An explicit congestion notification (ECN)-based distributed transport protocol,ARROW-WTCP (AcceleRate tRansmission towards Optimal Window size TCP for Wireless network),was proposed.The ARROW-WTCP enables feasible dep...An explicit congestion notification (ECN)-based distributed transport protocol,ARROW-WTCP (AcceleRate tRansmission towards Optimal Window size TCP for Wireless network),was proposed.The ARROW-WTCP enables feasible deployment of ARROW-TCP from wired to wireless networks by providing a joint design of source and router algorithms.The protocol obtains the actual capacity of the wireless channel by calculating the queue variation in base station (BS) and adjusts the congestion window by using the feedback from its bottleneck link.The simulation results show that the ARROW-WTCP achieves strong stability,max-min fairness in dynamic networks,fast convergence to efficiency without introducing much excess traffic,and almost full link utilization in the steady state.It outperforms the XCP-B (eXplicit Control Protocol Blind),the wireless version of XCP,in terms of stability,fairness,convergence and utilization in wireless networks.展开更多
In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed...In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.展开更多
基金Projects(60873265,60903222) supported by the National Natural Science Foundation of China Project(IRT0661) supported by the Program for Changjiang Scholars and Innovative Research Team in University of China
文摘An explicit congestion notification (ECN)-based distributed transport protocol,ARROW-WTCP (AcceleRate tRansmission towards Optimal Window size TCP for Wireless network),was proposed.The ARROW-WTCP enables feasible deployment of ARROW-TCP from wired to wireless networks by providing a joint design of source and router algorithms.The protocol obtains the actual capacity of the wireless channel by calculating the queue variation in base station (BS) and adjusts the congestion window by using the feedback from its bottleneck link.The simulation results show that the ARROW-WTCP achieves strong stability,max-min fairness in dynamic networks,fast convergence to efficiency without introducing much excess traffic,and almost full link utilization in the steady state.It outperforms the XCP-B (eXplicit Control Protocol Blind),the wireless version of XCP,in terms of stability,fairness,convergence and utilization in wireless networks.
基金Foundation item： Projects（61102106, 61102105） supported by the National Natural Science Foundation of China Project（2013M530148） supported by China Postdoctoral Science Foundation Project（HEUCF120806） supported by the Fundamental Research Funds for the Central Universities of China
文摘In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.
