﻿ 认知无线电协同传输在车联网中的应用及性能分析

# 认知无线电协同传输在车联网中的应用及性能分析Application and Performance analysis of Cognitive Radio Cooperative Transmission in Internet of Vehicles

Abstract: With the rapid development of communication and smart antenna technology, the Internet of Vehicles (IoV) has received extensive attention and research. The application of IoV is hampered by lack of spectrum and limited communication coverage. In this paper, cognitive radio cooperative transmission is introduced into the IoV system to expand the transmission range without increasing the transmission power. The outage probability of end-to-end transmission is calculated by analyzing and deducing the signal to interference plus noise ratio. Numerical simulation results show that compared with direct communication, the use of cognitive radio cooperative transmission can effectively reduce the outage probability and improve the performance of the IoV.

1. 引言

[8] 将最佳中继协同传输引入到认知无线电中，推导了对主网络的干扰小于或等于阈值的情况下，精准的中断概率表达式。但 [8] 中的协同中继转发策略依赖大量信道状态信息，实现起来并没有那么容易。在 [9] 中提出来一种新的中继协同传输方法，通过选择部分具有固定增益的中继，减少对于信道信息的需求。 [8] [9] 都为单中继协同传输策略， [10] 提出了多中继的协同传输策略，它们以牺牲一定的带宽来获得更高的信干噪比。

2. 系统模型及信干噪比分析

2.1. 系统模型

Figure 1. System model

2.2. 信干噪比分析

${y}_{1}=\sqrt{{P}_{1}}{h}_{1}{x}_{1}+{n}_{0}+\sqrt{{P}_{3}}{h}_{3}{x}_{2}$ (1)

${y}_{2}=\sqrt{{P}_{2}}{h}_{2}{x}_{1}+{n}_{0}$ (2)

${\zeta }_{1}=\frac{{P}_{1}{|{h}_{1}|}^{2}}{{N}_{0}+{P}_{3}{|{h}_{3}|}^{2}}=\frac{\frac{{P}_{1}{|{h}_{1}|}^{2}}{{N}_{0}}}{1+\frac{{P}_{3}{|{h}_{3}|}^{2}}{{N}_{0}}}=\frac{{\gamma }_{1}}{1+{\varphi }_{1}}$ (3)

${\zeta }_{2}=\frac{{P}_{2}{|{h}_{2}|}^{2}}{{N}_{0}}={\gamma }_{2}$ (4)

$x\ge 1$ 时， ${F}_{{\varphi }_{1}+1}\left(x\right)=\mathrm{Pr}\left\{{\varphi }_{1}+1\le x\right\}=\mathrm{Pr}\left\{{\varphi }_{1}\le x-1\right\}={F}_{{\varphi }_{1}}\left(x-1\right)$${\varphi }_{1}+1$ 分布函数为：

${F}_{{\varphi }_{1}+1}\left(x\right)=1-{\text{e}}^{-{\mu }_{3}\left(x-1\right)}$ (5)

${f}_{{\varphi }_{1}+1}\left(x\right)={{F}^{\prime }}_{{\varphi }_{1}+1}\left(x\right)={\mu }_{3}{\text{e}}^{-{\mu }_{3}\left(x-1\right)}$ (6)

$x<1$ 时， ${F}_{{\varphi }_{1}+1}\left(x\right)=0$${f}_{{\varphi }_{1}+1}\left(x\right)=0$

$\begin{array}{l}\because {F}_{{\zeta }_{1}}\left(x\right)=\mathrm{Pr}\left\{\frac{{\gamma }_{1}}{1+{\varphi }_{1}}\le x\right\}={\int }_{0}^{\infty }\mathrm{Pr}\left\{{\gamma }_{1}\le xt\right\}\cdot {f}_{{\varphi }_{1}+1}\left(t\right)\text{d}t\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}={\int }_{0}^{\infty }\left(1-{\text{e}}^{-{\mu }_{1}\left(xt\right)}\right)\cdot \left({\mu }_{3}{\text{e}}^{-{\mu }_{3}\left(t-1\right)}\right)\text{d}t={\text{e}}^{{\mu }_{3}}-\frac{{\mu }_{3}{\text{e}}^{{\mu }_{3}}}{{\mu }_{1}x+{\mu }_{3}}\end{array}$ (7)

(7)式为车辆S到车辆R链路的信干噪比分布函数，又因为 ${\zeta }_{2}=\frac{{P}_{2}{|{h}_{1}|}^{2}}{{N}_{0}}={\gamma }_{2}~\mathrm{exp}\left({\mu }_{2}\right)$

${\zeta }_{2}$ 的分布函数为：

${F}_{{\zeta }_{2}}\left(x\right)=1-{\text{e}}^{-{\mu }_{2}x}$ (8)

${\zeta }_{e2e}=\mathrm{min}\left\{{\zeta }_{1},{\zeta }_{2}\right\}$

$\begin{array}{c}{F}_{{\zeta }_{e2e}}\left(x\right)=\mathrm{Pr}\left\{\mathrm{min}\left\{{\zeta }_{1},{\zeta }_{2}\right\}\le x\right\}=1-\mathrm{Pr}\left\{\mathrm{min}\left\{{\zeta }_{1},{\zeta }_{2}\right\}>x\right\}\\ =1-\mathrm{Pr}\left\{{\zeta }_{1}>x\right\}\cdot \mathrm{Pr}\left\{{\zeta }_{2}>x\right\}=1-\left(1-{F}_{{\zeta }_{1}}\left(x\right)\right)\cdot \left(1-{F}_{{\zeta }_{2}}\left(x\right)\right)\end{array}$ (9)

${F}_{{\zeta }_{e2e}}\left(x\right)=1-\left(1-{\text{e}}^{{\mu }_{3}}+\frac{{\mu }_{3}{\text{e}}^{{\mu }_{3}}}{{\mu }_{1}x+{\mu }_{3}}\right)\cdot \left({\text{e}}^{-{\mu }_{2}x}\right)$ (10)

3. 性能分析及数值模拟

3.1. 中断概率

${P}_{out}=\mathrm{Pr}\left(\frac{1}{2}{\mathrm{log}}_{2}\left(1+{\zeta }_{e2e}\right)\le r\right)={F}_{{\zeta }_{e2e}}\left({2}^{2r}-1\right)$

${P}_{out}=1-\left(1-{\text{e}}^{{\mu }_{3}}+\frac{{\mu }_{3}{\text{e}}^{{\mu }_{3}}}{{\mu }_{1}\left({2}^{2r-1}\right)+{\mu }_{3}}\right)\cdot \left({\text{e}}^{-{\mu }_{2}\left({2}^{2r-1}\right)}\right)$ (11)

3.2. 数值模拟

Figure 2. The influence curve of ${\gamma }_{1}$ on ${P}_{out}$

Figure 3. The influence curve of ${\varphi }_{1}$ on ${P}_{out}$

4. 结论

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