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混合数据的多集群系统中数据价值与信息年龄的联合优化

罗佳 陈前斌 唐伦

罗佳, 陈前斌, 唐伦. 混合数据的多集群系统中数据价值与信息年龄的联合优化[J]. 电子与信息学报, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023
引用本文: 罗佳, 陈前斌, 唐伦. 混合数据的多集群系统中数据价值与信息年龄的联合优化[J]. 电子与信息学报, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023
LUO Jia, CHEN Qianbin, TANG Lun. Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data[J]. Journal of Electronics & Information Technology, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023
Citation: LUO Jia, CHEN Qianbin, TANG Lun. Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data[J]. Journal of Electronics & Information Technology, 2024, 46(1): 308-316. doi: 10.11999/JEIT230023

混合数据的多集群系统中数据价值与信息年龄的联合优化

doi: 10.11999/JEIT230023
基金项目: 国家自然科学基金(62071078),重庆市自然科学基金(cstc2021jcyj-bsh0175),四川省科技计划(2021YFQ0053)
详细信息
    作者简介:

    罗佳:男,讲师,博士,研究方向为下一代无线通信网络、人工智能、区块链等

    陈前斌:男,教授,博士生导师,研究方向为个人通信、多媒体信息处理与传输、异构蜂窝网络等

    唐伦:男,教授,博士生导师,研究方向为下一代无线通信网络、异构蜂窝网络、图像处理等

    通讯作者:

    罗佳 luojia@cqupt.edu.cn

  • 中图分类号: TN929.5

Joint Optimization of Data Value and Age of Information in Multi-cluster System with Mixed Data

Funds: The National Natural Science Foundation of China (62071078), The Chongqing Municipal Natural Science Foundation (cstc2021jcyj-bsh0175), The Sichuan Science and Technology Program (2021YFQ0053)
  • 摘要: 信息年龄(AoI)是一种业界新兴的时间相关指标,其经常用于评估接收数据的新鲜度。该文考虑了一个视频数据与环境数据混合的多集群视频直播系统,并制定调度策略以联合优化系统数据价值与信息年龄。为克服优化问题中动作空间过大导致难以实现有效求解的问题,该文将优化问题的调度策略分解为相互关联的内外两层策略,外层策略利用深度强化学习实现集群间的信道分配,内层策略则基于构造的虚拟队列实现集群内的链路选择。双层调度策略将每个集群的内层策略嵌入到外层策略中进行训练,仿真结果显示,与现有调度策略相比,该文所提的调度策略可以提高时间平均的接收数据价值并降低时间平均的信息年龄。
  • 图  1  单位集群内终端数量对时间平均RDVA的影响

    图  2  单位集群内终端数量对时间平均数据价值的影响

    图  3  单位集群内终端数量对时间平均AoI的影响

    算法1 求解问题$ {\mathcal{P}}_{3} $的TS策略
     输入:全局神经网络参数集$ {\theta } $和$ {{\theta }}_{\mathrm{c}} $,全局计数器$ T=0 $,线程独有神经网络参数集$ {{\theta }}^{'} $和$ {{\theta }}_{\mathrm{c}}^{'} $,线程独有计数器$ t=0,\tilde{T},{T}_{\mathrm{m}\mathrm{a}\mathrm{x}} $
     输出:动作向量$ \boldsymbol{a}\left(t\right) $
     Repeat
      重置全局神经网络参数集的梯度:$ \mathrm{d}{\theta }=0 $,$ \mathrm{d}{{\theta }}_{\mathrm{c}}=0 $。同步线程独有神经网络参数集:$ {{\theta }}^{'}={\theta },{{\theta }}_{\mathrm{c}}^{'}={{\theta }}_{\mathrm{c}} $。获得当前时隙状态$ {\boldsymbol{s}}_{t} $,$ {t}_{\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{t}}=t $
      Repeat
      根据策略$ \pi \left(\left.\boldsymbol{a}\left(t\right)\right|\boldsymbol{s}\left(t\right),{{\theta }}^{'}\right) $选择动作$ \boldsymbol{a}\left(t\right) $
       For $n\in \left\{\mathrm{1,2},\cdots ,N\right\}$ do
        For $l\in \left\{N+1,N+2, \cdots ,L\right\}$ do
         If $ {\varphi }_{n}^{\mathrm{s}}\left(t\right)=l $ Then
          基于以下原则选择集群$ n $内的传感器$ {m}^{*} $与信道$ l $进行配对
         ${m}^{*}=\underset{m\in \left[2,M\right]}{\mathrm{arg}\mathrm{max} }\displaystyle\sum\limits_{f=1}^{F}{\beta }_{n,m}^{f}f+\frac{q\left(t\right){\varDelta }_{n,m}\left(t\right)}{\displaystyle\sum\limits _{j=1}^{Y}{\omega }_{n,m}^{ {y}_{j} }\left\lceil { {y}_{j}/\left({R}_{l}b\right)} \right\rceil }$
         End If
        End For
       End For
       执行动作$ \boldsymbol{a}\left(t\right) $与上述集群内链路选择决策
       获得更新后的状态$ \boldsymbol{s}\left(t+1\right) $以及即时奖励函数$ r\left(\boldsymbol{s}\left(t\right),\boldsymbol{a}\left(t\right),\boldsymbol{s}\left(t+1\right)\right) $
       $ t:=t+1,T:=T+1 $
      Until $ t=={t}_{\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{t}}+\tilde{T} $
      $ G=V\left(\boldsymbol{s}\left(t\right),{{\theta }}_{\mathrm{c}}^{'}\right) $
      For $ h\in \left\{t-1,t-2,\cdots ,{t}_{\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{r}\mathrm{t}}\right\} $ do
       $ G:=r\left(\boldsymbol{s}\left(h\right),\boldsymbol{a}\left(h\right),\boldsymbol{s}\left(h+1\right)\right)+\gamma G $
       累加线程独有的神经网络梯度:
       $\mathrm{d}{{\theta } }_{\rm{c} }:=\mathrm{d}{{\theta } }_{\rm{c} }+\partial {\left(G-V\left(\boldsymbol{s}\left(h\right),{{\theta } }_{\rm{c} }^{'}\right)\right)}^{2}/\partial {{\theta } }_{\rm{c} }^{'}$
       $\mathrm{d}{\theta }:=\mathrm{d}{\theta }+{ {\boldsymbol{ {\text{∇} } } } }_{ {{\theta } }^{'} }\mathrm{l}\mathrm{n}\pi \left(\left.\boldsymbol{a}\left(h\right)\right|\boldsymbol{s}\left(h\right),{{\theta } }^{'}\right)\left(G-V\left(\boldsymbol{s}\left(h\right),{{\theta } }_{\rm{c} }^{'}\right)\right)$
      End For
      利用累积梯度$ \mathrm{d}{\theta } $和$\mathrm{d}{{\theta } }_{\rm{c} }$异步更新全局神经网络参数集$ {\theta } $和${{\theta } }_{\rm{c} }$
     Until $ T > {T}_{\mathrm{m}\mathrm{a}\mathrm{x}} $
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-16
  • 修回日期:  2023-04-18
  • 网络出版日期:  2023-04-26
  • 刊出日期:  2024-01-17

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