The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preempt...The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.展开更多
The space–air–ground information network(SAGIN)has been widely used due to its excellent performances including wide coverage and high flexibility.However,the dynamic network topology of SAGIN presents challenges fo...The space–air–ground information network(SAGIN)has been widely used due to its excellent performances including wide coverage and high flexibility.However,the dynamic network topology of SAGIN presents challenges for traditional protocols.The statistical priority-based multiple access(SPMA)control protocol has received widespread attention because it effectively allocates resources in networks with heterogeneous terminals and dynamic topology.However,the existing SPMA protocols suffer from issues like large errors and low prediction accuracy in channel load statistics.Therefore,this paper proposes an improved SPMA based on the bi-directional long short-term memory(BiLSTM)neural network.First,we analyze and correct errors in channel load statistics at the physical layer,then develop a BiLSTM-based channel load prediction model,and finally simulated the improved SPMA using Matlab.Experimental results show that the proposed channel load prediction model achieves good prediction accuracy,and the improved SPMA protocol markedly improves channel utilization,providing differentiated services for multi-priority businesses.展开更多
基金supported by national fundamental research key project (No. JCKY2017203B082)
文摘The Statistical Priority-based Multiple Access Protocol(SPMA)is the de facto standard for Tactical Target Network Technology(TTNT)and has also been implemented in ad hoc networks.In this paper,we present a non-preemptive M/M/1/K queuing model to analyze the performance of different priorities in SPMA in terms of average packet loss rate and delay.And based on this queuing model,we designed a percentile scoring system combined with Q-learning algorithm to optimize the protocol parameters.The simulation results show that our theoretical model is closely matched with the reality,and the proposed algorithm improves the efficiency and accuracy in finding the optimal parameter set of SPMA protocol.
基金funded by the National Natural Science Foundation of China Youth Fund(grant no.62203048)the National Natural Science Foundation of China(grant nos.62073039 and 62073040).
文摘The space–air–ground information network(SAGIN)has been widely used due to its excellent performances including wide coverage and high flexibility.However,the dynamic network topology of SAGIN presents challenges for traditional protocols.The statistical priority-based multiple access(SPMA)control protocol has received widespread attention because it effectively allocates resources in networks with heterogeneous terminals and dynamic topology.However,the existing SPMA protocols suffer from issues like large errors and low prediction accuracy in channel load statistics.Therefore,this paper proposes an improved SPMA based on the bi-directional long short-term memory(BiLSTM)neural network.First,we analyze and correct errors in channel load statistics at the physical layer,then develop a BiLSTM-based channel load prediction model,and finally simulated the improved SPMA using Matlab.Experimental results show that the proposed channel load prediction model achieves good prediction accuracy,and the improved SPMA protocol markedly improves channel utilization,providing differentiated services for multi-priority businesses.
