# An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

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## Abstract

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## 1. Introduction

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- The proposed energy saving scheme is further elaborated and detailed algorithms from the aspects of state management of BSs and session management of UEs are proposed.
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- A thorough optimization problem for the proposed energy saving scheme is formulated for the performance analysis.
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- Particle swarm optimization is applied to practically solve the formulated optimization problem.
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- Extensive numerical examples are obtained through simulations developed by the authors and analyzed in detail.

## 2. Proposed Energy Saving Scheme

_{r,j}) is the number of UEs that request high rate data traffic and n(u

_{I,j}) is the number of UEs that exist in the overlapping areas commonly covered by the BS and the neighbor BSs. An MBS manages all of the related information about SBSs within the coverage of the MBS. If n(u

_{r,j}) of SBS j is larger than a threshold value of P

_{thr}, or n(u

_{I,j}) of SBS j is larger than a threshold value of I

_{thr}, SBS j switches to sleep state to save energy. Otherwise, SBS j switches to off state.

## 3. Performance Analysis

_{macro}and power consumption by SBSs under the coverage of an MBS, ${{\displaystyle \sum}}_{j=0}^{m-1}P{\left(j\right)}_{small}$, and power consumption due to switching between different states, P

_{switching}and it is denoted as P

_{total}as in Equation (1):

_{j,i}, and the value of it is defined as in Equation (2), where we note that BS

_{m}corresponds to an MBS and BSj ($0\le j\le m-1)$ corresponds to an SBS. We assume that all UEs under the coverage area of an MBS are associated with the MBS.

_{i}, and the value of it is defined as in Equation (3):

_{i}and the value of it is defined as in Equation (4):

_{j}is related with whether the BS j is in on state or not and e

_{j}is related with whether the BS j is in sleep state or not in Equation (6):

_{j}and s

_{j}, and the values of them are defined as in Equations (7) and (8):

_{small}, is obtained as in Equation (9):

_{macro}, is obtained as in Equation (11) as the sum of fixed power consumption and load-dependent power consumption.

_{switching}, is obtained as in Equation (13):

_{aggregate}, which is defined as the time needed to get service from a BS by a UE, is additionally introduced as a performance measure as in Equation (23), where the T

_{switching}is defined as average time needed to make a transition between states of an SBS and the T

_{connect}is defined as average connection initiation time between a UE and a BS.

_{switching}can be obtained as in Equation (24), where the T

_{on-off}is the time needed to make a transition from off state to on state by an SBS and the T

_{on-sleep}is the time needed to make a transition from sleep state to on state.

_{connect}is obtained as in Equation (25), where T

_{c}is defined as the time needed to initiate a connection between a UE and a BS.

**Pb**and the value of the objective function applying

**Pb**is defined as P

_{best}. In each P

_{best}, the next P

_{best}is obtained by using velocity ${\mathit{V}}_{\mathit{i}}=\left({\mathit{v}}_{\mathit{i}\mathbf{1}},{\mathit{v}}_{\mathit{i}\mathbf{2}},{\mathit{v}}_{\mathit{i}\mathbf{3}},\cdots ,{\mathit{v}}_{\mathit{i}\mathit{D}}\right),$ and the value of P

_{best}is updated and stored at

**Pb**. In each swarm, the best value of P

_{best}is defined as G

_{best}, and it is stored in Gb. Equation (26) shows the update of velocity.

**Pb**, the values of ${\mathit{v}}_{k}^{nm}\left(t+1\right)$ are normalized using Equation (27):

Algorithm 1. PSO Process | |

1: | Initialize a, z, v, p, Pb, Gb, Pbest, Gbest, d, r, w, e, f, s |

2: | // a is n $\times $ m + 1 matrix of current association between UE and BS. |

3: | // z is swarm size. |

4: | // v is z $\times $ n $\times $ m + 1 matrix of velocity of particle. |

5: | // p is z $\times $ n $\times $ m + 1 matrix of particle of selected association between UE and BS. |

6: | // Pb is z $\times $ n $\times $ m + 1 of local optimum. |

7: | // Gb is n $\times $ m + 1 matrix of global optimum. |

8: | // Pbest is 1 $\times $ z matrix of fitness value of Pb. |

9: | // Gbest is the best fitness value of Pbest. |

10: | // d is 1 $\times $ n matrix of the data traffic demand of UE. |

11: | // r is 1 $\times $ n matrix of the type of required data traffic of UE. |

12: | // w is 1 $\times $ m + 1 matrix of on state of BS. |

13: | // e is 1 $\times $ m matrix of sleep state of BS. |

14: | // f is 1 $\times $ m matrix of state transition of SBS between on and off. |

15: | // s is 1 $\times $ m matrix of state transition of SBS between on(off) and sleep. |

16: | for iteration < PSO_iteration do |

17: | for p_index < z do |

18: | // Calculate velocity using the Equations (26) and (27). |

19: | v‘ = 1/(1 + exp(wg $\times $ v [p_index,:,:] + c1 $\times $ r1 $\times $ (Pb[p_index,:,:] − a) + c2 $\times $ r2 $\times $ (Gb − a))); |

20: | // Algorithm 2: Find particle |

21: | [p[p_index,:,:], w‘] = FindParticle(v‘, d, r); |

22: | // Algorithm 3: Find state information of BS |

23: | [e‘, f, s] = FindBSStateInformation(w, e, w‘); |

24: | // Calculate power consumption |

25: | Psmall = 0, SumPsmall = 0, Pmacro = 0, Pswitching = 0, Pprocessing = 0; |

26: | for j < m+1 do |

27: | // Calculate power consumption of BS due to traffic processing. |

28: | if j == m |

29: | // Calculate the traffic load dependent power consumption of MBS |

30: | // using the Equation (12). |

31: | for i < n do |

32: | Pmacro += Ptx_m $\times $ d[i] $\times $ p[p_index,i,j] $\times $ (r[i] $\times $ Ch + (1 − r[i]) $\times $ Cl)/Cmax_m; |

33: | end |

34: | // Calculate the power consumption of MBS using the Equation (11). |

35: | Pmacro = Pf_m + rho_m$\times $Pmacro |

36: | else |

37: | // Calculate the traffic load dependent power consumption of SBS |

38: | // using the Equation (10). |

39: | Psmall = 0; |

40: | for i < n do |

41: | Psmall += Ptx_s$\times $d[i]$\times $p[p_index,i,j]$\times $(r[i]$\times $Ch+(1-r[i])$\times $Cl)/Cmax_s; |

42: | end |

43: | // Calculate the power consumption of SBS using the Equation (9). |

44: | Psmall = w‘[j] $\times $ (1 − e‘[j]) $\times $ (Pf_s + rho_s $\times $ Psmall)+(1 − w‘[j]) + e‘[j] $\times $ Pe; |

45: | SumPsmall += Psmall; |

46: | end |

47: | // Calculate power consumption due to state switching using the Equation (13). |

48: | Pswitching += f[j] $\times $ (1 − s[j]) $\times $ Pon_off + (1 − f[j]) $\times $ s[j] $\times $ Pon_sleep |

49: | end |

50: | // Calculate total power consumption using the Equation (1). |

51: | Ptotal = Pmacro + SumPsmall + Pswitching; |

52: | // Set Pb and Pbest if the fitness value is lower than the Pbest and update v. |

53: | if Ptotal < Pbest |

54: | Pb[p_index,:,:] = p[p_index,:,:]; |

55: | Pbest[p_index] = Ptotal; |

56: | v[p_index,:,:] = v‘; |

57: | end |

58: | // Choose the Pb with the best Pbest of all the particles as the Gbest. |

59: | // Update BS state information. |

60: | if Pbest < Gbest |

61: | Gb = Pb[p_index]; |

62: | Gbest = Pbest[p_index]; |

63: | BSstate = [w‘, e‘]; |

64: | end |

65: | end |

66: | end |

67: | [w, e] = BSstate; |

68: | a = Gb; |

Algorithm 2. Find Particle | |

1: | FindParticle(v‘, d, r) |

2: | Initialize w, p |

3: | // Apply the Equations (16)–(18). |

4: | for i < n do |

5: | // Set association if a UE demands low rate data service. |

6: | if d[i] == 1 and r[i] == 0 |

7: | p[i, m] = 1; |

8: | w[m] = 1; |

9: | continue; |

10: | end |

11: | for j < m + 1 do |

12: | // Set association if a UE demands high rate data service. |

13: | if d[i] == 1 and r[i] == 1 |

14: | // Extract the k of the best index of BS using the Equation (29). |

15: | extract k |

16: | end |

17: | end |

18: | // Serve UE’s demand using MBS. |

19: | if k does not exist or does not meet the condition of the Equation (14). |

20: | p[i, m] = 1; |

21: | w[m] = 1; |

22: | // Serve UE’s demand using SBS. |

23: | else |

24: | p[i, k] = 1; |

25: | w[k] = 1; |

26: | end |

27: | end |

28: | return [p, w]; |

Algorithm 3. Find State Information of BS | |

1: | FindBSStateInformation(w, e, w‘) |

2: | Initialize e, f, s |

3: | // Apply the Equations (19) and (20). |

4: | for j < m do |

5: | // u_r is the number of UEs that request high rate data traffic. |

6: | // u_I is the number of UEs that exist in the overlapping areas commonly covered |

7: | // by the BS and the neighbor BSs. |

8: | if u_r[j] > Pthr and u_I[j] > Ithr and w‘[j] != 1 |

9: | e[j] = 1; |

10: | end |

11: | // Find state switching of BS. |

12: | if w[j] != w‘[j] and e[j] == e[j] |

13: | f[j] = 1; |

14: | continue; |

15: | end |

16: | if w[j] == w‘[j] and e[j] != e[j] |

17: | s[j] = 1; |

18: | continue; |

19: | end |

20: | end |

21: | return [e, f, s]; |

## 4. Numerical Results

## 5. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 4.**Conventional energy saving operation of BSs in the modified separation architecture (

**a**) before energy saving; (

**b**) after energy saving.

**Figure 5.**Proposed energy saving operation of BSs in the modified separation architecture (

**a**) before energy saving; (

**b**) after energy saving.

Type | Basic Separation Architecture | Modified Separation Architecture | |
---|---|---|---|

Control Signal | Macro Cell Base Station | Macro cell Base Station | |

Data Traffic | Low Rate Data | Small Cell Base Station | Macro cell Base Station |

High Rate Data | Small Cell Base Station | Small cell Base Station |

Parameter | Description |
---|---|

$n$ | number of UEs |

$m$ | number of SBSs |

${u}_{i}$ | a UE i |

${B}_{j}$ | a BS j |

${C}_{h}$ | capacity needed to serve a high rate data traffic service |

${C}_{l}$ | capacity needed to serve a low rate data traffic service |

${C}_{S}^{max}$ | maximum capacity of an SBS |

${C}_{M}^{max}$ | maximum capacity of an MBS |

${\rho}_{S}$ | slope of the load-dependent power consumption in an SBS |

${\rho}_{M}$ | slope of the load-dependent power consumption in an MBS |

${P}_{S}^{f}$ | fixed power consumption of an SBS in on state |

${P}_{S}^{e}$ | fixed power consumption of an SBS in sleep state |

${P}_{M}^{f}$ | fixed power consumption of an MBS in on state |

${P}_{S}^{max}$ | maximum power consumption of an SBS |

${P}_{M}^{max}$ | maximum power consumption of an MBS |

${P}_{on-off}$ | power consumption for a switching between on and off states in an SBS |

${P}_{on-sleep}$ | power consumption for a switching between on and sleep states in an SBS |

${T}_{on-off}$ | delay for a switching between on state and off state in an SBS |

${T}_{on-sleep}$ | delay for a switching between on state and sleep state in an SBS |

${T}_{c}$ | delay for a new connection initiation between a UE and a BS |

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

simulation time | 24 h |

number of SBSs | 50 |

number of UEs | 250 |

service time of traffic | U[0, 2.5] min |

inter-arrival time of traffic | U[5, 10] min |

ratio of high data | 0.5 |

speed of UE | U[0, 100] km/h |

${P}_{S}^{f}$ | 21.6 W |

${P}_{S}^{e}$ | 5.4 W |

${P}_{S}^{max}$ | 27 W |

${P}_{M}^{f}$ | 780 W |

${P}_{M}^{max}$ | 1350 W |

${P}_{on-off}$ | 54 W |

${P}_{on-sleep}$ | 13.5 W |

${C}_{h}$ | 1 Mbps |

${C}_{l}$ | 0.01 Mbps |

${C}_{S}^{max}$ | 25 Mbps |

${C}_{M}^{max}$ | 500 Mbps |

${P}_{S}^{tx,max}$ | 1.3 W |

${P}_{M}^{tx,max}$ | 120 W |

${\rho}_{S}$ | 4.15 W/Mbps |

${\rho}_{M}$ | 4.5 W/Mbps |

${T}_{on-off}$ | 0.532 s |

${T}_{on-sleep}$ | 0.00216 s |

${T}_{c}$ | 0.05 s |

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

PSO iteration | 200 |

number of swarm | 5 |

${v}_{max}$ | 4 |

${v}_{min}$ | −4 |

${c}_{1}$ | 0.5 |

${c}_{2}$ | 0.5 |

${r}_{1}$ | 0.5 |

${r}_{2}$ | 0.5 |

$wg$ | 0.25 |

© 2017 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/).

## Share and Cite

**MDPI and ACS Style**

Kang, M.W.; Chung, Y.W.
An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization. *Energies* **2017**, *10*, 1417.
https://doi.org/10.3390/en10091417

**AMA Style**

Kang MW, Chung YW.
An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization. *Energies*. 2017; 10(9):1417.
https://doi.org/10.3390/en10091417

**Chicago/Turabian Style**

Kang, Min Wook, and Yun Won Chung.
2017. "An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization" *Energies* 10, no. 9: 1417.
https://doi.org/10.3390/en10091417