# Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission

^{*}

## Abstract

**:**

## 1. Introduction

- (1)
- The improved SNR and N-SNR scheduling mechanisms for multi-user SWIPT system are proposed, wherein the users are sorted in ascending order according to SNR and N-SNR, respectively. The jth user, who has the jth smallest SNR or N-SNR, will be selected to deliver the data, and the rest users will collect the energy from surroundings. Obviously, a lower j means that the user selected for data transmission has a poorer channel condition. Meanwhile, the users with better channel conditions will perform the energy collection. Thus, the lower j will cause a smaller ergodic capacity and a larger average collected energy (ACE).
- (2)
- The improved ET scheduling mechanism is designed, wherein the users are sorted in ascending order according to N-SNR. ${S}_{a}$ is a specific set of ordinal. The user, who has an N-SNR order in the set of ${S}_{a}$ and has the minimum average throughput, will be selected to transmit data in each time slot. Therefore, a smaller set of ${S}_{a}$ will cause a larger ACE and a smaller ergodic capacity. In addition, this paper gives the necessary conditions for the user to achieve ET.
- (3)
- The improved SNR, N-SNR and ET scheduling mechanisms are analyzed using order statistical theory for heterogeneous (independent or non-identically distributed) Nakagami-m, Weibull, Ricean and Rayleigh fading channels. In addition, three types of scheduling mechanisms and the relative approximate expressions are established for ACE and ergodic channel capacity (ECC) of a single user in corresponding fading models.

## 2. Model Introduction

#### 2.1. Symbols and Formulas

#### 2.2. System Model

#### 2.3. Energy Collecting Model

#### 2.4. Fading Channel

- (1)
- Ricean fading channel: The Ricean fading channel is suitable for short distance RF EC situation, where the NAP is within the range of the user’s sight. If $\sqrt{{h}_{n}}$ complies with the Rician distribution, the channel power gain ${h}_{n}$ will obey the non-central ${\chi}^{2}$ distribution. The first order Marcum-Q equation [22] is used to facilitate the performance analysis of different scheduling mechanisms in Ricean fading channels, namely ${Q}_{1}(a,b)\approx {e}^{-{e}^{\nu \left(a\right)}{b}^{\mu \left(a\right)}}$. $\mu \left(a\right)$ and $\nu \left(a\right)$ are nonnegative parameters that can decrease the error between the theoretical value and the approximation of the function:$$\begin{array}{c}\hfill \mu \left(a\right)=\left(\right)open="\{"\; close>\begin{array}{cc}2+\frac{9}{8(9{\pi}^{2}-80)}{a}^{4},\hfill & a\ll 1,\hfill \\ 2.174-0.592a+0.593{a}^{2}-0.092{a}^{3}+0.005{a}^{4},\hfill & \mathrm{otherwise},\hfill \end{array}\end{array}$$$$\begin{array}{c}\hfill \nu \left(a\right)=\left(\right)open="\{"\; close>\begin{array}{cc}\frac{45{\pi}^{2}+72ln2+36C-496}{64(9{\pi}^{2}-80)}{a}^{4}-\frac{{a}^{2}}{2}-ln2,\hfill & a\ll 1,\hfill \\ 0.327a-0.840-0.740{a}^{2}+0.083{a}^{3}-0.004{a}^{4},\hfill & \mathrm{otherwise}.\hfill \end{array}\end{array}$$The accuracy of the approximation will decrease with K (Ricean factor) increasing. Applying an approximation of Marcum-Q equation, the cumulative distribution function of the channel power gain is ${F}_{{h}_{n}}\left(x\right)\approx 1-{e}^{-{\beta}_{n}{x}^{{\mu}^{\prime}}}$, where ${\mu}^{\prime}=\mu \left(\sqrt{2K}\right)/2$, ${\beta}_{n}={e}^{\nu \left(\sqrt{2K}\right)}{\left(\right)}^{2}\mu \left(\sqrt{2K}\right)/2$. It can be seen that the result of the cumulative distribution function is similar to that of Weibull fading channel.
- (2)
- Weibull fading channel: Weibull fading channel is suitable for broadcast channels in narrowband body area networks. The network embedded in the human body can only be powered by wireless energy transmission. If the channel parameter $\sqrt{{h}_{n}}$ of sensor n is in accordance with the Weibull distribution, the channel power gain ${h}_{n}$ will follow the Weibull distribution, $\forall n\in \left(\right)open="\{"\; close="\}">1,...,N$.
- (3)
- Nakagami-m fading channel: Nakagami-m fading channel can cope with the indoor wireless energy collecting model. The fading channels can be distinguished by the shape parameter m, which is the index of the channel fading degree. The smaller the m is, the quicker the channel fading will be. m is considered as the parameter for auxiliary analysis. The Nakagami-m fading channel energy obtained by the users will follow the Erlang distribution.
- (4)
- Rayleigh fading channel: Rayleigh fading channel model is a particular case of the above channel model. If $\sqrt{{h}_{n}}$ of user n conforms to Rayleigh distribution, the channel power gain will obey an exponentially distributed.

#### 2.5. Reference Scheduling Mechanism

- (1)
- RR mechanism: RR scheduling strategy confers the channel to the users orderly, and the NAP does not need to know the channel gain of the different users. Therefore, the probability of each user receiving messages is $\frac{1}{N}$, so the probability of collecting energy is $1-\frac{1}{N}$. The ergodic capacity $E\left({C}_{{U}_{n}}\right)$ achieved by user n (denoted by ${U}_{n}$) is $\frac{1}{N}$ times of $E\left({C}_{{U}_{n,f}}\right)$:$$\begin{array}{c}\hfill E\left({C}_{{U}_{n}}\right){\mid}_{RR}=\frac{1}{N}E\left({C}_{{U}_{n,f}}\right),\end{array}$$
- (2)
- ET Mechanism: The long-term average traffic of all users will be balanced by scheduling users with minimum moving average traffic in each time slot according to the traditional ET scheduling. In time slot t, the user ${n}^{*}$ for transmitting messages should satisfy$$\begin{array}{c}\hfill {n}^{*}=argmin{r}_{n}(t-1),n\in \left(\right)open="\{"\; close="\}">1,...,N.\end{array}$$

## 3. Improved SNR Scheduling Mechanism

#### 3.1. IS2M

#### 3.2. Performance Analysis

## 4. Improved N-SNR Scheduling Mechanism

#### 4.1. INS2M

#### 4.2. Performance Analysis

## 5. Improved Equal Throughput Scheduling Mechanism

#### 5.1. IETSM

#### 5.2. Performance Analysis

**Theorem**

**1.**

## 6. Implementation and Performance Analysis

## 7. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

WSNs | Wireless Sensor Networks |

SNR | Signal Noise Ratio |

TDMA | Time Division Multiple Access |

IS2M | Improved SNR Scheduling Mechanism |

INS2M | Improved N-SNR Scheduling Mechanism |

IETSM | Improved Equal Throughput Scheduling Mechanism |

EC | Energy Collecting |

RF | Radio Frequency |

SWIPT | Synchronous Wireless Information and Power Transmission |

R-E | Rate-Energy |

MD | Message Decoding |

MUD | Multi-User Diversity |

ACE | Average Collected Energy |

ECC | Ergodic Channel Capacity |

NAP | Network Access Port |

STs | Subscriber Terminals |

RR | Round Robin |

ET | Equal Throughout |

CAP | Channel Access Probability |

Probability Density Function | |

CDF | Cumulative Distribution Function |

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**Figure 3.**The average system capacity and the total collected energy obtained by IS2M, INS2M and RR scheduling mechanism in the Nakagami-m fading channel with $N=7$ and $m=3$. (

**a**) average system capacity; (

**b**) average collected energy.

**Figure 4.**The average system capacity and the total collected energy of single user obtained by IS2M in the Nakagami-m fading channel $N=7$ and $m=3$. (

**a**) ECC of single user; (

**b**) ACE of single user.

**Figure 5.**The energy efficiency of INS2M, RR and IETSM in the Ricean fading channel with $N=7$ and $K=6$. (

**a**) INS2M; (

**b**) IETSM.

**Figure 6.**The average system capacity and total collected energy of INS2M with different numbers of users within the independent identically distributed Weibull fading channels, $k=1.5$. (

**a**) the average system capacity; (

**b**) the total collected energy.

Variable | Meaning | Variable | Meaning |
---|---|---|---|

n | the ordinal of user | j | the ordinal of the selected user |

x | the baseband symbol | K | shape parameter in Ricean Fading |

$\eta $ | the conversion efficiency | ${\theta}_{n}$ | the phase of fading coefficient |

${r}_{n}$ | the throughput of user n | k | shape parameter in Weibull Fading |

N | the number of users | ${\Omega}_{n}$ | the mean channel power gain of user n |

$\beta $ | a smoothing factor | $\sqrt{{h}_{n}}$ | the amplitude of fading coefficient |

${h}_{n}$ | the channel power gain | a,b | the smallest positive integers satisfying $\frac{b}{a}}=k$ |

${\overline{\gamma}}_{n}$ | the average SNR of user n | ${Z}_{n}$ | zero-mean additive white Gaussian noise |

m | a measure in Nakagami-m Fading | ${p}_{n}$ | the channel access probability of user n |

Channel Model | PDF ${\mathit{f}}_{{\mathit{h}}_{\mathit{n}}}\left(\mathit{x}\right)$ | CDF${\mathit{F}}_{{\mathit{h}}_{\mathit{n}}}\left(\mathit{x}\right)$ | Parameters |
---|---|---|---|

Nakagami-m | $\frac{1}{\Gamma \left(m\right)}{\lambda}_{n}^{m}{x}^{m-1}{e}^{-{\lambda}_{n}x}$ | $\begin{array}{c}1-\frac{\Gamma (m,{\lambda}_{n}x)}{\Gamma \left(m\right)}\hfill \\ =1-{e}^{-{\lambda}_{n}x}\sum _{s=0}^{m-1}\frac{{\left({\lambda}_{n}x\right)}^{s}}{s!}\hfill \end{array}$ | ${\lambda}_{n}=\frac{m}{{\Omega}_{n}}$ |

Weibull | $k{\lambda}_{n}^{k}{x}^{k-1}{e}^{-{\left({\lambda}_{n}x\right)}^{k}}$ | $1-{e}^{-{\left({\lambda}_{n}x\right)}^{k}}$ | ${\lambda}_{n}=\frac{\Gamma \left(\right)open="("\; close=")">1+\frac{1}{k}}{}{\Omega}_{n}$ |

Ricean | $\begin{array}{c}\frac{K+1}{{\Omega}_{n}}{e}^{-K-\frac{(K+1)x}{{\Omega}_{n}}}\hfill \\ {I}_{0}\left(\right)open="("\; close=")">2\sqrt{\frac{K(K+1)}{{\Omega}_{n}}x}\hfill \end{array}$ | $\begin{array}{c}1-{Q}_{1}\left(\right)open="("\; close=")">\sqrt{2K},\sqrt{\frac{2(K+1)x}{{\Omega}_{n}}}\hfill \end{array}$ | $\begin{array}{c}{\beta}_{n}={e}^{\nu \left(\sqrt{2K}\right)}{\sqrt{\frac{2(K+1)}{{\Omega}_{n}}}}^{\mu \left(\sqrt{2K}\right)}\hfill \\ {\mu}^{{}^{\prime}}=\frac{\mu \left(\sqrt{2K}\right)}{2}\hfill \end{array}$ |

Rayleigh | ${\lambda}_{n}{e}^{-{\lambda}_{n}x}$ | $1-{e}^{-{\lambda}_{n}x}$ | ${\lambda}_{n}=\frac{1}{{\Omega}_{n}}$ |

Channel Model | $\mathit{E}\left[{\mathit{C}}_{{\mathit{U}}_{\mathit{n},\mathit{f}}}\right]$ |
---|---|

Nakagami-m | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)\Gamma \left(m\right)}{\left(\right)}^{\frac{m}{{\overline{\gamma}}_{n}}}m{G}_{2,3}^{3,1}\left(\right)open="["\; close="]">\frac{m}{{\overline{\gamma}}_{n}}|\begin{array}{ccc}& -m,& 1-m,\\ 0,& -m,& -m,\end{array}\end{array}$ |

Weibull | $\begin{array}{c}\hfill \frac{k{\lambda}_{n}^{\prime k}}{ln\left(2\right)}\frac{{\sqrt{ab}}^{-1}}{{\left(2\pi \right)}^{\frac{a+2b-3}{2}}}{G}_{2b,a+2b}^{a+2b,b}\left(\right)open="["\; close="]">\frac{{\lambda}_{n}^{\prime ak}}{{a}^{a}}|\begin{array}{cc}\u25b5(b,-k),& \u25b5(b,1-k),\\ \u25b5(a,0),& \u25b5(b,-k),\u25b5(b,-k)\end{array}\end{array}$ |

Ricean | $\begin{array}{c}\hfill \frac{(1+K){e}^{-K}}{ln\left(2\right){\overline{\gamma}}_{n}}\sum _{i=0}^{\infty}\frac{1}{{(i!)}^{2}}{\left(\right)}^{\frac{K(1+K)}{{\overline{\gamma}}_{n}}}i{G}_{2,3}^{3,1}\left(\right)open="["\; close="]">\frac{(K+1)}{{\overline{\gamma}}_{n}}|\begin{array}{ccc}& -1-i,& -i,\\ 0,& -1-i,& -1-i,\end{array}\end{array}$ |

Rayleigh | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}{e}^{\frac{1}{{\overline{\gamma}}_{n}}}{E}_{1}\left(\right)open="("\; close=")">\frac{1}{{\overline{\gamma}}_{n}}\end{array}$ |

Channel Model | $\mathit{E}\left[{\mathit{C}}_{\mathit{j},{\mathit{U}}_{\mathit{n}}}\right]$ |
---|---|

Nakagami-m | $\begin{array}{c}\frac{{\lambda}_{n}^{m}}{ln\left(2\right)\Gamma \left(m\right)}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}\sum _{{S}_{m,r}}\frac{{\prod}_{t=1}^{N-j+r}{\lambda}_{{u}_{t}}^{{s}_{t}}}{{\prod}_{t=1}^{N-j+r}{s}_{t}!}{\left(\right)}^{{\lambda}_{n}}-\left(\right)open="("\; close=")">m+{\sum}_{t=1}^{N-j+r}{s}_{t}\hfill \end{array}\Gamma \left(\right)open="("\; close=")">m+\sum _{t=1}^{N-j+r}{s}_{t}\left(\right)open="("\; close=")">\Psi \left(\right)open="("\; close=")">m+{\Sigma}_{t=1}^{N-j+r}{s}_{t}\hfill & +ln\left(\right)open="("\; close=")">\frac{\overline{\gamma}}{{\lambda}_{n}+{\Sigma}_{t=1}^{N-j+r}{\lambda}_{{u}_{t}}}$ |

Weibull | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}{\lambda}_{n}^{k}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\lambda}_{n}^{k}}-1\left(\right)open="("\; close=")">ln\left(\overline{\gamma}\right)-\frac{1}{k}\left(\right)open="("\; close=")">ln\left(\right)open="("\; close=")">{\lambda}_{n}^{k}+\sum _{t=1}^{N-j+r}{\lambda}_{{u}_{t}}^{k}& +C\end{array}$ |

Ricean | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}{\beta}_{n}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\beta}_{n}}-1\left(\right)open="("\; close=")">ln\left(\overline{\gamma}\right)-\frac{1}{{\mu}^{{}^{\prime}}}\left(\right)open="("\; close=")">ln\left(\right)open="("\; close=")">{\beta}_{n}+\sum _{t=1}^{N-j+r}{\beta}_{{u}_{t}}& +C\end{array}$ |

Rayleigh | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}{\lambda}_{n}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\lambda}_{n}}-1{e}^{\frac{1}{\overline{\gamma}}\left(\right)open="("\; close=")">{\lambda}_{n}+{\sum}_{t=1}^{N-j+r}{\lambda}_{{u}_{t}}}& {E}_{1}\left(\right)open="("\; close=")">\frac{1}{\overline{\gamma}}\left(\right)open="("\; close=")">{\lambda}_{n}+\sum _{t=1}^{N-j+r}{\lambda}_{{u}_{t}}\end{array}$ |

Channel Model | $\mathit{E}\left[{\mathit{EC}}_{\mathit{j},{\mathit{U}}_{\mathit{n}}}\right]$ |
---|---|

Nakagami-m | $\begin{array}{c}\eta P{\Omega}_{n}-\eta P\frac{{\lambda}_{n}^{m}}{\Gamma \left(m\right)}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}\sum _{{S}_{m,r}}\frac{{\prod}_{t=1}^{N-j+r}{\lambda}_{{u}_{t}}^{{s}_{t}}}{{\prod}_{t=1}^{N-j+r}{s}_{t}!}{\left(\right)}^{{\lambda}_{n}}-\left(\right)open="("\; close=")">m+1+{\sum}_{t=1}^{N-j+r}{s}_{t}\hfill \end{array}\Gamma \left(\right)open="("\; close=")">m+1+\sum _{t=1}^{N-j+r}{s}_{t}\hfill $ |

Weibull | $\begin{array}{c}\hfill \eta P\left(\right)open="("\; close=")">{\Omega}_{n}-{\lambda}_{n}^{k}\Gamma \left(\right)open="("\; close=")">1+\frac{1}{k}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\lambda}_{n}^{k}}-\left(\right)open="("\; close=")">1+\frac{1}{k}\end{array}$ |

Ricean | $\begin{array}{c}\hfill \eta P\left(\right)open="("\; close=")">{\Omega}_{n}-{\beta}_{n}\Gamma \left(\right)open="("\; close=")">1+\frac{1}{{\mu}^{{}^{\prime}}}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\beta}_{n}^{+}}-\left(\right)open="("\; close=")">1+\frac{1}{{\mu}^{{}^{\prime}}}\end{array}$ |

Rayleigh | $\begin{array}{c}\hfill \eta P\left(\right)open="("\; close=")">{\Omega}_{n}-{\lambda}_{n}\sum _{r=0}^{j-1}{(-1)}^{r}\sum _{{U}_{n,r}}{\left(\right)}^{{\lambda}_{n}}-2\end{array}$ |

Channel Model | $\mathit{E}\left[{\mathit{C}}_{\mathit{j},{\mathit{U}}_{\mathit{n}}}\right]$ |
---|---|

Nakagami-m | $\begin{array}{c}\frac{1}{ln\left(2\right)\Gamma \left(m\right)}\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=N-j}^{N-1}{(-1)}^{l-N+j}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ N-l-1\end{array}\hfill & \frac{l!}{{(1+l)}^{m}}\end{array}\sum _{{L}_{m,l}}\left(\right)open="("\; close=")">\prod _{s=0}^{m-1}\frac{{\left(\right)}^{\frac{1}{s!{(1+l)}^{s}}}}{{i}_{s}}{i}_{s}!\hfill & \Gamma \left(\right)open="("\; close=")">m+\sum _{s=0}^{m-1}s{i}_{s}\\ \left(\right)open="("\; close=")">\Psi \left(\right)open="("\; close=")">m+{\Sigma}_{s=0}^{m-1}s{i}_{s}$ |

Weibull | $\begin{array}{c}\frac{1}{ln\left(2\right)}\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=0}^{j-1}\frac{{(-1)}^{l}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ l\end{array}}{}N-j+l+1\hfill \end{array}\left(\right)open="("\; close=")">ln\left({\overline{\gamma}}_{n}\right)-\frac{1}{k}\left(\right)open="("\; close=")">ln\left(\right)open="("\; close=")">(N-j+l+1)\Gamma {\left(\right)}^{1}k\hfill \\ +C$ |

Ricean | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=0}^{j-1}\frac{{(-1)}^{l}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ l\end{array}}{}N-j+l+1& \left(\right)open="("\; close=")">ln\left({\overline{\gamma}}_{n}\right)-\frac{1}{{\mu}^{{}^{\prime}}}\left(\right)open="("\; close=")">ln\left(\right)open="("\; close=")">(N-j+l+1)\beta \\ +C\end{array}$ |

Rayleigh | $\begin{array}{c}\hfill \frac{1}{ln\left(2\right)}\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=0}^{j-1}\frac{{(-1)}^{l}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ l\end{array}}{}N-j+l+1& {e}^{\frac{(N-j+l+1)}{{\overline{\gamma}}_{n}}}{E}_{1}\left(\right)open="("\; close=")">\frac{(N-j+l+1)}{{\overline{\gamma}}_{n}}\end{array}$ |

Channel Model | $\mathit{E}\left[{\mathit{EC}}_{\mathit{j},{\mathit{U}}_{\mathit{n}}}\right]$ |
---|---|

Nakagami-m | $\begin{array}{c}\eta P{\Omega}_{n}-\eta P{\Omega}_{n}\frac{1}{\Gamma (m+1)}\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=N-j}^{N-1}{(-1)}^{l-N+j}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ N-l-1\end{array}\hfill & \frac{l!}{{(1+l)}^{m+1}}\end{array}\sum _{{L}_{m,l}}\left(\right)open="("\; close=")">\prod _{s=0}^{m-1}\frac{{\left(\right)}^{\frac{1}{s!{(1+l)}^{s}}}}{{i}_{s}}{i}_{s}!\hfill & \Gamma \left(\right)open="("\; close=")">m+1+\sum _{s=0}^{m-1}s{i}_{s}$ |

Weibull | $\begin{array}{c}\hfill \eta P{\Omega}_{n}\left(\right)open="("\; close=")">1-\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=0}^{j-1}{(-1)}^{l}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ l\end{array}& {(N-j+l+1)}^{-\left(\right)open="("\; close=")">1+\frac{1}{k}}\end{array}$ |

Ricean | $\begin{array}{c}\hfill \eta P{\Omega}_{n}\left(\right)open="("\; close=")">1-\left(\right)open="("\; close=")">\begin{array}{c}N-1\\ j-1\end{array}\sum _{l=0}^{j-1}{(-1)}^{l}\left(\right)open="("\; close=")">\begin{array}{c}j-1\\ l\end{array}& {(N-j+l+1)}^{-\left(\right)open="("\; close=")">1+\frac{1}{{\mu}^{{}^{\prime}}}}\end{array}$ |

Rayleigh | $\begin{array}{c}\hfill \eta P{\Omega}_{n}\left(\right)open="("\; close=")">1-\frac{1}{N}\sum _{l=N-j+1}^{n}\frac{1}{l}\end{array}$ |

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**MDPI and ACS Style**

Qin, D.; Yang, S.; Zhang, Y.; Ma, J.; Ding, Q.
Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission. *Sensors* **2017**, *17*, 1343.
https://doi.org/10.3390/s17061343

**AMA Style**

Qin D, Yang S, Zhang Y, Ma J, Ding Q.
Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission. *Sensors*. 2017; 17(6):1343.
https://doi.org/10.3390/s17061343

**Chicago/Turabian Style**

Qin, Danyang, Songxiang Yang, Yan Zhang, Jingya Ma, and Qun Ding.
2017. "Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission" *Sensors* 17, no. 6: 1343.
https://doi.org/10.3390/s17061343