Satellite Terminal Agent Flow Control for Uplink Resource Allocation
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Solution Overview
Problem
Current satellite communication (SATCOM) systems face challenges in dynamically allocating resources efficiently due to tight integrations of remote terminals and satellite ground hubs, variable data transfer requirements, and messaging overheads, which are exacerbated by increasing traffic demands and queue backlogs, necessitating a more sophisticated approach to resource management.
Innovation Solution
The implementation of a SATCOM framework with distributed terminal agents that utilize a Minimal-Cost-Variance (MCV) control theory and Kalman state estimates for flow control, enabling intelligent decision-making based on current and future modem data rates and router queue sizes to manage resource allocation and quality of service, thereby optimizing uplink resource assignments and reducing packet loss and delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used in SATCOM systems, then system stability is maintained, but resource allocation efficiency deteriorates due to tight integrations and messaging overheads
Solution Approach 1:
The patent segments the centralized control function into distributed terminal agents that operate autonomously at each terminal. Each agent locally manages resource allocation decisions, eliminating the need for complex centralized coordination and messaging overhead. This segmentation directly improves resource allocation efficiency while reducing system integration complexity.
Solution Approach 2:
Terminal agents are equipped with learning capabilities that enable them to autonomously predict traffic demands and make resource allocation decisions without external intervention. The agents self-adjust their behavior based on observed patterns, eliminating dependency on centralized control mechanisms and reducing messaging overhead while maintaining high allocation efficiency.
2Ease of operation
If more automation is added to dynamic resource allocation, then operational complexity for operators is reduced, but queue backlogs and traffic demand challenges worsen
Solution Approach 1:
The patent implements feedback mechanisms where terminal agents continuously monitor queue backlogs and traffic demand patterns, using this information to adjust their resource allocation decisions. This closed-loop control enables automated queue management that maintains reliability while reducing operator workload, as the system self-corrects based on real-time feedback.
Solution Approach 2:
Terminal agents use learning algorithms to predict future traffic demands and proactively allocate resources before queue backlogs occur. By taking preliminary actions based on predicted trends rather than reacting to actual congestion, the system maintains reliable queue management while operating fully autonomously without operator intervention.
3Reliability
If iterative learning control is implemented in terminal routers, then quality of service and load balancing improve, but computational requirements and processing complexity increase
Solution Approach 1:
The patent implements iterative learning control that focuses on the most critical QoS parameters and traffic patterns rather than attempting to optimize all possible variables. The learning agents prioritize actions that provide the greatest QoS improvement, achieving reliable service quality while keeping processing complexity manageable through selective optimization.
Data Source
AI summary
A satellite communication framework includes a satellite system controller; at least one satellite transponder; and a plurality of remote terminals, each including a modem, a router, and a terminal agent. The terminal agent is configured to, based on a current allowable data rate and measurements of a current router queue size and a current router packet arrival rate, use a delayed uplink resource assignment for each modem and an MCV-based flow-control policy to forecast a future router queue size and a future router packet arrival rate and further update the delayed uplink resource request for a time after an uplink allocation delay. The modem is configured to communicate with the router and also with the satellite system controller through the satellite transponder, perform modulation and demodulation, and manage packet loss and delay according to the future router queue size and the future router packet arrival rate.