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能量收集通信系统中功率和调制方式的在线联合优化策略

雷维嘉 孙嘉琳 谢显中 雷宏江

雷维嘉, 孙嘉琳, 谢显中, 雷宏江. 能量收集通信系统中功率和调制方式的在线联合优化策略[J]. 电子与信息学报, 2022, 44(3): 1024-1033. doi: 10.11999/JEIT210145
引用本文: 雷维嘉, 孙嘉琳, 谢显中, 雷宏江. 能量收集通信系统中功率和调制方式的在线联合优化策略[J]. 电子与信息学报, 2022, 44(3): 1024-1033. doi: 10.11999/JEIT210145
LEI Weijia, SUN Jialin, XIE Xianzhong, LEI Hongjiang. Online Joint Optimization of Power and Modulation in Energy Harvesting Communication Systems[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1024-1033. doi: 10.11999/JEIT210145
Citation: LEI Weijia, SUN Jialin, XIE Xianzhong, LEI Hongjiang. Online Joint Optimization of Power and Modulation in Energy Harvesting Communication Systems[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1024-1033. doi: 10.11999/JEIT210145

能量收集通信系统中功率和调制方式的在线联合优化策略

doi: 10.11999/JEIT210145
基金项目: 国家自然科学基金(61971080, 61471076),重庆市教委科学技术研究重点项目(KJZD-M201900602)
详细信息
    作者简介:

    雷维嘉:男,1969年生,博士,教授,主要研究方向为无线通信和移动通信技术

    孙嘉琳:女,1995年生,硕士生,研究方向为无线通信和物理层速率自适应技术

    谢显中:男,1966 年生,博士生导师,教授,研究方向为无线和移动通信技术

    通讯作者:

    孙嘉琳 2503443663@qq.com

  • 中图分类号: TN92

Online Joint Optimization of Power and Modulation in Energy Harvesting Communication Systems

Funds: The National Natural Science Foundation of China (61971080, 61471076), The Key Project of Science and Technology Research of Chongqing Education Commission (KJZD-M201900602)
  • 摘要: 针对源节点配备能量收集装置的点对点能量收集无线通信系统,该文以最大化长期平均传输速率为目标,提出一种基于Lyapunov优化框架的在线功率控制和自适应调制联合优化策略。由于能量到达和信道状态的随机性,优化问题是一个随机优化问题。利用Lyapunov优化框架将电池操作和可用能量约束下的长期时间优化问题转化为每时隙以虚队列“漂移加惩罚”最小化为目标的发送功率、调制方式和帧长的联合优化问题,并求解。该算法仅依赖当前的电池状态和信道状态信息做出决策,计算复杂度低,实用性强。仿真结果显示,所提算法通过联合优化发送功率、调制方式和帧长,能够高效地利用收集的能量,适应信道变化,长期平均实际可达的信息传输速率要明显优于贪婪和半功率算法,即使相比较以信道容量最大化为目标的离线注水算法和其他对比算法,在实际可达的信息传输速率上也有优势。
  • 图  1  系统模型

    图  2  与对比算法性能比较

    图  3  能量到达率$ \lambda $对实际传输速率的影响

    图  4  虚队列偏移量A变化对系统性能的影响

    图  5  惩罚项权重V对系统性能的影响

    表  1  算法实现流程

     设定参数:权重V,虚队列偏移量A
     输入:可选调制方案,初始电池电量Eb(0),搜索步长$ \delta $;
     输出:最优调制方式M,帧长N,发送功率P(t);
     在时隙t
     (1) 观察系统状态:$ h(t) $,$ X(t) $;
     (2) for M=1,2,3,4,5,6 do %给定M下优化$P(t), N$,目
       标函数为$ J(P(t),N|M) $
     (3)   if $ X(t) \ge 0 $
     (4)      $ P(t) = \min \left( {{P_{{\text{d}},\max }},{E_{\text{S}}}(t)/\Delta t} \right) $;
     (5)      由式(7)得到Peb
     (6)      由式(30)得到N
     (7)  else
     (8)      将$ \frac{{\partial J(P(t),N|M)}}{{\partial N}} = 0 $中的Peb看成已知数,由
            式(30)得到$ N(P(t)) $;
     (9)      将$ N(P(t)) $代入目标函数中,在[0, Pd,max]内以
            $ \delta $为步长搜索使目标函数最大化的P(t);
     (10)      由式(7)得到Peb
     (11)      由式(30)得到N
     (12)  end if
     (13) end for
     (14) 计算所有M下各自最优$ \{ P(t),N\} $时的目标函数
       $ J(P(t),M,N) $,选择目标函数最大的M及对应的
       $ \{ P(t),N\} $;
     (15) if $ N \le {N_{{\text{CRC}}}} $ then
     (16)  P(t)=0,$ {R_{\text{b}}}(t) = 0 $;
     (17) end if
     (18) return P(t),MN
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-18
  • 修回日期:  2021-10-20
  • 录用日期:  2021-11-05
  • 网络出版日期:  2021-11-11
  • 刊出日期:  2022-03-28

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