# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

_{2}emissions as a measure of “greenness,” but CNs’ carbon emissions are negligible. Apart from the carbon emissions issue, there are other drivers of “green” wireless technology that include financial gains (lower energy costs). With the adoption of economic gains in the “green” concept, energy savings or energy efficiency appears to be a more appropriate way to measure “greenness.” Hence, the concept of “green communication” technology in wireless systems can be made meaningful with a comprehensive evaluation of energy savings and performance in a practical system [8]. Thus, the goal of green communication is to enhance the EE of BSs, decrease OPEX, and eradicate greenhouse gases. For further information on green wireless communications network, see [9,10,11,12].

#### 1.1. Motivation

- (i)
- According to [13], the world’s yearly electricity usage for the telecommunications sector is increasing and expected to reach 51% of global electricity in 2030 unless the electricity efficiency of wireless access networks and access/data center networks is sufficiently improved. CNs are considered the cardinal contributor to the appreciable rise in energy utilization in the telecommunication sector [14]. Figure 2 shows the expected global electricity usage of cellular networks based on different cellular generations to 2030. BSs are the major consumer of energy in CNs and account for 57% of the total energy consumed [15]. The number of BSs worldwide is increasing, which led OPEX to rise conspicuously because a greater percentage of the OPEX entails electricity bills [16].

- (ii)
- CNOs contribute significantly to greenhouse gas (GHG) emissions. According to [17], the carbon dioxide (CO
_{2}) quantity emitted by the mobile sector is envisaged to increase to 179 MtCO_{2}by 2020, which translates to 51% of the carbon footprint of the information and communication technology (ICT) sector.

#### 1.2. Contributions

- (i)
- To propose a mechanism of cooperation between the LTE and next-generation wireless networks, such as 5G. Thereby, we create an equilibrium between network performance and EE via a 5G BSs switching off/on strategy driven by the network instantaneous traffic load demand while guaranteeing service coverage for mobile subscribers by the remaining active LTE BSs. The proposed BSs switching on/off decision-making algorithm is presented in Section 3.1.
- (ii)
- To determine the optimum criteria of the active LTE BSs (transmission power, the total antenna gain, bandwidth/spectrum, and SINR) that achieves the maximum coverage for the entire area during the 5G BS switch-off session.

#### 1.3. Paper Organization

## 2. Related Work

## 3. System Model and Problem Formulation

#### 3.1. System Model and Proposed BSs Switching On/Off Mechanism

#### 3.1.1. High Traffic Load (0.4 < λ ≤ 1)

#### 3.1.2. Low Traffic Load (0 < λ ≤ 0.4)

#### 5G BSs Switch-Off Procedures

- (i)
- Pre-processing Status: The 5G BS is assigned with the task of monitoring the generated traffic load often (e.g., every few minutes) and decides if it would be best, performance-wise, to turn off 5G BSs, depending on the given traffic load. In situations where the traffic load decreases below a given benchmark (λ ≤ 0.4) and remains below it for a specific duration, then the 5G BSs switch-off decision algorithm can be executed. The 5G BSs are the sole determinant of this information.
- (ii)
- Decision Status: At the onset, a unicast control signal is sent from the 5G BSs to the switching off/on module located in the central office requesting that they be switched off. On the receipt of a go-ahead response signal from the module, the 5G BSs initiate a gradual decrease in their transmission power mechanism that will ultimately result in a switch-off. Meanwhile, UEs that are in control of the switched-off 5G BSs are reassigned to active LTE BSs within the neighborhood. Of course, this will be determined by the signal strength. This procedure is akin to the current handover scheme, the only difference being that this involves handing over group of UEs other than a single UE. Considerable research has been performed on group handovers. Most of the relevant studies targeted the support of commuters on mass transit systems like buses and trains. There might be a need for a strategy to predict the group handover a priori. In this case, one of the current group handover techniques [34], in conjunction with our proposed switching-off algorithm, may be adopted for the implementation of our group handover policy.
- (iii)
- Post-processing Status:The 5G BSs that are switched off listen for the wake-up control signal from the switching off/on server.. Meanwhile, the active LTE BSs are in full functionality and are capable of supplying subscribers’ QoS requirements. In addition, LTE BSs are mandated to monitor traffic across the network based on LTE BSs’ information, which is based on the decision to keep the 5G BSs off or start switching them on if the traffic increases to more than 0.4.

#### II. 5G BSs Switching-On Procedures

- (i)
- Pre-processing Status: In this phase, it is the duty of the LTE BSs to continuously monitor the generated traffic load and make a decision on whether the 5G BSs should be switched on or off. If the current load is more than the stated threshold of 0.4 and stays over the threshold for a given duration, it is plausible to invoke the 5G BSs switching-on decision. This decision is solely dependent on LTE BSs signal information.
- (ii)
- Decision Status: Having analyzed the instantaneous traffic load, the LTE BSs initiate a unicast control signal to be sent to the switching-off/on module, requesting that the 5G BSs be switched on. After the traffic load has been analyzed and the switching-off/on module deems it necessary, multicast signals are then transmitted to wake up the 5G BSs’ control signals. Next, the 5G BSs begin to gradually increase their transmission power and, subsequently, propagate a unicast signal to the switching-off/on module containing the response to the switch-on request. Using the signal strength paths metrics, the UEs serviced by the LTE BSs are handed over to the active 5G BSs (small cells). This transfer is solely determined by the signal strength path of the UE_BS. Meanwhile, LTE BSs remain active to support the 5G BSs while guaranteeing coverage and radio services.
- (iii)
- Post-processing Status: In this status, there is a role reversal in which the post-processing phase of the switching-on algorithm assumes the role and duties of the pre-processing state of the switching-off algorithm already discussed. In this phase, the 5G BSs become active and continue the task of monitoring the UE load traffic.

#### 3.2. Problem Formulation and Mathematical Modeling

#### 3.2.1. Propagation Channel Model

_{rx}) propagation model can be written as follows [35]:

_{r}= P

_{tx}+ G − P

_{Loss}− σ,

_{tx}, G, P

_{Loss}, σ, denote device transmitted power, total antenna gains, path loss, shadow fading margin, respectively.

#### LTE Path Loss Model

_{BS}), UE antenna height (h

_{UE}), average building height (h

_{bl}), street width (w

_{st}), and radius (R) in meters. The function is given as follows:

#### II. 5G Path Loss Model

_{L_mod_SUI}) for a frequency of 28 GHz for Non-Line of Sight (NLOS) is proposed, as given in Equation (3). This mathematical model (the modified SUI path loss model) is constructed based on extensive empirical measurements [37].

_{L_mod_SUI}(d) = α

_{NLOS}× (P

_{L_SUI}(d) − P

_{L_SUI}(d

_{o})) + P

_{L}(d

_{o}) + X

_{σ},

_{NLOS}is the mean slope correction factor, obtained directly from the NLOS empirical results; P

_{L_SUI}(d) is an original SUI model at a distance d; P

_{L_SUI}(d

_{o}) is an original SUI model at a reference distance d

_{o}; P

_{L}(d

_{o}) represents free space path loss (dB) in close-in reference distance d

_{o}; and X

_{σ}is a typical lognormal random shadowing with variable mean of (0dB) and standard deviation (σ). The original SUI model P

_{L_SUI}(d) for a frequency above 2 GHz is [37]

_{L_SUI}(d) = P

_{L}(d

_{o}) + 10nlog

_{10}(d/d

_{o}) + X

_{fc}+ X

_{RX}+ X

_{σ},

_{fc}and X

_{RX}signify the correction factors for frequency and receiver heights, respectively; f

_{MHz}denotes carrier frequency (MHz); and h

_{TX}and h

_{RX}denote the transmitter and receiver antenna heights in meters, respectively. The parameters a, b, and c are constants that are service area. The SUI terrain type A is considered, with parameters given as a = 4.6, b = 0.0075, and c = 12.6 [37].

_{min}. In reality, signal degradation factors of random shadowing and path loss will lead to some cell portions having a received power below P

_{min}. Given by [38],

_{min}= N

_{o}BW + N

_{f}+ SINR + IM,

_{o}BW connotes thermal noise level assigned for a specified noise bandwidth; N

_{f}is the receiver noise figure; while IM connotes implementation margin.

#### 3.2.2. Cell Coverage

_{ϕ}is the standard deviation of the shadow fading component; and α is a path loss exponent component. In Equation (10), the functional representation of the cell coverage area is denoted by $C=f\left(a,b\right)=f\left({P}_{\mathrm{min}},{P}_{rx},\alpha ,{\sigma}_{\phi}\right)$, where the ${P}_{\mathrm{min}}=f\left({N}_{o},BW,{N}_{f},SINR,IM\right)$ and the received power is ${P}_{rx}=f\left({P}_{tx},G,L,\sigma \right)$. The problem formulation is described as follows:

_{tx}, BW, G, SINR, and σ constraints. Moreover, the MCS, data rate, and EE were taken into consideration. The PSO approach are equipped with the following attributes: flexibility, adaptability to the problems, strong global search ability, and robust performance. PSO performance is comparable to that of genetic algorithms or the ant colony algorithm because it is faster and less complicated. PSO has also been successfully applied to a wide variety of problems. Moreover, PSO is simple to implement and is a very efficient global optimizer for continuous variable problems. See [40,41,42,43] for applications in which PSO has been successfully applied to the field of wireless communication networks.

#### 3.2.3. Data Rate

_{Slot}is the slot time.

#### 3.2.4. Energy Efficiency

_{TRX}is the number of transceiver, which can be computed as follows [45]:

_{TRX}= N

_{Carr}× N

_{Sect}× N

_{Ant},

_{Carr}, N

_{Sect}, and N

_{Ant}denote the number of carriers, sectors, and antennas, respectively.

## 4. Optimization Programming and Simulation Setup

#### 4.1. PSO Heuristic Algorithm

_{i}= (x

_{i}

_{1}, x

_{i}

_{2}, ..., x

_{in}); velocity array, V

_{i}= (v

_{i}

_{1}, v

_{i}

_{2}, ..., v

_{in}); best previous position, P

_{i}= (p

_{i}

_{1}, p

_{i}

_{2}, ..., p

_{in}); and the global best position, P

_{g}= (p

_{g}

_{1}, p

_{g}

_{2}, ..., p

_{gn}) [46]. The velocity and position of each particle are updated as follows:

_{1}and r

_{2}are random numbers; c

_{1}is the self-recognition component coefficient; c

_{2}is the social component coefficient; and the choice of the values c

_{1}= c

_{2}= 2 is generally referred to as the learning factors. The following weighting function is usually utilized in Equation (21):

_{max}is the initial weight; w

_{min}is the final weight; iter is the current iteration number; and iter

_{max}is the maximum iteration number.

#### 4.2. Pseudocode of the Proposed Scheme

#### 4.3. Simulation Setup

_{tx}, BW, G, SINR, and σ. More details of the simulation parameters are given in Table 1.

## 5. Results and Discussion

_{tx}= 43.08 dB

_{m}and G = 7.97 dB, at SINR = 1.49 dB and σ = 5.50 dB, as shown in Figure 6. From Figure 6, when the SINR decreased, both P

_{tx}and G increased to maintain the maximum coverage area. The simulation results demonstrated that the optimum transmitting power P

_{tx}and the antenna gain G that maintain maximum coverage at the edge (where the SINR was the lowest at −5.1 dB and where the shadowing is at 4.8 dB) are 43.1 dB

_{m}and 6.3 dB, respectively. In addition, the optimal BW is 10 MHz during the period that the 5G BSs are switched off; the optimal BW is proportional to P

_{min}, as given in Equation (11). However, for a high data rate, a BW greater than 10 MHz can be used. In this case, the full coverage is not secure because the P

_{min}required is high. It is plausible that big bandwidth has the possibility of improving the EE when compared to a small bandwidth within the same size coverage area based on the premise that big bandwidth can support more resource blocks, eventually leading to a higher data rate.

_{min}, and the MCS. The received power level is obtained from the sensitivity calculation in Equation (9), and the values of SINR, IM, etc., are retrieved from Table 1.

_{min}reduces, the MCS reduces simply because the demodulation error rate rises as a result of the rise in both the noise and interference. This analysis is of a cell radius of 500 m (edge of the LTE cell), denoting a cell experiencing a low-traffic case; the lowest modulation rate (QPSK) supports a cell radius of 500 m. For LTE with a 10 MHz BW, this case involves 50 resource blocks (RBs) with each RB including 12 subcarriers, each subcarrier having seven symbols for normal CP, and the time of the slot set to 0.5 ms. Hence, the total number of symbols per RB is 12 × 7 × 2 = 168 symbols per ms. Therefore, 8400 symbols per ms are identified in this case. When 1/8 QPSK is used (2 bits per symbol), the data rate will be 2.1 Mbps for a single chain, and with 2 × 2 MIMO (2T, 2R), the data rate will be twice that of a single chain, i.e., 4.2 Mbps, for the worst case of SINR. Figure 10 shows the data rate versus cell radii, with P

_{tx}= 43.1 dB

_{m}and BW = 10 MHz.

- (i)
- Without the switch-off, all BSs are active for 24 h:$$\begin{array}{l}{E}_{Cons}^{day}={\displaystyle \sum \left({N}_{BS}^{active}\times {P}_{op}^{BS}\right)\times \mathrm{day}\text{}\mathrm{time}}\\ =\left[\left(1\text{}\mathrm{LTE}\text{}\mathrm{BSs}\times {P}_{op}^{LTE}\right)+\left(7\text{}5\mathrm{G}\text{}\mathrm{BSs}\times {P}_{op}^{5G}\right)\right]\times 24\text{}\mathrm{h}\\ =\left[\left(1\times 584.2\right)+\left(7\times 46.57\right)\right]\times 24=21.84\text{}\mathrm{kWh}.\end{array}$$
- (ii)
- With the proposed switch-off:$${E}_{Cons}^{day}=13[\underset{\mathrm{high}\text{}\mathrm{traffic}}{\underbrace{\underset{\mathrm{LTE}\text{}\mathrm{BSs}\text{}(\mathrm{on})}{\underbrace{\left(1\times 584.2\right)}}+\underset{5\mathrm{G}\text{}\mathrm{BSs}\text{}(\mathrm{on})}{\underbrace{\left(7\times 46.57\right)}}}}]+11[\underset{\mathrm{low}\text{}\mathrm{traffic}}{\underbrace{\underset{\mathrm{LTE}\text{}\mathrm{BSs}\text{}(\mathrm{on})}{\underbrace{\left(1\times 584.2\right)}}+\underset{5\mathrm{G}\text{}\mathrm{BSs}\text{}(\mathrm{off})}{\underbrace{\left(7\times 0.9\right)}}}}]=18.32\text{}\mathrm{kWh}.$$

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Expected growth of global mobile subscribers and data traffic [2]. (

**a**) Expected growth of global mobile subscribers and (

**b**) expected traffic growth and the percentage of each type of data.

**Figure 9.**Cell radii versus receiver sensitivity power for different MCSs, with P

_{tx}= 43.1 dB

_{m}and BW = 10 MHz.

**Figure 10.**Data rate versus macrocell radii, with P

_{tx}= 43.1 dB

_{m}, BW = 10 MHz, and 2 × 2 MIMO.

**Figure 12.**Data rate versus different BWs values for different numbers of antennas at the edge of 5G small cell.

Item | Parameter | Acronym | LTE | Unit |
---|---|---|---|---|

Network parameters | Frequency | f | 2.6 | GHz |

Bandwidth | BW | 1.4–20 | MHz | |

Cell radius | R | 0.5 | km | |

Base station parameters | Transmission power | ${P}_{tx}^{\mathrm{min}}$–${P}_{tx}^{\mathrm{max}}$ | 10–40 | W |

40–46 | dB_{m} | |||

Antenna height | h_{BS} | 20 | m | |

Antenna gain | G_{min}–G_{max} | 5–10 | dB | |

Number of antennas | N_{Ant.} | 2 | # | |

Number of sectors | N_{Sect.} | 3 | # | |

Number of carriers | N_{Carr} | 1 | # | |

Mobile station parameters | Thermal noise density | N_{o} | 174 | dB_{m}/Hz |

Noise figure | N_{f} | 9 | dB | |

Implementation margin | IM | 3 | dB | |

Antenna height | h_{UE} | 1.5 | M | |

Propagation losses | Morphology | Uurban | ||

Propagation model | 3GPP UMa-NLOS | |||

Avg. building height | h_{bl} | 20 | m | |

Street width | W_{st} | 20 | m | |

SINR | SINR_{min} | −5.1 | dB | |

SINR_{max} | 18.6 | |||

Shadow fading margin | σ | 4–8 | dB | |

Exponent path loss | α | 3.2 | # |

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## Share and Cite

**MDPI and ACS Style**

Alsharif, M.H.; Kelechi, A.H.; Kim, J.; Kim, J.H.
Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks. *Symmetry* **2019**, *11*, 408.
https://doi.org/10.3390/sym11030408

**AMA Style**

Alsharif MH, Kelechi AH, Kim J, Kim JH.
Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks. *Symmetry*. 2019; 11(3):408.
https://doi.org/10.3390/sym11030408

**Chicago/Turabian Style**

Alsharif, Mohammed H., Anabi Hilary Kelechi, Jeong Kim, and Jin Hong Kim.
2019. "Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks" *Symmetry* 11, no. 3: 408.
https://doi.org/10.3390/sym11030408