factors
If electromagnetic communication mode is used to minimize detection probability, then environmental compatibility is improved, but transmission range is reduced
Solution Approach 1:
The system dynamically adjusts communication mode based on transmission distance requirements and environmental conditions. For short-range, low-detection applications, electromagnetic mode is selected; for long-range transmissions where detection risk is lower or acceptance is higher, acoustic mode may be used to achieve the required range.
Solution Approach 2:
The system changes the fundamental transmission parameter (signal type) to match the operational requirements. Electromagnetic signals provide low detectability for short-range communications, while acoustic signals provide extended range when detection constraints are less critical or can be managed through other means.
5Adaptability or versatility
If multiple communication modes are available to optimize different criteria, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system employs an autonomous neural network that automatically selects the optimal communication mode based on environmental sensor data and transmission requirements. This self-service approach eliminates the need for complex manual control systems or centralized decision-making, allowing the system to manage its own complexity through intelligent automation.
Solution Approach 2:
The system replaces complex mechanical or algorithmic decision-making systems with a neural network-based intelligent system. The neural network processes environmental inputs and automatically determines optimal communication mode selection, substituting what would otherwise require complex control logic with a more adaptable and easier-to-manage intelligent system.
Data Source
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AI summary
The present invention relates to a method of determining a preferred communication mode and communication parameter for an underwater data transmission to be sent between transceiver nodes, using either one of at least a first communication mode and a second communication mode, being two different modes out of the group of an acoustic communication mode, an electromagnetic communication mode, and an optical communication mode. The first transceiver node is connected to a sensor unit configured to measure at least one environmental parameter. The method comprises the step of, using a neural network, determining a preferred communication mode for the transmission and at least one transmission parameter. Input to the neural network is based on: at least a first measurement data value of the environmental parameter from the sensor unit; a communication channel characteristic for each of the at least two communication modes for a communication channel between the transceiver nodes; and a network characteristic based on feedback information from one or more node in the underwater communication network.