莒南县巩固提升农村饮水安全的做法及成效

近年来,莒南县以巩固提升农村饮水安全为主线,聚焦发力、周密部署、狠抓落实,抓紧抓实这项民心工程,使农村饮水安全有效衔接脱贫攻坚成果和推进乡村振兴提供了有力支撑,取得较好成效。针对以后农村饮水安全工作提出了建议对策。

一种基于计算资源度的星间路由协议

随着发射技术的提升和星载任务需求的扩大,低轨互联网星座逐渐成为航天产业的研究热点。在网络层进行算力资源感知有助于构建最短计算时延路径,有效利用边缘或路径上的节点资源进行任务调度。由于传统组网协议没有考虑邻居节点算力资源情况,难以做到资源感知、星间协同组网完成卸载任务。为解决上述问题,提出了将低轨卫星组网建模为移动自组织网络(MANET),并在主动式协议优化链路状态路由协议(OLSR)中引入节点计算资源度来感知周边组网节点、CPU、内存和负载等计算能力情况,并且根据该指标修改OLSR中的多点中继(multi point relay,MPR)选择算法与路由表更新算法。通过仿真验证了在星间协同计算中,文章提出的路由协议在任务计算时延上降低了15%~30%,并通过与地面云计算的比较验证了星间协同计算的优势。

基于分布式深度学习的多星计算卸载策略

在边缘计算增强的低轨卫星网络场景下,低轨卫星集群协同处理地面任务能有效降低用户响应时延。对卫星集群的联合卸载决策和资源分配优化问题进行研究,将其描述为一个混合整数规划问题,并采用了一种基于分布式深度学习算法的卫星边缘计算卸载算法(deep learning-based offloading algorithm, DLOA)。该算法使用多个并行DNN用于生成卸载决策并采用经验回放存储新生成的卸载决策,当采用隐藏层结构不同的DNN,收敛速度比同构DNN提升18%,收敛值与最优值的比值基本为1,可以认为已收敛至最优。此外,探讨了DNN的数量对所使用的算法的影响,仿真结果表明采用少量DNN就可以获得近优的收敛效果。通过对不同任务规模下采用不同算法的任务完成率进行研究,结果表明DLOA算法可通过采用异构DNN和优化资源分配方案显著提升完成率,其较单星运算方案任务完成率提升1倍,较二进制粒子群算法方案提升20%。

基于深度强化学习的置信传播译码算法

文章通过深度强化学习的方法来寻求二进制线性编码的有效解码策略。在加性高斯白噪声的条件下,将置信传播(BP)解码算法中软信息的迭代看作是对软信息的连续决策,并将其映射到马尔可夫决策过程,用深度强化学习网络代替传统译码器,扩大探索空间以提高译码性能,从而实现对数据驱动的最佳决策策略的学习。结果表明,相较于传统BP解码器,在误码率=10;时,学习型BP解码器在BCH码上取得大约0.75 dB的优势,这在一定程度上解决了以往研究中过于依赖数据的问题。

A multi-path routing algorithm of LEO satellite networks based on an improved ant colony system

Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system(MPRA-AC) is proposed.Matrix indicating the importance of the link between satellites is introduced into MPRA-AC in order to find the optimal path more quickly.Simulation results show that MPRA-AC reduces the number of iterations to achieve a satisfactory solution.At the same time,the packet delivery ratio of LEO satellite networks when running MPRA-AC and DSR-LSN(dynamic source routing algorithm for LEO satellite networks) is compared.The packet delivery ratio is about 7.9%lower when running DSR-LSN.Moreover,because of the mechanism of active load balancing of MPRA-AC,simulation results show that MPRA-AC outperforms DSR-LSN in link utilization when data packets are transmitted in the networks.

空间智能信息网络发展构想

随着以深度学习、集群智能等为代表的人工智能技术的突破,计算机类人分析和学习能力显著增强,人工智能技术的引入将会大幅提高空间任务执行的自主性。同时,空间信息网络基于安全环境设计,系统的无线暴露特性使得其随时会面临严峻的安全威胁。文章在研究分析空间智能信息网络国内外发展现状的基础上,对空间智能信息网络的网络架构和协议体系进行了初步设计,并对未来空间智能信息网络可能发展的关键技术进行了梳理。研究结果表明,智能认知和弹性生存是未来空间信息网络的两种基本属性。对网络架构、协议体系以及关键技术的梳理和总结,有助于形成新的智能发展观、信息安全观和能力安全观,为构建具备智能化-弹性化的空间信息网络提供理论准备和技术支撑。

Dynamic routing and wavelength assignment algorithm of optical satellite networks based on cross-layer design

In order to overcome the adverse effects of Doppler wavelength shift on data transmission in the optical satellite networks,a dynamic routing and wavelength assignment algorithm based on crosslayer design( CL-DRWA) is introduced which can improve robustness of the network. Above all,a cross-layer optimization model is designed,which considers transmission delay and wavelength-continuity constraint,as well as Doppler wavelength shift. Then CL-DRWA is applied to solve this model,resulting in finding an optimal light path satisfying the above constraints for every connection request. In CL-DRWA,Bellman-Ford method is used to find an optimal route and a distributed relative capacity loss method is implemented to get an optimal wavelength assignment result on the optimal route. Moreover,compared with the dynamic routing and wavelength assignment algorithm based on minimum delay strategy( MD-DRWA),CL-DRWA can make an improvement of 5. 3% on the communication success probability. Meanwhile,CL-DRWA can meet the requirement of transmission delay for real-time services.

Design of adaptive backoff algorithm for satellite network using grey system

In contention-based satellite communication system, collisions between data packets may occur due to the randomly sending of the packets. A proper delay before each＇ transmission can reduce the data collision rate. As classical random multiple access protocol, the slotted ALOHA （S-ALOHA） reduces the data collision rate through time slot allocation and synchronous measures. In order to improve the stability and throughput of satellite network, a backoff algorithm based on S-ALOHA will be effective. A new adaptive backoff algorithm based on S-ALOHA using grey system was proposed, which calculates the backofftime adaptively according to the network condition. And the network condition is estimated by each user terminal according to the prediction of the channel access success ratio using the model GM （1,1） in grey system. The proposed algorithm is compared to other known schemes such as the binary exponential backoff （BEB） and the multiple increase multiple decrease （MIMD） backoff. The performance of the proposed algorithm is simulated and analyzed. It is shown that throughput of the system based on the proposed algorithm is better than of system based on BEB and MIMD backoff. And there are also some improvements of the delay performance compared to using BEB. The proposed algorithm is especially effective for large number of user terminals in the satellite networks.