#
Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks^{ †}

^{†}

## Abstract

**:**

_{op}per MNO only its to outdoor macro UEs, whereas the total spectrum of all MNOs of the country B

_{co}to its small cells indoor per building such that technically any small indoor cell of an MNO can have access to B

_{co}instead of merely B

_{op}assigned only to the MNO itself. We develop an interference management strategy as well as an algorithm for the proposed technique. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived, and a generic model for energy efficiency is presented. An optimal amount of small indoor cell density in terms of the number of buildings L carrying these small cells per MNO to trade-off the spectral efficiency and the energy efficiency is derived. With the system-level numerical and simulation results, we define an optimal value of L for a dense deployment of small indoor cells of an MNO and show that the proposed spectrum sharing technique can achieve massive indoor capacity, spectral efficiency, and energy efficiency for the MNO. Finally, we demonstrate that the proposed spectrum sharing technique could meet both the spectral efficiency and the energy efficiency requirements for 5G mobile networks for numerous traffic arrival rates to small indoor cells per building of an MNO.

## 1. Introduction

#### 1.1. Background

#### 1.2. Existing Literature

#### 1.3. Contribution

#### 1.4. Organization

#### 1.5. Declaration

## 2. System Model

#### 2.1. System Architecture

_{m}= 4 of a nation. Each MNO is assumed to consist of a number of outdoor, indoor and offloaded UEs. A certain percentage of total macro UEs of each MNO is considered indoor, and a number of macro UEs are considered offloaded to picocells. Each MNO is assumed to deploy in-building small cells to provide good indoor coverage and the number of small cells per MNO varies from one building to another. Each MNO has a dedicated spectrum of B

_{op}and B

_{op}of each of the x

_{m}MNOs are aggregated to become the total spectrum of all MNOs of the country B

_{co}. B

_{op}of each MNO is aggregated to a common pool where a centralized scheduler manages sharing and allocating B

_{co}to all operators. Note that due to the necessity of a new entity to the centralized management of the radio spectrum, such a common spectrum polling could be provided with an additional cost from the deployment, by either the national regulatory authority (NRA), or any third party, or operators under certain common sharing and negotiation among them. In summary, the followings are assumed for the system: (1) Each MNO has a dedicated licensed spectrum B

_{op}, (2) B

_{op}would vary with an operator’s requirement, and (3) There are x

_{m}operators, and hence, the x

_{m}spectrum bands exist in the system.

#### 2.2. Proposed Spectrum sharing Technique and Related Issues

_{op}of one system with the other, simultaneously. To overcome this problem, in this paper, we propose a novel spectrum sharing technique by considering that the dedicated spectrum B

_{op}per MNO is allocated to outdoor macro UEs of any MNO. However, for small indoor cells, B

_{co}is made accessible by each small cell of all MNOs of the country with an equal priority such that technically any small indoor cell of each MNO could have access to the maximum spectrum of the whole B

_{co}instead of the merely dedicated B

_{op}only for any outdoor UE of the corresponding MNO, as shown in Figure 2. This results in achieving massive indoor capacity by an MNO.

_{co}anywhere anytime by small indoor cells of any MNO subject to satisfying certain conditions in order to avoid CCI among MNOs, we refer the proposed spectrum sharing scheme as the indoor ubiquitous spectrum sharing technique. By adopting such a technique, each MNO can pay only whatever the amount of spectrum it uses to serve its indoor users over a certain duration of time instead of paying the whole spectrum B

_{co}licensing fee. This can ensure both the fairness in the licensing fee and the improvement in overall spectral utilization.

- (1)
- Issue 1: How to address dynamic resource allocation among MNOs to improve the overall spectrum utilization?How to address: The static allocation of the time resource based on the number of MNOs may cause either underutilization or scarcity of the spectrum of an MNO. To address this issue, we consider allocating the time resource to small cells of an MNO dynamically based on the actual traffic arrival rate in order to improve the spectrum utilization.
- (2)
- Issue 2: How to optimize for the fair allocation of the time resource to each MNO when applying the proposed nationwide spectrum sharing technique?How to address: We consider assigning an optimal number of transmission time intervals (TTIs) to an MNO x in proportion with the ratio of the average number of UEs of the MNO x to the sum of the average number of UEs of all other MNOs that are active during any APP.
- (3)
- Issue 3: How to ensure optimality between the spectral efficiency and the energy efficiency performances of an MNO for the proposed technique?How to address: we consider defining the minimum value of L denoted as L
_{min}by using the slope of the energy efficiency curve such that choosing any value of L ≥ L_{min}results in improving both the spectral efficiency and energy efficiency responses of an MNO. The values of spectral and energy efficiencies corresponding to L ≥ L_{min}define the region of optimality for both efficiencies. In other words, depending on the requirements for the spectral and energy efficiencies, an optimal value of L ≥ L_{min}is chosen. - (4)
- Issue 4: How to clarify that the proposed technique is scalable and can meet the spectral and energy efficiencies of the next-generation mobile networks?How to Address: To ensure scalability, we consider sharing the whole spectrum with small cells per building L per MNO such that by increasing the value of L subject to L ≥ L
_{min}more spectral efficiency and energy efficiency could be achieved. Further, in the paper, we show that by adjusting the value of L corresponding to the varying number of TTIs allocated to an MNO during each APP, the spectral efficiency and the energy efficiency requirements for the 5G mobile network could be achieved. - (5)
- Issue 5: How to describe and address the CCI scenarios in the proposed technique. Also, how does the CCI affect the point of optimality for spectral and energy efficiencies?How to address: We describe in detail the CCI due to the presence of multiple MNOs operating at the spectrum using the ABS based enhanced intercell interference coordination (eICIC) technique. Since with the change of CCI, the number of TTIs allocated to an MNO change, an optimal number of TTIs is allocated to each MNO based on the actual traffic demand of the MNO as compared to that of the others. In general, since an increase in CCI results in degrading the spectral and efficiency performances, the minimum point of optimality in terms of L, corresponding to which both the spectral and energy efficiencies intersect one another, shifts leftward resulting in a decrease in the minimum optimal point L
_{min}and vice versa.

## 3. Co-Channel Interference Scenario and Management

#### 3.1. Co-Channel Interference Scenario

_{m}denotes the maximum number of MNOs of a country. Then, for a given MNO x, if any UE of MNOs other than x exists within the coverage of a small cell of MNO x ${s}_{x}\in {\mathit{S}}_{x}=\left\{0,1,2,3,\dots ,{s}_{x,\mathrm{max}}\right\}$ deployed in any building $L\in {\mathit{L}}_{x}=\left\{0,1,2,3,\dots ,{L}_{x,\mathrm{max}}\right\}$, any UE ${u}_{x}\in {\mathit{U}}_{x}=\left\{0,1,2,3,\dots ,{u}_{x,\mathrm{max}}\right\}$ within the coverage of ${s}_{x}\in {\mathit{S}}_{x}$ is served only during non-ABSs. The allocation of non-ABSs to a UE ${u}_{x}\in {\mathit{U}}_{x}$ is defined such that there is a fair allocation of time resources, i.e., TTIs, to UEs of each MNO over an APP, resulting in increasing the number of non-ABSs with an increase in UEs of the MNO x within the coverage of ${s}_{x}\in {\mathit{S}}_{x}$. Note that ${u}_{x,\mathrm{max}}$ and ${s}_{x,\mathrm{max}}$ vary with MNO x and its corresponding $L\in {\mathit{L}}_{x}$.

#### 3.2. Defining Call Arrivals of Small Cells per MNO

_{x}of MNO x, is present within a building is given by,

_{x}number of SBSs are active to serve UEs simultaneously during any time period.

#### 3.3. Co-Channel Interference Management

_{APP}is updated frequently in regular time intervals to update the presence of UEs of other MNOs within the small cell coverage. Further, we assume that if there exists at least a UE of any MNO x, the number of TTIs allocated to that UE must be non-zero to ensure the continuity of services. Hence, using Equations (5)–(7) and above assumptions, by forming and solving the following optimization problem, we can find an optimal value of non-ABSs for the MNO x = 1.

_{x}of any MNO x. The solution of the above optimization problem for the MNO x = 1 is given by,

**Proof:**

**X/**x = 1 in any TTI may not exist in the small cell coverage of MNO 1, ${\lambda}_{\mathrm{T}}$ can be expressed as ${\lambda}_{\mathrm{T}}={\displaystyle \sum _{\psi =1}^{{\psi}_{\mathrm{m}}}{1}_{{\nu}_{\psi}}\left({\lambda}_{\psi}\right)}\hspace{0.17em}{\lambda}_{\psi}$, where ${\nu}_{\psi}\in \left\{{\lambda}_{1},{\lambda}_{2},\dots ,{\lambda}_{{\psi}_{\mathrm{m}}}\right\}$, and $1(\cdot )$ defines that $1(\cdot )=1$ if ${\lambda}_{\psi}$ exists in the set ${\nu}_{\psi}$; otherwise, $1(\cdot )=0$.

_{nABS,x}is given by,

## 4. Performance Metrics Estimation

#### 4.1. Capacity, Spectral Efficiency, and Energy Efficiency Performance

_{co}denote the number of resource blocks (RBs) in B

_{co}. Note that the whole spectrum B

_{co}is aggregated to a common pool where a centralized scheduler manages sharing and allocating M

_{co}RBs to all MNOs. Since, in general, the proposed technique may take advantage of the spatial distribution of UEs of different MNOs in small indoor cells, any MNO may get access to as much spectra as needed out of B

_{co}. Likewise, all other MNOs also make use of the necessary spectrums from the common pool of spectrum of B

_{co}Hz, which in turn results in improved spectral utilization of B

_{co}. Such a common poll of spectrums for the indoor coverage can be provided by either the NRA, or any third party, or under certain conditions concerning sharing and negotiations among MNOs.

_{co}and use the same indoor environment, the system-level performance of small cells of all MNOs would follow the same. Hence, in the following, we evaluate the performance of only one MNO (i.e., MNO 1) operating at the dedicated spectrum B

_{1.}The performance of the rest of the MNOs can be evaluated following the same procedure. Assume that there are 4 MNOs, i.e., x

_{m}= 4, in the system shown in Figure 1 such that $x\in \mathit{X}=\left\{1,2,3,4\right\}$, which are assigned with the dedicated spectrum $\left\{{B}_{1},{B}_{2},{B}_{3},{B}_{4}\right\}$, respectively such that the following holds.

_{1}RBs corresponding to B

_{1}and Q TTIs can be expressed as

**T**.

_{co}in ${t}_{\mathrm{nABS}}\in {T}_{\mathrm{nABS},x=1}$ given by (9), the capacity served by an SBS of MNO 1 at ${M}_{\mathrm{co}}$ RBs corresponding to the spectrum B

_{co}is then given by,

#### 4.2. System-Level Performance Analysis

**Remark**

**1.**

_{co}with SBSs of MNO 1 is given by,

_{co}with its SBSs per building in joules/bit (J/b) is given by,

_{1}and Q, the above equations imply that the system-level capacity, and hence, the spectral efficiency for an ultra-dense small indoor cell network are mainly affected by the density of small cells such that both the capacity and spectral efficiency performances could be improved with an increase in small cell density.

## 5. Modeling Energy Efficiency and the Condition for Optimality

#### 5.1. Modeling Energy Efficiency

#### 5.2. Condition for Optimality

_{min}where the normalized responses of both ${\mathsf{\sigma}}_{{}_{x=1}}^{\mathrm{EE}}\left(L\right)$ and ${\mathsf{\sigma}}_{x=1}^{\mathrm{SE}}\left(L\right)$ are obtained by scaling Equations (31) and (24) by their respective maximum values. Due to the negative exponential decay response of the energy efficiency and the linearly increased response of spectral efficiency, choosing any values of L < L

_{min}results in less data rate per Hertz and more energy required per bit transmission than that for L = L

_{min}. On the other hand, choosing any values of L > L

_{min}results in more data rate per Hertz and less energy required per bit transmission than that for L = L

_{min}.

_{min}. L

_{min}is the minimum value L at which the normalized energy efficiency and spectral efficiency curves versus L intersect one another. Choosing any value of L ≥ L

_{min}results in improving both the spectral efficiency and energy efficiency performances. The values spectral efficiency and energy efficiency corresponding to L ≥ L

_{min}define the region of optimality for both efficiencies. Hence, depending on the requirements for the spectral and energy efficiencies, an optimal value of L ≥ L

_{min}could be chosen. It is to be noted that since the energy efficiency does not vary significantly for large values of L, the point of optimality is mainly driven by the spectral efficiency requirement of an MNO.

_{min}, using Equation (37), L* can be found for the slope $\frac{d\left({\mathsf{\sigma}}_{{}_{x=1}}^{\mathrm{EE}}\left(L\right)\right)}{dL}=\Delta $ as follows.

## 6. Proposed Algorithm and Default Parameters and Assumptions

_{co}. In Table 1, the default parameters and assumptions used for the performance evaluation are given. Since we intend to find an optimal value of small cell density and the corresponding spectral and energy efficiencies by normalizing the actual values of spectral and energy efficiencies for an arbitrary density of small cells, changing the values of bandwidths of MNOs, as shown in Table 1, does not have any impact on the characteristics of any performance responses. Further, since we mainly consider homogeneous systems and focus on showing how to share the licensed spectrums, which are mainly low frequencies of below 3 GHz, of one MNO to another in a country, the performance evaluation with millimeter-wave signals for 5G, which are considered mainly for the small indoor cell coverage and backhauling purposes, is beyond the scope of this paper.

Algorithm 1 Proposed spectrum sharing technique among all MNOs of a country |

## 7. Performance Analysis and Comparison

#### 7.1. Impact of ${\lambda}_{x}$ on ${T}_{\mathrm{nABS},x}$

_{APP}allocated to in-building small cells of MNO x is a function of the average rate of arrivals of UEs to small cells of other MNOs $\mathit{X}\backslash x$, as well as the value of x

_{m}. Straightforwardly, an increase in the values of either ${\lambda}_{\mathit{X}\backslash x}$ or x

_{m}causes the number of TTIs allocated to small cells of MNO x to decrease. Since the number of serving TTIs T

_{nABS,}

_{x}decreases, the resultant capacity obtained from small cells of MNO x also decreases. From Figure 3, it is to be noted that as the collocated small cells belong to more MNOs, more CCI is generated, resulting in degrading the overall network capacity of any MNO x. Since the probability of simultaneously active small cells of all MNOs x

_{m}is low enough, a major portion of the common spectrum B

_{co}in the pool could be used by any MNO x at any time t.

#### 7.2. Impact of ${T}_{\mathrm{nABS},x}$ on Capacity, Spectral Efficiency, and Energy Efficiency

_{m}= 3, Figure 3c for minimum CCI, and Figure 3d for no CCI. According to Equation (9), these arrival rates, in turn, vary the serving period of small cells of MNO 1 ${T}_{\mathrm{nABS},1}$. Now applying Equation (9), the optimal number of TTIs per APP ${T}_{\mathrm{nABS},1}{}^{\ast}$ allocated to small cells of a building of MNO 1 for these sets of arrival rates is given by ${T}_{\mathrm{nABS},1}\in {\mathit{T}}_{\mathrm{nABS},1}=\left\{1,4,7,8\right\}$ respectively. Figure 6 shows that the achievable capacity for each CCI scenario, i.e., ${T}_{\mathrm{nABS},1}\in {\mathit{T}}_{\mathrm{nABS},1}=\left\{1,4,7,8\right\}$, for a single building L = 1.

_{co}. Hence, to avoid this mutual CCI among small cells, based on the traffic arrival rates to small cells of all MNOs, the common resource scheduler (CoRS) allocates an optimal number of TTIs ${T}_{\mathrm{nABS},x}{}^{\ast}$ to small cells of each MNO x. That’s why, the minimum number of TTIs of only 1, for the arrival rate $\left\{{\lambda}_{1},2{\lambda}_{1},2{\lambda}_{1},3{\lambda}_{1}\right\}$ as mentioned before, is allocated to small cells of MNO 1 when small cells of all MNOs (i.e., x

_{m}= 4) are active. This results in the minimum achievable capacity for small cells of MNO 1, as shown in Figure 6. Likewise, the capacity performance of small cells of MNO 1 is the maximum when no small cells of MNOs, other than MNO 1 are active (i.e., for $\left\{{\lambda}_{1},0,0,0\right\}$) such that no CCI exists and all eight TTIs per APP could be allocated to small cells of MNO 1 only. Note that all these assumed sets of values of arrival rates of UEs to small cells of each MNO are arbitrary, and considering any values other than these will not alter the innate characteristics of the performance shown in Figure 6.

**Remark**

**2.**

_{op}, in all TTIs per APP without exploring the actual capacity demand of users. This may cause underutilization of the spectrum if there is no traffic demand from one MNO at any time, whereas there exists a scarcity of spectrum from other MNOs at the same time. To allow fairness among MNOs, the maximum capacity for an APP is upper limited by twice the capacity per TTI, i.e., approximately 5.8514 Mbps, as shown in Figure 6 for x

_{m}= 4, which is one-fourth the maximum capacity that can be achieved when sharing spectrums of all four MNOs with no CCI. This problem is solved by the proposed technique by dynamically adjusting the radio resource allocation to small cells of any MNO x based on the actual traffic demand as shown in Figure 6. Further, the proposed spectrum sharing can improve the achievable capacity of small cells of an MNO x by up to the maximum x

_{m}times the capacity that can be achieved without sharing spectrum with small cells of MNO x.

#### 7.3. Spectral Efficiency and Energy Efficiency Performances

_{nABS,1}. For any T

_{nABS,1}, as the value of L increases, both spectral efficiency and energy efficiency responses improve. More specifically, the overall system-level spectral efficiency varies linearly, whereas energy efficiency varies negatively exponentially. Unlike the spectral efficiency, the improvement in the energy efficiency is noticeable when L is small and gets almost fixed when L tends to very large. Likewise, for any value of L, an increase in the number of non-ABSs (i.e., TTIs) allocated to small cells of MNO 1 improves both spectral efficiency and energy efficiency responses, i.e., increases the average data rate per Hertz and decreases the average required to transmit power per bit.

_{nABS,1}= 4 in Figure 7 are shown in Figure 8. It is found that choosing a slope on the energy efficiency curve that corresponds to the value of L = L1, which is less than the value of L = L2 = 22, results in very poor performances in both the spectral efficiency and energy efficiency. Also, choosing a slope that corresponds to the value of L = L3, which is greater than the value of L = L2, results in an improvement in both spectral efficiency and energy efficiency performances.

_{min}that gives a trade-off and ensures optimal performance in both energy and spectral efficiencies. Hence, any values of L ≥ L

_{min}is an optimal value of L = L* as given by Equation (39) corresponding to the optimal spectral efficiency and energy efficiency requirements set by the MNO 1 for T

_{nABS,1}= 4 as given by Equations (40) and (41). Note that the normalized value of the spectral efficiency and energy efficiency is 0.16 for L = L

_{min}= 22 (Figure 8). Hence, the actual values of spectral and energy efficiencies corresponding to the values of L ≥ L

_{min}are the region of optimality. Hence, from Figure 7, for the (minimum) optimal value of L = L

_{min}= 22, the corresponding optimal (minimum) values of spectral efficiency and energy efficiency are respectively 1.74 µJ/bit (i.e., 0.16 × the maximum value of energy efficiency of 10.93 µJ/bit for L = 1) and 9.3702 bps/Hz (i.e., 0.16 × the maximum value of spectral efficiency of 58.5636 bps/Hz for L = 150). Note that the improvement in energy efficiency performance gets almost steady for L > L3. Moreover, choosing a slope corresponding to L > L3 also causes to increase both the cost and the complexity of the network due to increasing the number of small cells. Hence, it is recommended to choose a slope on the energy efficiency curve that corresponds to a value of L which is not too larger than that of the (minimum) optimal value of L = L

_{min}= 22 (Figure 8).

#### 7.4. Performance Comparison with 5G Requirements

_{nABS,1}can be found as follows.

**Proof:**

_{min}required to meet the spectral efficiency and energy efficiency for 5G mobile networks for different values of T

_{nABSs,1}, including 1, 4, 7, and 8, as shown in Figure 7.

_{min}that can satisfy both ${\mathsf{\sigma}}_{5\mathrm{G}}^{\mathrm{SE}}$ and ${\mathsf{\sigma}}_{5\mathrm{G}}^{\mathrm{EE}}$ for T

_{nABSs,1}= 1, 4, 7, and 8 are given by L* = L

_{min}= 356, 94, 57, and 49, respectively. Note that with an increase in time resources allocated to small cells of MNO 1, the requirement in terms of L to satisfy spectral and energy efficiencies of 5G mobile networks decreases. Moreover, as mentioned earlier (Figure 7b), energy efficiency does not change significantly for any L > L

_{min}though spectral efficiency changes proportionately. So, the choice of L is mainly driven by the requirement for spectral efficiency of 5G mobile networks as compared to that for energy efficiency. □

## 8. Conclusions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**An abstract view of the proposed spectrum sharing technique for the maximum number of MNOs x

_{m}= 4 [12].

**Figure 3.**An illustration of the CCI strength when sharing the spectrum of one MNO (MNO 1) with that of others (MNOs 2, 3, and 4): (

**a**) maximum CCI for x

_{m}= 4, (

**b**) CCI for x

_{m}= 3, (

**c**) minimum CCI for x

_{m}= 2, and (

**d**) no CCI for x

_{m}= 1.

**Figure 5.**CCI avoidance using ABS based eICIC technique for small cells of MNO 1 in a building [12].

**Figure 6.**Capacity performance of small cells of MNO 1 with the variation of ${T}_{\mathrm{nABS},1}$ for L = 1.

**Figure 7.**System-level spectral efficiency (

**a**) and energy efficiency (

**b**) performances of MNO 1 with the density of small cells, i.e., for L ≥ 1.

**Figure 8.**Normalized spectral efficiency and energy efficiency performances of MNO 1 for L ≥ 1 and T

_{nABS,1}= 4.

Parameters and Assumptions | Value | |||
---|---|---|---|---|

E-UTRA simulation case ^{1} | 3GPP case 3 | |||

Cellular layout ^{2} and Inter-site distance (ISD) ^{1,2} | Hexagonal grid, dense urban, 3 sectors per macrocell site and 1732 m | |||

Carrier frequency ^{2,3} and transmit direction | 2 GHz and downlink | |||

Number of MNOs x_{m} | 4 | |||

Bandwidth per MNO B_{op} | 1 MHz | |||

Considered MNO for performance evaluation | MNO 1 | |||

Number of cells of MNO1 | 1 macrocell, 2 picocells, 8 SBSs per building | |||

Total base station transmit power ^{1} (dBm) of MNO 1 | 46 for microcell ^{1,4}, 37 for picocell ^{1}, 20 for SBS ^{1,3,4} | |||

Co-channel fading model ^{1} | Frequency selective Rayleigh for the macrocell and picocells, and Rician for SBSs | |||

External wall penetration loss ^{1} (L_{ow}) of a building | 20 dB | |||

Path loss for MNO 1 | Macocell BS (MBS) and a UE ^{1} | Indoor macro UE | PL (dB) = 15.3 + 37.6 log_{10}R, R is in m | |

Outdoor macro UE | PL (dB) = 15.3 + 37.6 log_{10}R + L_{ow}, R is in m | |||

Picocell BS (PBS) and a UE ^{1} | PL (dB) = 140.7 + 36.7log_{10}R, R is in km | |||

SBS and a UE ^{1,2,3} | PL (dB) = 127 + 30 log_{10} (R/1000), R is in m | |||

Lognormal shadowing standard deviation (dB) | 8 for MBS ^{2}, 10 for PBS ^{1}, and 10 for SBS ^{2,3} | |||

Antenna configuration | Single-input single-output for all base stations and UEs | |||

Antenna pattern (horizontal) | Directional (120°) for microcell ^{1}, omnidirectional for picocell ^{1} and SBS ^{1} | |||

Antenna gain plus connector loss (dBi) | 14 for MBS ^{2}, 5 for PBS ^{1}, 5 for SBS ^{1,3} | |||

UE antenna gain ^{2,3} | 0 dBi | |||

UE noise figure ^{2} and UE speed ^{1} | 9 dB, 3 km/h | |||

Total number of macro UEs | 30 | |||

Maximum number of small cell UEs served simultaneously by an SBS | 1 | |||

Picocell coverage and macro UEs offloaded to all picocells ^{1} | 40 m (radius), 2/15 | |||

3D multistory building and SBS models (regular square-grid) for MNO 1 | Number of buildings | L | ||

Number of floors per building | 2 | |||

Number of apartments per floor | 4 | |||

Number of SBSs per apartment | 1 | |||

SBS activation ratio | 100% | |||

SBS deployment ratio | 1 | |||

Total number of SBSs per building | 8 | |||

Area of an apartment | $10\times 10\hspace{0.17em}{\mathrm{m}}^{2}$ | |||

Location of an SBS in an apartment | center | |||

Scheduler and traffic model ^{2} | Proportional Fair (PF) and full buffer | |||

Type of SBSs | Closed Subscriber Group femtocell base stations | |||

Channel State Information (CSI) | Ideal | |||

TTI ^{1}, scheduler time constant (t_{c}), T_{APP} | 1 ms, 100 ms, 8 ms | |||

Maximum simulation run time | (8 × T_{APP}) ms |

**Table 2.**The minimum value of L to satisfy the spectral efficiency and energy efficiency requirements of 5G systems.

T_{nABSs,1} | L_{min} (To Meet Requirements for 5G Mobile Networks) | ||
---|---|---|---|

Spectral Efficiency (bps/Hz/cell) | $\mathbf{Energy}\mathbf{Efficiency}\left(\mathit{\mu}\mathbf{J}/\mathbf{b}\right)$ | $\mathbf{Both}\mathbf{Spectral}\mathbf{and}\mathbf{Energy}\mathbf{Efficiencies}\left({\mathit{L}}^{\ast}\right)$ | |

1 | 356 | 41 | 356 |

4 | 94 | 11 | 94 |

7 | 57 | 7 | 57 |

8 | 49 | 6 | 49 |

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## Share and Cite

**MDPI and ACS Style**

Saha, R.K.
Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks. *Energies* **2019**, *12*, 3825.
https://doi.org/10.3390/en12203825

**AMA Style**

Saha RK.
Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks. *Energies*. 2019; 12(20):3825.
https://doi.org/10.3390/en12203825

**Chicago/Turabian Style**

Saha, Rony Kumer.
2019. "Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks" *Energies* 12, no. 20: 3825.
https://doi.org/10.3390/en12203825