Distributed Deep Learning for Multi-Objective Optimization in Wireless Networks
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Solution Overview
Problem
Conventional multi-objective resource allocation techniques for Next-generation communication networks are centralized and iterative, making real-time solutions impossible in decentralized environments like Machine-type Communication systems without a centralized coordination mechanism.
Innovation Solution
A deep learning method for distributed multi-objective optimization using neural network modules that generate and exchange messages based on local observations and priority weights, enabling each base station to calculate a local solution that maximizes wireless network performance in real-time through cooperative optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized multi-objective resource allocation techniques are used, then optimization accuracy is improved, but real-time solution capability deteriorates
Solution Approach 1:
The centralized optimization problem is segmented into multiple distributed base stations, each independently calculating local solutions using neural network modules. This segmentation enables parallel computation across multiple nodes, achieving real-time solutions while maintaining optimization accuracy through distributed coordination mechanisms.
Solution Approach 2:
Neural network modules act as intermediaries that enable distributed base stations to exchange information and coordinate solutions. These modules facilitate real-time communication and cooperation among base stations, allowing the system to achieve centralized-level optimization accuracy through decentralized computation.
2Measurement precision
If centralized coordination mechanism is implemented, then resource allocation optimization is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
Each base station is equipped with neural network modules that enable it to independently calculate local solutions and make autonomous decisions. This self-service capability eliminates the need for complex centralized coordination mechanisms, reducing system complexity while maintaining resource allocation optimization through distributed intelligence.
Solution Approach 2:
The centralized coordination function is segmented and distributed to individual base stations. Each base station performs localized optimization tasks independently, transforming a complex centralized system into multiple simple autonomous units that collectively achieve the same optimization goals with reduced overall system complexity.
3Speed
If distributed computation is used, then real-time solution capability is improved, but coordination efficiency among base stations deteriorates
Solution Approach 1:
The neural network modules implement feedback mechanisms where base stations exchange information about their local solutions and observations. This feedback loop enables coordinated optimization across the distributed network, ensuring that real-time solutions maintain high coordination efficiency through continuous information sharing and joint optimization.
Solution Approach 2:
Neural network modules serve as intermediaries that streamline coordination between distributed base stations. These modules efficiently manage information exchange and cooperation protocols, minimizing coordination overhead and time loss while enabling real-time distributed computation and optimization.
Data Source
AI summary
A deep learning method for distributed multi-objective optimization, computer program, and apparatus therefor. The method, performed by a computing device, for computing a local solution of a multi-objective optimization problem includes generating a first message for cooperation with at least one counterpart computing device based on a local observation and a priority weight, transmitting the first message to the counterpart computing device, receiving a second message from the counterpart computing device, and calculating the local solution for the computing device based on the local observation, the priority weight, and the second message.