Queuing Over Ever-Changing Communication Scenarios in Tactical Networks

This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem <inline-formula><tex-math notation="LaTeX">$A$</tex-math><alternatives><mml:math><mml:mi>A</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq1-3005737.gif"/></alternatives></inline-formula>) through ever-changing network conditions (problem <inline-formula><tex-math notation="LaTeX">$B$</tex-math><alternatives><mml:math><mml:mi>B</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq2-3005737.gif"/></alternatives></inline-formula>) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem <inline-formula><tex-math notation="LaTeX">$A|B$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>A</mml:mi><mml:mo>|</mml:mo><mml:mi>B</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq3-3005737.gif"/></alternatives></inline-formula>). We introduce two stochastic models to create sequences of QoS-constrained messages (<inline-formula><tex-math notation="LaTeX">$A$</tex-math><alternatives><mml:math><mml:mi>A</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq4-3005737.gif"/></alternatives></inline-formula>) and to create ever-changing network conditions (<inline-formula><tex-math notation="LaTeX">$B$</tex-math><alternatives><mml:math><mml:mi>B</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq5-3005737.gif"/></alternatives></inline-formula>). In sequence, we sketch a control loop to shape <inline-formula><tex-math notation="LaTeX">$A$</tex-math><alternatives><mml:math><mml:mi>A</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq6-3005737.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$B\;$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>B</mml:mi><mml:mspace width="0.277778em"/></mml:mrow></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq7-3005737.gif"/></alternatives></inline-formula> to test our hypothesis using model <inline-formula><tex-math notation="LaTeX">$A|B$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>A</mml:mi><mml:mo>|</mml:mo><mml:mi>B</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq8-3005737.gif"/></alternatives></inline-formula>, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems <inline-formula><tex-math notation="LaTeX">$A$</tex-math><alternatives><mml:math><mml:mi>A</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq9-3005737.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$B$</tex-math><alternatives><mml:math><mml:mi>B</mml:mi></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq10-3005737.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$A|B$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>A</mml:mi><mml:mo>|</mml:mo><mml:mi>B</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="rigolinferreiralopes-ieq11-3005737.gif"/></alternatives></inline-formula>.

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