| 引用本文: | 原源,曹先彬,范梅梅.动态服务质量的多信道媒体接入控制传输机制[J].国防科技大学学报,2018,40(2):150-155.[点击复制] |
| YUAN Yuan,CAO Xianbin,FAN Meimei.Multi-channel MAC transmission mechanism based on dynamic QoS[J].Journal of National University of Defense Technology,2018,40(2):150-155[点击复制] |
|
| |
|
|
| 本文已被：浏览 637次 下载 604次 |
| 动态服务质量的多信道媒体接入控制传输机制 |
| 原源1,2, 曹先彬1, 范梅梅3 |
| (1. 北京航空航天大学 电子信息工程学院， 北京 100191;2.
2. 中国人民解放军95801部队， 北京 100843;3. 空军装备研究院六所， 北京 100081) |
| 摘要: |
| 针对航空自组网在高负载下的服务质量及时延问题，提出一种动态服务质量的多信道媒体接入控制传输机制。以多信道检测统计为平台，结合优先级机制，通过在高负载网络中适当遏制低优先级业务，并且进行网络流量优化，保证高优先级业务的低时延发送；同时利用流量预测模型估计网络流量，通过粒子群优化算法进行优化，寻找合适的优先级门限值，确保高优先级业务接入率。通过计算机仿真可知，所设计的动态服务质量的多信道媒体接入控制传输机制，可在大负载网络中动态控制信道的接入，保持良好的网络吞吐量，其高优先级业务接入率达到99%以上，能有效解决航空数据链网络高业务量导致的服务质量及时延问题。 |
| 关键词: 优先级 阈值 预测 粒子群算法 |
| DOI：10.11887/j.cn.201802024 |
| 投稿日期：2017-01-05 |
| 基金项目:国家自然科学基金青年科学基金资助项目（61401499） |
|
| Multi-channel MAC transmission mechanism based on dynamic QoS |
| YUAN Yuan1,2, CAO Xianbin1, FAN Meimei3 |
| (1. School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;2.
2. The PLA Unit 95801, Beijing 100843, China;3. No. 6 Institute, Air Force Equipment Research Institute, Beijing 100081, China) |
| Abstract: |
| Aiming at the QoS(quality of service) and delay problems of aeronautic Ad Hoc network under high load, a DQM transmission mechanism was proposed(dynamic QoS of multi-channel MAC). On the basis of multi-channel detection statistics and the priority mechanism, the DQM optimized the network traffic via shutting down the low-priority traffic in the high-load network timely to ensure the low-latency transmission of high-priority traffic. The traffic prediction model was used to estimate the network traffic. Combined the particle swarm optimization algorithm, the DQM could find the optimal priority threshold and guarantee the access rate of high priority services. Simulation results prove that the DQM maintains an optimized network traffic by adaptively controlling channel access in high-load network. Thus, the access rate of high-priority services reaches above 99%, which helps to solve the QoS and latency issues associated with high traffic in the air data link. |
| Key words: priority threshold prediction particle swarm optimization |
|
|
|
|
|
