Neural approximation of open-loop feedback rate control in satellite networks

Area of Science:

• Computer Science
• Electrical Engineering
• Operations Research

Background:

• Satellite networks face NP-Hard resource allocation challenges due to traffic load and fading variations.
• Traditional optimization methods using steady-state approximations have significant drawbacks.
• Discrete stochastic programming is complex for real-time satellite network management.

Purpose of the Study:

• To develop novel, efficient resource allocation strategies for satellite networks.
• To overcome limitations of existing optimization techniques, particularly those relying on closed-form solutions.
• To introduce a neural network-based approach for dynamic resource reallocation.

Main Methods:

• Investigated a gradient estimation method using infinitesimal perturbation analysis (IPA) on a relaxed continuous extension.
• Developed and analyzed an open-loop feedback control (OLFC) strategy for state-dependent reallocation.
• Employed a neural network-based technique to approximate the solution of the functional optimization problem.

Main Results:

• The IPA-based method provides gradient estimates without requiring closed-form performance measures or system state feedback.
• The OLFC strategy, approximated by neural networks, yields near-optimal reallocation strategies.
• The neural network approach significantly reduces real-time computational effort compared to IPA gradient descent.
• The proposed OLFC method avoids suboptimal transient periods inherent in IPA-based algorithms.

Conclusions:

• Neural network-based OLFC offers a computationally efficient and effective solution for dynamic satellite network resource allocation.
• The developed methods provide near-optimal performance, improving upon traditional approaches.
• This work advances the field of resource management in complex, stochastic communication systems.