Power Control Algorithm Based on Q-Learning in Femtocell
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摘要: 该文研究macro-femto异构蜂窝网络中移动用户的功率控制问题，首先建立了以最小接收信号信干噪比为约束条件，最大化毫微微小区的总能效为目标的优化模型；然后提出了基于Q-Learning算法的毫微微小区集中式功率控制(PCQL)算法，该算法基于强化学习，能在没有准确信道状态信息的情况下，实现对小区内所有用户终端的发射功率统一调整。仿真结果表明该算法能实现对用户终端的功率有效控制，提升系统能效。
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关键词:
- 集中式功率控制 /
- Q-Learning算法 /
- 能效优化
Abstract: The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient. -
表 1 基于Q-Learning算法的毫微微小区功率控制算法(PCQL)
输入：W, ${n_0}$, $P_{b,\mu }^{\rm{c}} $, ${\rm{SINR}}_{b,\mu }^{\min }$, $p_{b,\mu }^{{\rm{max}}}$, $\gamma $, $\alpha $, $T\;$, $\varepsilon $，动作空间${A_b}$； 输出：${{\text{π}}^ * }$, $p_{b,\mu }^*$($\mu \in {U_b}$)； 定义：${\text{k}}$表示代理选取的动作；${\rm{SINR}}_{b,\mu }^{{\rm{real}}}$表示${u_{b,\mu }}$与基站$b$通信时 的实际信干噪比； $Q\left( {{{\text{s}}_b},{{\text{a}}_b}} \right) = 0$, ${\text{π}}\left( {{{\text{s}}_b},{{\text{a}}_b}} \right) = \frac{1}{{\left| {{A_b}\left( {{{\text{s}}_b}} \right)} \right|}}$, $\text{s}_b^t = \text{s}_b^0$； for $t = 0,1, ·\!·\!· ,T\;$ do 若rand()<$\varepsilon $，从${A_b}$中随机选动作${\text{k}}$；否则${\text{k}} \!=\! \mathop {\arg \max }\limits_{{\text{a}}_b^t} \!Q\left( {{\text{s}}_b^t,{\text{a}}_b^t} \right)$； 根据式(1)确定${\rm{SINR}}_{b,\mu }^{{\rm{real}}}$； for $\mu = 1,2, ·\!·\!· ,{N_b}$ do 若${\rm{SINR}}_{b,\mu }^{{\mathop{\rm real}\nolimits} } \ge {\rm{SINR}}_{b,\mu }^{\min }$，那么${\lambda _{b,\mu }} = 1$；否则${\lambda _{b,\mu }} = 0$； end for； 根据式(7)计算采取动作${\text{a}}_b^t = {\text{k}}$所带来的奖赏值${\Re _b}\left( {{\text{s}}_b^t,{\text{a}}_b^t} \right)$； ${\text{a}}_b^{t + 1} = {\text{π}}\left( {{\text{s}}_b^{t + 1}} \right)$；
${\rm Q}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) \leftarrow {\rm Q}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) + \alpha ( {\Re _b}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) \!+\! \gamma \mathop {\max}\limits_{ {\rm{a} }_b^{t + 1} } \left( { {\rm Q}\left( { { {\text{s} } }_b^{t + 1},{ {\text{a} } }_b^{t + 1} } \right)} \right)$ $\left.- {{\rm Q}\left( {{{\text{s}}}_b^t,{{\text{a}}}_b^t} \right)} \right)$；${\text{s}}_b^t \leftarrow {\text{s}}_b^{t + 1}$； end for； ${{\text{π}}^ * }\left( {{{\text{s}}_b}} \right) = \mathop {\arg \max }\limits_{{{\text{a}}_b}} Q\left( {{{\text{s}}_b},{{\text{a}}_b}} \right),\forall {{\text{s}}_b} \in S$. 
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表 2 主要的仿真参数
参数名称 参数值 MBS/FBS 1个/4个 MUE/FUE最大的发射功率 37 dBm/30 dBm MBS/FBS覆盖范围半径 250 m/50 m ${{\rm{SINR}} _{b,\mu }}^{\min }$ –9 dB 固定的电路功耗 100 mW 信道带宽 10 MHz 高斯白噪声的功率谱密度 ${10^{ - 11}}$ W/Hz
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