Outage Performance Analysis and Optimization of Energy Harvesting Cognitive Multihop Relay Networks
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摘要: 针对能量收集认知无线网络中的多跳中继传输问题，该文构建了一种新的具有主网络干扰的功率信标(PB)辅助能量收集认知多跳中继网络模型，并提出单向传输方案。在干扰链路统计信道状态信息场景下，推导了次网络精确和渐近总中断概率闭合式。针对精确总中断概率表达式的复杂性和非凸性，采用自适应混沌粒子群优化(ACPSO)算法对次网络总中断性能进行优化。仿真结果表明，PB功率、干扰约束、次网络跳数、能量收集比率、主接收端数目和信道容量阈值等参数对中断性能影响显著，所提算法能快速和有效地对网络中断性能进行优化。Abstract: Considering the problems of multihop relay transmission in energy harvesting cognitive radio networks, a novel Power Beacon (PB) assisted energy harvesting cognitive multihop relay network model with primary network interference is proposed, and a one-way transmission scheme is proposed. In the scenario of interference link statistical channel state information, the closed-form formulas of exact and asymptotic total outage probability are derived. In view of the complexity and nonconvexity of the exact total outage probability expression, the Adaptive Chaos Particle Swarm Optimization (ACPSO) algorithm is used to optimize the total outage performance of the secondary network. Simulation results show that the parameters such as PB’s power, interference constraint, number of secondary network hops, energy harvesting ratio, the number of primary receivers and channel capacity threshold have significant impacts on outage performance, the proposed algorithm can quickly and effectively optimize the network outage performance.
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Key words:
- Cognitive radio /
- Multihop relay networks /
- Energy harvesting /
- Outage probability
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表 1 ACPSO算法
输入：${\lambda _k}$, ${\beta _k}$, ${\omega _k}$, ${\theta _k}$, ${P_{\rm{I}} }$, ${P_0}$, ${P_{\max }}$, $N$, $K$, ${R_{\rm{th}}}$, $\varepsilon $, $\eta $, $\zeta $, $S$, $l$和$L$； 输出：全局最优值$P_{\rm{t}}^ * $和${\alpha ^ * }$； (1)设置算法参数，采用式(29)分别对粒子s, $s = 1,2, \cdots ,S$的位置和速度进行初始化。其中，$t{\rm{ = }}1$, ${x_{s,1}}\left( t \right) \in \left[ {0,{P_{\max }}} \right]$, ${x_{s,2}}\left( t \right) \in \left( {0,1} \right]$, ${v_{s,1}}\left( t \right) \in \left[ { - \mu {P_{\max }},\mu {P_{\max }}} \right]$, ${v_{s,2}}\left( t \right) \in \left[ { - \mu ,\mu } \right]$, $\mu $为0～1之间均匀分布的随机数，也由式(29)产生； (2)将${x_{s,1}}\left( 1 \right)$和${x_{s,2}}\left( 1 \right)$代入式(23)，计算${f_s}\left( 1 \right)$。令${p_{s,1}}{\rm{ = }}{x_{s,1}}\left( 1 \right)$, ${p_{s,2}}{\rm{ = }}{x_{s,2}}\left( 1 \right)$, ${p_{g,1}} = {x_{{s^*},1}}\left( 1 \right)$, ${p_{g,2}} = {x_{{s^*},2}}\left( 1 \right)$, ${s^*}$为${f_s}\left( 1 \right)$中最小值 所对应的粒子； (3)依据式(26)调整${\omega _s}\left( t \right)$；依据式(27)和式(28)分别调整${c_1}\left( t \right)$和${c_2}\left( t \right)$；依据式(29)调整${r_1}\left( t \right)$和${r_2}\left( t \right)$, $t \ge 1$； (4)依据式(24)和式(25)更新${v_{s,1}}\left( t \right){\rm{ = }}\min \left( {\max \left( { - \mu {P_{\max }},{v_{s,1}}\left( t \right)} \right),\mu {P_{\max }}} \right)$和${v_{s,2}}\left( t \right){\rm{ = }}\min \left( {\max \left( { - \mu ,{v_{s,2}}\left( t \right)} \right),\mu } \right)$以及 ${x_{s,1}}\left( t \right){\rm{ = }}\min \left( {\max \left( {0,{x_{s,1}}\left( t \right)} \right),{P_{\max }}} \right)$和${x_{s,2}}\left( t \right){\rm{ = }}\min \left( {\max \left( {0,{x_{s,2}}\left( t \right)} \right),1} \right)$, $t \ge2$；根据式(23)和式(30)分别计算${f_s}\left( t \right)$和$\varphi \left( t \right)$, $t \ge 2$， 计算更新${p_{s,1}}$, ${p_{s,2}}$, ${p_{g,1}}$和${p_{g,2}}$； (5)若满足$t > L$或$\varphi \left( t \right) < {10^{ - 6}}$，则停止迭代，输出$P_{\rm{t}}^ * = {p_{g,1}}$和${\alpha ^ * }{\rm{ = }}{p_{g,2}}$，否则返回步骤(3)，继续迭代。 
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