# Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

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## Abstract

**:**

## 1. Introduction

## 2. Related Studies

## 3. System Model and Problem Formulation

## 4. Proposed Algorithm

#### 4.1. RRH Switching Operation and SINR-Based User Association

- The trajectory ${T}_{d}$ is organized for each UE consisting of a list of serving RRHs at regular intervals, where ${T}_{d}$ is the $d$
^{th}day’s history ($1\le d\le D$); the trajectory ${T}_{d}$ is then divided into time intervals $t$. - The RRH that appears most frequently in each interval is selected as a representative RRH and a representative history is developed. The latest $k$ representative RRHs are considered, indicated as $patter{n}_{k}$, where $1\le k\le t$, and a $patter{n}_{s}$ is created by combining with all RRHs, where $s\in \mathrm{S}$.
- The probability of appearance in the next interval $P\left(patter{n}_{s}\right)$ is calculated for each $patter{n}_{s}$:$$P\left(patter{n}_{s}\right)={S}^{\frac{{\theta}_{s}}{\left|{T}_{d}\right|}}\times \frac{{N}_{patter{n}_{s}}}{{N}_{patter{n}_{k}}},$$
- The RRH $s$ with the highest probability $P\left(patter{n}_{s}\right)$ is used as the next serving RRH $\mathrm{s}$ for the UE $u$; the predicted RRH $\tilde{s}$ corresponds to RRH $s$.

Algorithm 1: RRH switching operation with mobility prediction and SINR-based user association. |

1: calculate $EE$ using Equation (7); set $E{E}_{curr}=EE$ and $E{E}_{prev}=0$ |

2: calculate $P\left(patter{n}_{s}\right)$ for all RRH $s$ using Equation (9) |

3: calculate ${\rho}_{s}$ for all RRH $s$ using Equation (10) |

4: while $E{E}_{curr}>E{E}_{prev}$ do |

5: set $E{E}_{prev}=E{E}_{curr}$ |

6: if $\sum}_{u=1}^{U}{\gamma}_{u}>RB,\forall s$ then |

7: deactivate the activated RRH $s$ that has the largest ${\rho}_{\tilde{s}}$ |

8: else |

9: activate the deactivated RRH $s$ that has the smallest ${\rho}_{\tilde{s}}$ |

10: end if |

11: do user association for all UE $u$ with an active RRH providing the highest SINR |

12: calculate $E{E}_{curr}$ using Equation (7) |

13: end while |

14: solve Equation (8) using Algorithm 2 |

15: calculate $EE$ using Equation (7) |

#### 4.2. Optimization of Transmission Power Based on a Gradient Method

Algorithm 2: Optimization of the transmission power based on the gradient method. |

1: set the maximum iteration number $K$ and convergence condition $\u03f5$ |

2: set the positive step size $\alpha $ |

3: set ${p}_{t}^{k}$ = ${p}_{t}^{s}$, ${p}_{t}^{k+1}=0$, $k=1$, ${p}_{t}^{\ast}={p}_{t}^{k}$ calculate $EE\left({p}_{t}^{k}\right)$ using Equation (8) |

4: for $1\le k\le K$ do |

5: set $k=k+1$ |

6: ${p}_{t}^{k+1}={p}_{t}^{k}+\alpha \xb7{\mathsf{\eta}}^{k}\left({p}_{t}^{k}\right)$ |

7: if $EE\left({p}_{t}^{k+1}\right)>EE\left({p}_{t}^{k}\right)$ then |

8: ${p}_{t}^{\ast}$ = ${p}_{t}^{k+1}$. |

9: else if $EE\left({p}_{t}^{k+1}\right)-EE\left({p}_{t}^{k}\right)\le \u03f5$ then |

10: break |

11: end if |

12: set $\alpha =\alpha /k$ |

13: end for |

## 5. Results and Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Comparison of energy efficiencies. (

**a**) San Francisco and (

**c**) Beijing datasets with Gaussian distributions; (

**b**) San Francisco and (

**d**) Beijing datasets with uniform distributions.

**Figure 3.**Comparison of the average SINRs. (

**a**) San Francisco and (

**c**) Beijing datasets with Gaussian distributions; (

**b**) San Francisco and (

**d**) Beijing datasets with uniform distributions.

**Figure 4.**Comparison of the online and offline algorithm with 500 UEs. (

**a**) The number of suboptimal served users with Gaussian distribution and (

**b**) with uniform distribution; (

**c**) blocking probability with Gaussian distribution and (

**d**) with uniform distribution.

**Figure 5.**Comparison of the energy efficiencies. (

**a**) 2x and (

**c)**4x speeds with Gaussian distributions; (

**b**) 2x and (

**d**) 4x speeds with uniform distributions.

**Figure 6.**Comparison of the energy efficiencies in environments with 15 RRHs. (

**a**) San Francisco and (

**c**) Beijing datasets with Gaussian distributions; (

**b**) San Francisco and (

**d**) Beijing datasets with uniform distributions.

Parameter | Notation | Value |
---|---|---|

Channel bandwidth | $B$ | 100 $\mathrm{MHz}$ |

Noise power spectral density | ${N}_{0}$ | −174 ($\mathrm{dBm}/\mathrm{Hz}$) |

Requirement threshold of SINR | $SIN{R}_{th}$ | 0 ($\mathrm{dB}$) |

Maximum outage probability | ${P}_{outage}^{max}$ | 0.05 |

Number of resource blocks | $RB$ | 50 |

Maximum transmission power of RRH | ${p}_{t}^{max,s}$ | 30 $\mathrm{dBm}$ |

Constant power of active RRH | ${\mathsf{\Phi}}_{RRH}$ | 6.8 $\mathrm{W}$ |

Power of sleep RRH | ${\mathsf{\Phi}}_{Sleep}$ | 4.3 $\mathrm{W}$ |

Slope of RRH | $\Delta \mathrm{s}$ | 4.0 |

Constant power of backhaul link | ${P}_{bh}$ | 13.25 ${\mathrm{W}}^{3}$ |

Power consumption per bit/s of fronthaul link | $\beta $ | 0.83 $\mathrm{W}$ |

Constant power of fronthaul link | ${\mathsf{\Phi}}_{fh}$ | 13 $\mathrm{W}$ |

Minimum required data rate of UE | ${d}_{u}$ | 512, 1024, 1536, 2048, 2560 $\mathrm{kbps}$ |

Maximum iteration number | $K$ | 100 |

Positive step size | $\alpha $ | 0.001 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Park, H.; Lim, Y. Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network. *Sensors* **2018**, *18*, 2904.
https://doi.org/10.3390/s18092904

**AMA Style**

Park H, Lim Y. Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network. *Sensors*. 2018; 18(9):2904.
https://doi.org/10.3390/s18092904

**Chicago/Turabian Style**

Park, Hyebin, and Yujin Lim. 2018. "Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network" *Sensors* 18, no. 9: 2904.
https://doi.org/10.3390/s18092904