为保证船用网络通信的稳定性、可靠性,避免通信过程中数据丢失,研究5G通信技术的船用网络防拥塞机制。该机制结合船用网络数据传输场景的数据传输情况,分析其拥塞原因,引入5G技术设计和规划船用网络结构,计算网络链路带宽和资源分配情况,依据该计算结果,通过节点-链路控制方法计算网络中和链路关联节点的平均功率,依据计算结果对节点进行判定,再依据资源分配策略进行资源分配,以此有效降低船用网络的拥塞现象。测试结果表明,该机制应用后,船用网络的拥塞窗口大小明显降低,最大值仅为151.6 kB;并且网络碰撞概率值均在0.15以下,依旧满足期望应用标准,可有效处理船用网络防拥塞情况。
In order to ensure the stability and reliability of marine network communication and avoid data loss during the communication process, the anti congestion mechanism of 5G communication technology for marine networks is studied. This mechanism combines the data transmission situation of the marine network data transmission scenario, analyzes the reasons for congestion, introduces 5G technology to design and plan the marine network structure, calculates the network link bandwidth and resource allocation, and based on the calculation results, calculates the average power of the network and link associated nodes through the node link control method. Based on the calculation results, the nodes are judged, and then resource allocation is carried out according to the resource allocation strategy, effectively reducing the congestion phenomenon of the marine network. The test results show that after the application of this mechanism, the congestion window size of the marine network is significantly reduced, with a maximum value of only 151.6 Kbytes; And the network collision probability values are all below 0.15, still meeting the expected application standards and effectively handling the anti congestion situation of marine networks.
2024,46(16): 166-169 收稿日期:2024-02-13
DOI:10.3404/j.issn.1672-7649.2024.16.028
分类号:TP391
基金项目:2023中青年教师项目(2023KY2114)
作者简介:陶华宁(1984 – ),女,高级工程师,研究方向为计算机网络技术、网络安全技术及通信技术
参考文献:
[1] 陈立家, 周为, 许毅, 等. 一种基于SDN的多约束无人船网络传输路由算法[J]. 中国舰船研究, 2022, 17(4): 107-113.
CHEN Lijia, ZHOU Wei, XU Yi, et al. Multi-constrained unmanned surface vessel network transmission routing algorithm based on SDN[J]. Chinese Journal of Ship Research, 2022, 17(4): 107-113.
[2] 胡思尧, 杨柳涛. 集装箱船LoRa无线网络控制策略的设计及优化[J]. 中国航海, 2023, 46(3): 111-117.
HU Siyao, YANG Liutao. Design and optimization of LoRa wireless network control strategy for container ship[J]. Navigation of China, 2023, 46(3): 111-117.
[3] 陈世河, 徐彦彦, 潘少明. 基于深度强化学习的无线自组网拥塞控制性能提升方法[J]. 计算机应用研究, 2023, 40(7): 2138-2145.
CHEN Shihe, XU Yanyan, PAN Shaoming. Method of improving performance of congestion control in wireless Ad hoc network based on deep reinforcement learning[J]. Application Research of Computers, 2023, 40(7): 2138-2145.
[4] 唐婧壹, 唐杰. 基于最小流量的宽带电子设备通信容量分配算法设计[J]. 电子器件, 2023, 46(6): 1615-1620.
TANG Jingyi, TANG Jie. Design of communication capacity allocation algorithm for broadband electronic equipment based on minimum traffic[J]. Chinese Journal of Electron Devices, 2023, 46(6): 1615-1620.
[5] 李信, 李勇军, 赵尚弘. 基于能量效率的星地NOMA网络功率分配算法[J]. 电子学报, 2023, 51(5): 1310-1318.
LI Xin, LI Yongjun, ZHAO Shanghong. Power allocation in satellite-terrestrial NOMA network based on energy efficiency[J]. Acta Electronica Sinica, 2023, 51(5): 1310-1318.