ID 原文 译文
57338 仿真结果表明，相比较于迫零算法，基于 ADMM 的增强信漏噪比算法能够有效地提升系统的安全性能. The simulation results show that，compared with the zero forc- ing algorithm，the ADMM algorithm can effectively improve the security performance of the system.
57339 未来第 6 代移动通信系统( 6G) 网络服务支持虚实结合、实时交互，亟需快速匹配多租户个性化服务需求，对 此，提出了一种两层递阶的网络切片智能管理方案，上层部署全局资源管理器，下层部署面向不同租户的本地资源管理器. In the future，the sixth generation of mobile communications system ( 6G) network services merge reality and virtual reality，and support real-time interaction. It is urgent to quickly match the per- sonalized service requirements of multiple tenants，therefore a two-layer hierarchical intelligent manage- ment scheme for network slicing is proposed，including the global resource manager at the upper level and the local resource managers for different tenants at the lower level.
57340 首先，考虑不同租户多类型切片请求的差异性，基于端到端切片的实时状态描述建立服务质量评估模型. Firstly，based on the real-time status description of end-to-end slice，a service quality evaluation model is established considering the differ- ence of multi-type slice requests from different tenants.
57341 结合服务质量反馈，利用深度强化学习( DＲL) 算法，优化上层全局资源分配和下层局部资源调整，提升不同域多维资源的使用效益，并使能租户资源定制化. With the service quality feedback，deep rein- forcement learning ( DＲL) algorithm is adopted to optimize the global resource allocation and local re- source adjustment. Hence，utilization efficiency of multi-dimensional resources in different domains are improved and tenants are able to customize resource usage.
57342 仿真结果表明，所提方案能够在优化资源供应商长期收益的同时，保障服务质量. The simulation results show that the proposed scheme can optimize the long-term revenue of resource providers while guaranteeing the service quality.
57343 单个节点无法满足各种新颖的应用程序对时延或能耗的要求，为此提出了一种分布式无线节点任务协同分配方法，通过利用周围节点的空闲资源，来降低所有节点处理任务的总时延或总能耗. Aiming at the fact that a single node cannot meet the delay or energy consumption require- ments of various novel applications，a distributed wireless node task collaborative allocation method is pro- posed to reduce the total delay or total energy consumption of all node processing tasks by utilizing the i- dle resources of surrounding nodes.
57344 首先根据层次分析法 ( AHP) 综合任务的多维属性，如计算负载、最晚完成时间等，确定任务执行的优先级; Firstly，according to the analytic hierarchy process ( AHP) ，the prior- ity of task execution is determined according to the multi-dimensional attributes of tasks，such as calcula- tion load and latest completion time.
57345 然后建立时延和能耗的优 化模型，并将其转化为二分图最大权值的匹配问题，采用 Kuhn Munkras( KM) 算法求解得到任务分配的最优解， 实现终端节点在网络边缘高效地协同执行任务. Then，the optimization model of time delay and energy consumption is established，which is transformed into the problem of maximum weight matching of bipartite graph. The optimal solution of task allocation is obtained by using Kuhn Munkras( KM) algorithm，which realizes the efficient cooperation of terminal nodes at the edge of network.
57346 仿真结果表明，该算法能够有效地降低任务处理的时延和 能耗. The simulation results show that the algo- rithm can effectively reduce the time delay and energy consumption of task processing.
57347 移动边缘计算( MEC) 系统在恶意用户干扰攻击和窃听的双重威胁下，会带来上行卸载受阻、用户信息泄露、系统能源利用率低等问题. Under the double threat of malicious user jamming and eavesdropping，the mobile edge com- puting( MEC) system will cause problems such as blocked uplink offloading，user information leakage， and low system energy utilization.