Introducing Redundancy in the Radio Planning of LPWA Networks for

Internet of Things

Pedro Vieira

1,2

, Andr

e Martins

1,3

and Tiago Cunha

Instituto de Telecomunicac¸

oes, Lisbon, Portugal

Instituto Superior de Engenharia de Lisboa (ISEL), Lisbon, Portugal

Celﬁnet, Consultoria em Telecomunicac¸

oes, Lisbon, Portugal

Keywords:

Internet of Things, Low-Power-Wide-Area Networks, Radio Planning, Redundancy.

Abstract:

This paper presents an enhanced methodology in order to introduce redundancy requirements in the Low-

Power Wide-Area (LPWA) networks radio planning for Internet-of-Things (IoT). The Jake’s Curves were

extended, allowing to compute new log-normal fading margins which traduce combined coverage and redun-

dancy requirements. The methodology was applied developing a LPWA SIGFOX network simulator for a

typical urban environment. The double and triple redundancy requirement produced a 10 dB increase in the

log-normal fading margin, reducing the cell range to one half, which roughly quadruples the site density. In

fact, assuring redundancy enhances the networks’s quality of experience, but strongly increases the network

investment in base-station equipment and site acquisition. This new approach allowed to compute new site

grids and to introduce the concept of assisted planning for IoT networks, where the most suitable candidates

among a site list will be automatically chosen, avoiding the inefﬁcient and ineffective trial and error method

in radio planning.

1 INTRODUCTION

Nowadays, Telecom engineers are being challenged

to plan and deploy LPWA networks that can connect

to modules requiring a single AA battery for 10 years

of life time, and which will cost less than 5 euro

each (Tom Rebbeck, Michele Mackenzie and Nuno

Afonso, 2014).

This challenge is, in fact, already in motion, since

LPWA networks are being built worldwide, boost-

ing Machine-to-Machine (M2M) connections and the

IoT.

It is believed that LPWA services can target a mar-

ket of over 3 billion M2M connections by 2023 and

generating over USD10 billion from connectivity rev-

enues alone. LPWA networks open new market op-

portunities due to:

1. Low Cost. By selling cheap terminals and an-

nual connectivity for some applications, LPWA

networks will enable connectivity to a wide range

of different services, from Smart Cities to agricul-

tural and environment driven businesses.

2. No Power Source Required. By producing a wire-

less service that can operate for years using the

same batteries, it opens the door to other markets,

like gas and water metering.

3. Strong Propagation. The strong link budget

with Maximum Allowed Path Loss (MAPL) val-

ues reaching 165 dB allows deploying less base-

stations for the same coverage area, when com-

pared with the traditional cellular solutions. Also

it allows reaching deep indoor locations, which

enables, for example, connecting meters located

in basements and sensors monitoring sewer con-

ditions.

Many of the LPWA technologies operate in

license-exempt spectrum. Whereas license-exempt

spectrum has some beneﬁts, such as rapid time to mar-

ket and no spectrum fees, clear disadvantages also ex-

ist, such as the lack of control and the likelihood of

interference.

Concerning the chosen wireless technology, the

ability to re-use the existing network infrastructure

is very attractive for the already existing operators.

Integrating LPWA services with the existing network

infrastructure will reduce roll-out time, on going sup-

port and cost.

The technologies Clean-Slate (W. Guibene and K.

Vieira, P., Martins, A. and Cunha, T.

Introducing Redundancy in the Radio Planning of LPWA Networks for Internet of Things.

DOI: 10.5220/0005957701370144

In Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016) - Volume 6: WINSYS, pages 137-144

ISBN: 978-989-758-196-0

137

E. Nolan and M. Y. Kelly, 2015) and Long Term Evo-

lution (LTE) Machine-Type-Communications (MTC)

(3GPP, 2014) or NB-IoT are being developed to be

integrated with existing 2G, 3G and LTE networks.

For these technologies, the aspects of network up-

grade are being clariﬁed; it may be as simple as a

remote software upgrade but more likely hardware

changes will be required, but with no modiﬁcations

to the needed antennas. In contrast, proprietary tech-

nologies like SIGFOX, On-Ramp and Semtech (G.

Margelis and R. Piechocki and D. Kaleshi and P.

Thomas, 2015), require a purpose-built network to de-

ploy their services. For the existing operators, this

means still using existing sites, but additional integra-

tion and infrastructure, including antenna and base-

station equipment, should be required. For a new op-

erator entering in this market, the green ﬁeld approach

is possible, although new operators should perform

agreements with the existent ones in order to perform

site-sharing and reduce costs.

Whatever the case, sites have to be chosen among

a (internal or external) site list, in order to fulﬁl cover-

age and capacity requirements. Moreover, when con-

sidering LPWA technologies, often base-station re-

dundancy must be met, i.e., coverage requirements

should be set not only for the serving base-station but

also for neighbour base-stations, which should pro-

vide redundant coverage. This assures that the LPWA

time diversity gain is achieved, hence enabling the up-

link driven performance of small terminals, simulta-

neously connecting to up to three base-stations.

Coverage requirements are usually integrated us-

ing the Jakes approach (Jakes, William C. and Cox,

Donald C., 1994). Starting from a coverage area re-

quirement which is usually high (90% to 95% is typ-

ical), a log-normal fading margin is computed, which

conditions the MAPL and the cell radius calculation,

during the initial link budget. The cell radius and site

distance is, in fact, the basis for producing a theoret-

ical site grid, where the site locations are pin-pointed

in order to commit the coverage requirements.

The aim of this paper is to present an enhanced

methodology in order to extend the Jakes curves to

scenarios where site redundancy is required, such as

the LPWA networks. This allows to compute new site

grids and to introduce the concept of assisted planning

for LPWA networks, where the most suitable candi-

dates among a site list will be automatically chosen,

avoiding the inefﬁcient and ineffective ”trial and er-

ror” method.

The paper is organized as follows. Section 2

overviews the original Jake’s curves and the cover-

age area probability determination. In the sequence,

the extended Jakes curves are developed, consider-

ing redundancy, as simulations results are presented.

Section 3 presents a case study for urban environ-

ment where the new results are applied to single, dou-

ble and triple redundancy. Section 4 overviews the

developed radio planning simulator, developed from

scratch for this project, along with the introduction

to the assisted planning concept. Finally, conclusions

are drawn in section 5.

2 DETERMINATION OF

COVERAGE AREA

PROBABILITY

This section presents the research work around ex-

tending the Jakes area coverage probability for LPWA

networks when considering site redundancy. Firstly,

past work as in (Jakes, William C. and Cox, Don-

ald C., 1994), (Rappaport, Theodore, 2001) will be

overviewed. Secondly, the authors’ additional work

will follow.

2.1 The Jakes Approach for Computing

of the Coverage Area Probability

Due to random effects of shadowing (large-scale fad-

ing) some locations within the base-station surround-

ings will presents coverage problems, i.e., will be un-

der a certain received signal threshold. It is often use-

ful to compute how the border coverage reloads to

the amount of area covered within the border. For

a circular coverage area with radius R centred on the

base-station, let there be some desired received signal

threshold γ.

In the following, the percentage of useful service

area, i.e. the percentage of area with the received sig-

nal higher or equal to γ, U (γ), is given as a known

likelihood of coverage at the cell border.

Let d represent the radial distance from the base-

station. It can be shown that if Prob(P

(r) > γ) is the

probability that the random received signal power, P

at d = r exceed the threshold γ within an incremental

area dA, then U (γ) is given by,

U (γ) =

πR

[Prob(P

(r) > γ)] dA =

πR

2π

[Prob(P

(r) > γ)] rdrdθ

(1)

where,

[Prob(P

(r) > γ)] = Q

γ −P

(r)

(2)

WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems

138

(r) is the distance dependent received signal

mean power, and Q(z) is deﬁned as,

Q(z) =

√

2π

∞



−



dx =

1 −e

√

. (3)

As in (D. C. Cox and R. R. Murray and A. W. Nor-

ris, 1984), (P. Vieira and P. Queluz and A. Rodrigues,

2007), measurements have shown that for any value

of d, the path loss, at a particular location d, PL(d), is

random and log-normally distributed around the mean

distance dependent value.

Hence,

PL (d) [dB] = PL (d

) + X

PL (d

) + 10nlog





+ X

(4)

where PL (d

) is the mean path loss at a reference dis-

tance d

, n is the path loss decay and X

is a zero-

mean Gaussian random variable with standard devi-

ation σ (in dB). Moreover, the distance dependent

received power from the base-station P

(d) depends

also on the base-station radiated power, P

(d) [dBm] = P

[dBm] −PL (d)[dB]. (5)

In order to determine the path loss as referenced

to the cell boundary (r = R), it is clear that,

PL (r) = 10nlog





+10nlog





+PL (d

) (6)

and equation (2) can be expressed as:

[Prob(P

(r) > γ)] =





γ −

−10nlog





+ 10nlog





+ PL (d

)





(7)

2.2 The Enhanced Approach

considering Base-station

Redundancy

Consider the general implementation scenario pre-

sented in Figure 1. The base stations are placed on

a regular grid, with ﬁxed inter-site distances and po-

sitioned using a non-orthogonal 60

Cartesian pair of

axis (u,v). Each base-station will consider a service

area limited by a cell radius, R.

Using this coordinate system, the distances r

from

base-station i to a generic point P can be calculated as:

(0,1)

(0,0)

(1,0 )

Figure 1: General implementation scenario.

3(u

+ uv + v

)R (8)



+ u(v −1) + (v −1)



(9)



(u −1)

+ (u −1)v + v



(10)

Starting up at equation (7) by choosing the signal

level such that P

(R) = γ, γ is given by,

γ = P

−PL(d

) −10nlog





(11)

Additionally, γ is being chosen in order to equal

the mean received signal strength at the cell border,

therefore for the Q2 (50%) percentile. It can be use-

ful to consider a quite higher cell boundary γ cover-

age probability, which can be added in the following

way. Consider LNF

marg

as the shadow fading margin

which changes the cell boundary coverage probabil-

ity to a pre-deﬁned value, typically higher than 50%.

The probability of the received signal strength being

higher than γ, where γ is the signal strength threshold

which guarantees the predeﬁned cell border probabil-

ity is, based on equation (7), and given by,

[Prob(P

(r) > γ)] = Q

10nlog





−LNF

marg

(12)

Moreover, and as stated, LPWA networks often

demand signal strength redundancy, i.e., base-station

radio planning should by performed considering that

each terminal should have a coverage level not only

Introducing Redundancy in the Radio Planning of LPWA Networks for Internet of Things

139

dependent on the serving base-station (best server),

but also from the surrounding base-stations. The re-

dundancy level, K, which is the number of base-

station that assures coverage is, in fact, an initial ra-

dio planning requirement, ranging from one to usually

three, and dependent on the radio environment and the

operator demand.

If received signal strength independence is as-

sumed between the transmitting base stations, which

is valid considering a macro cell-topology with cell

ranges of several kilometres, the signal strength cov-

erage area probability with redundancy will be given

by,

U (γ) =

πR

2π

[Prob(P

(r) > γ)]rdrdθ =

∏

i=1

10nlog





−LNF

marg

≤

(13)

With this approach, the coverage area percentage

will be integrated within the serving cell radius R, but

considering not only the server base-station but also

the neighbour base-station coverage levels.

Moreover, a redundancy gain should be added,

since the radio connection will assume multiple radio

links affected by independent shadow fading. Spe-

cially along the serving cell border, shadow fading

can signiﬁcantly change the serving cell received sig-

nal strength, which will enable the connection being

handovered to the surrounding cells, if the latter are in

better radio connections. Hence, a combination gain

must be added.

If combination by selection is set, the receiving

terminal will consider the best cell for each location

and the redundant coverage area probability is com-

puted by,

U (γ) =

∏

i=1





10nlog





−LNF

marg

+ 10nlog



min(r

)







≤

(14)

with r

and r

given by equations (8), (9) and (10),

respectively.

Figures 2, 3 and 4 present the coverage area prob-

abilities (in %) as a function of σ/n for several values

of border coverage, and for redundancy levels of one,

two and three base-stations, respectively. σ is the re-

ceived signal strength standard deviation (in dB) and

n in the propagation decay. Hence, the σ/n parameter

characterizes the radio environment.

0.5 1 1.5 2 2.5 3 3.5 4 4.5

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

σ /n [dB]

Area Coverage [%]

70% @ Cell Border

80% @ Cell Border

90% @ Cell Border

Coverage Requirement

Figure 2: Area coverage for single redundancy.

0.5 1 1.5 2 2.5 3 3.5 4 4.5

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

σ /n [dB]

Area Coverage [%]

96% @ Cell Border

97.8% @ Cell Border

99% @ Cell Border

Coverage Requirement

Figure 3: Area coverage for double redundancy.

0.5 1 1.5 2 2.5 3 3.5 4 4.5

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

σ /n [dB]

Area Coverage [%]

99% @ Cell Border

98.6% @ Cell Border

98% @ Cell Border

Covergae Requirement

Figure 4: Area coverage for triple redundancy.

The curves were produced based on simulations

and are the key for the LPWA network initial plan-

ning, considering redundancy. Firstly, the coverage

area and redundancy level requirements are set, along

with the radio environment characterization, building

a triplet. Using the simulations, the triplet allows to

index the border coverage level, which is used to com-

pute the log-normal fading margin that reﬂects the ini-

WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems

140

tial requirements.

As an example, let us map the triplet in the previ-

ous ﬁgures, but considering the case study values pre-

sented in the next section 3 . A black ”dot” represents

90% coverage area (y-axis), in a typical urban envi-

ronment with n = 3 and σ = 8 dB (σ/n, x-axis). Con-

sidering a redundancy increase from single to triple,

the border coverage probability for the focused en-

vironment, strongly increases, as expected. The as-

sociated log-normal fading margin, computed from a

log-normal distribution for the considered probability

also steps up, affecting the link budget and initial di-

mensioning.

Moreover, the log-normal fading margin in intro-

duced in the LPWA link budget. Along with the log-

normal fading margins, and for the considered tech-

nology, parameters like transmit power, receiver sen-

sitivity, antenna gain, cable and combiner losses, re-

ceiver Low Noise Ampliﬁer (LNA) gain, and pene-

tration losses are used to calculate the MAPL. Then,

and using a large-scale propagation model, typically

tuned for the used frequency band, the cell range and

site distance is calculated, allowing to reach a theoret-

ical site location grid that mirrors the LPWA techno-

logical speciﬁcs, along with coverage and redundancy

requirements.

For a chosen environment, a higher coverage area

requirement produces a higher border coverage prob-

ability, increasing the log-normal fading margin and

reducing the cell range. The redundancy level re-

quirement has a strong impact in the log-normal fad-

ing margin, which rapidly grows, and originating a

reduced cell radius. If strong redundancy is an issue,

the site density will increase, along with the needed

investment concerning number of base-stations. This

will be properly quantiﬁed under a case-study in the

next section.

3 A CASE STUDY FOR URBAN

ENVIRONMENT USING

SIGFOX

The methodology was applied to a LPWA network in

a typical urban environment with n = 3 and σ = 8

dB. It is assumed that terminals are placed inside the

buildings, hence, a 15 dB penetration loss is con-

sidered. A large-scale propagation model was used

for the path loss calculations, after being tuned with

RF measurements. The coverage area requirement is

90%, and 3 scenarios are set, using single, double and

triple redundancy, respectively (K = 1,2,3).

The chosen LPWA technology was SIGFOX,

which was picked as an example. It should be high-

lighted that the presented methodology is transversal

to all point-to-area wireless technologies, which de-

pend on a coverage area requirement.

SIGFOX is an LPWA operated telecommunica-

tion technology, dedicated to the IoT, currently de-

ployed in Western Europe, San Francisco, and with

ongoing tests in South America and Asia. It oper-

ates on sub-GHz frequencies, on ISM bands : 868

MHz in Europe/ETSI and 902MHz in the USA/FCC.

SIGFOX uses an Ultra-Narrow Band (UNB) modu-

lation, With a 162 dB budget link SIGFOX enabling

long range communications. In uplink, the 14 dBm

terminals transmit to the closest base-stations, which

decode the signals and forward them to the network

back-end.

Table 1 presents the log-normal fading margin

(LNF

marg

), cell range (R), inter-site distance (ISD)

and site density (SD) for single, double and triple re-

dundancy.

Table 1: Simulation results.

K LNF

marg

R ISD SD

[dB] [km] [km] [site/km

]

1 5.91 2.17 3.75 8.18 ×10

−2

2 16.19 1.12 1.94 30.68 ×10

−2

3 17.47 1.03 1.78 36.28 ×10

−2

For the typical urban environment, the double and

triple redundancy requirement produces a huge 10

dB increase in the log-normal fading margin, reduc-

ing the cell range to one half, which roughly quadru-

ples the site density. In fact, assuring redundancy en-

hances the network quality, but also strongly increases

the network investment in base-station equipment and

site acquisition.

4 THE IoT RADIO PLANNING

SIMULATOR

In order to apply the developed methodology and to

considerer not only the link budget approach but also

to produce spatial simulations, a coverage prediction

simulator was developed for IoT network planning.

Section 4.1 will overview the simulator’s architecture

and section 4.2 will introduce the concept of assisted

planning.

Introducing Redundancy in the Radio Planning of LPWA Networks for Internet of Things

141

4.1 Planning Tool Architecture

The simulator was developed in C#, and its architec-

ture is presented in Figure 5.

VISFOX PLANNING TOOL

› PRODUCT DESCRIPTION

NEW SOLUTIONS

SITE LOCATION & ANTENNA HEIGHT:

SITE SHARING (MOBILE NETWORKS)

 GREENFIELD PLANNING

LOS EVALUATION

 TERRAIN DIFFRACTION

 CLUTTER ANALYSIS

 RF PROPAGATION MODELS

 CELLS FOOTPRINT

POPULATION

DENSITY DATASET

ADMINISTRATIVE

BORDERS

ANTENNA

PATTERNS

TERRAIN

DATASET

FIELD

MEASUREMENTS

Figure 5: IoT network planning simulator’s architecture.

The developed IoT simulator uses a large-scale

empirical propagation model in order to predict the

path loss and received signal strength for uplink and

downlink. As inputs, it relies on Geographic Infor-

mation System (GIS) datasets such as Terrain Digital

Surface and Elevation Models, as well as the Clut-

ter (Radio Environment) characterization. In addi-

tion, to accurately represent the antenna gain in each

azimuth/elevation, it also uses the antenna radiation

pattern model which is supplied by the vendor. Since

the considered use case may be to offer distinct cover-

age and redundancy requirements for each geograph-

ical region and population density, it requires the ge-

ographical population distribution, as well as its ad-

ministrative borders.

The median path loss is calculated depending

on the distance, frequency, antenna effective height,

Line-of-Sight (LoS) existence, diffraction losses and

clutter type. To get the best prediction results, it is still

necessary to adapt the model’s parameters to local

conditions and to the technology peculiarities of the

IoT radio planning. In this context, Lisbon ﬁeld mea-

surements (using SIGFOX network) were also used,

to the tune the model.

As outputs, it produces several thematic maps of

individual base-station foot-print, global coverage, re-

dundancy level, along with planning reports where the

considered regions compliance to the pre-set coverage

requirements are presented. As an example, Figure

6 represents the coverage footprint for a certain cell

whilst Figure 7 presents the redundancy level for one

of the Lisbon’s simulations. Here, the red stands for

the triple redundancy, the green for double, and ﬁnally

the blue shaded area for the single redundancy (K).

Figure 6: Coverage Footprint for a given cell.

Figure 7: Base-station redundancy level.

4.2 Assisted Planning

Using the presented methodology, it is possible to in-

troduce a new concept dependent on the redundancy

level, which will be named Assisted Planning. The

assisted planning feature provides a way of picking

the most suitable locations to plan the sites, in order

to achieve the coverage and redundancy requirements

in a speciﬁc region.

In fact, and as mentioned, new and already ex-

istent IoT operators should follow a site-sharing ap-

proach to reduce costs. Hence, they will have to pick

the best sites from an existing candidate list, in order

to build up the new network. The assisted planning

helps the radio planner in this task, since supplies a

cell grid already moulded to the speciﬁc radio envi-

ronment and also coverage and redundancy require-

ments. Hence, using assisted planning, it is possible

to automatically determine the best location to install

the base-station, simply pin-pointing the location or

by selecting it from a set of candidate locations.

WINSYS 2016 - International Conference on Wireless Networks and Mobile Systems

142

Figures 8 and 9 present the assisted planning grid

for the single and triple redundancy scenarios, respec-

tively. For triple redundancy, the ISD strongly re-

duces, as already mentioned.

ASSISTED PLANNING & NOMINAL PLANNING

› THE JAKE’S APPROACH FOR A REDUNDANT IOT BASE STATION SYSTEM (CONT.)

EW SOLUTIONS

• CHANGING THE INTER SITE DISTANCE FROM 3 KM TO 1.5 KM…

Figure 8: Assisted planning for single redundancy scenario.

ASSISTED PLANNING & NOMINAL PLANNING

› THE JAKE’S APPROACH FOR A REDUNDANT IOT BASE STATION SYSTEM (CONT.)

EW SOLUTIONS

• CHANGING THE INTER SITE DISTANCE FROM 3 KM TO 1.5 KM…

Figure 9: Assisted planning for triple redundancy scenario.

The yellow planning grid, which is set for each

region according to its terrain and clutter character-

istics, reveals an average site separation, dependent

on the redundancy level which the network is trying

to achieve.Both ﬁgures consider a 90% coverage area

target, but for different redundancy levels which im-

pose different site distance separations, as mentioned.

In this case, a set of candidate locations for site-

sharing is available (represented by red markers),

along with the yellow markers that represent the best

theoretical site locations. The assisted planning fea-

ture picks the best sites, after a ranking procedure,

where not only distance to theoretical markers is con-

sidered, but also the location height, type of radio en-

vironment and construction limitations.

5 CONCLUSIONS

This paper presented an enhanced methodology in

order to introduce redundancy requirements in the

LPWA networks radio planning for IoT. The Jake’s

Curves were extended, allowing to compute new

log-normal fading margins which traduce coverage

and redundancy requirements.The presented method-

ology is transversal to all point-to-area wireless tech-

nologies, which depend on a coverage area require-

ment, from emerging LPWA technologies (e.g SIG-

FOX, On-Ramp, Semtech) to existing cellular tech-

nologies which are focusing on IoT integration (NB-

IoT).

The methodology was applied to a LPWA SIG-

FOX network in a typical urban environment. The

double and triple redundancy requirement produced

a huge 10 dB increase in the log-normal fading mar-

gin, reducing the cell range to one half, which roughly

quadruples the site density. In fact, assuring redun-

dancy strongly increases the network investment in

base-station equipment and site acquisition.

This new approach allowed to compute new site

grids and to introduce the concept of assisted plan-

ning for IoT networks, where the most suitable candi-

dates among a site list will be automatically chosen,

avoiding the inefﬁcient and ineffective ”trial and er-

ror” method in radio planning.

ACKNOWLEDGEMENTS

This work was supported by the Instituto de

Telecomunicac¸

oes (IT) and the Portuguese Founda-

tion for Science and Technology (FCT) under project

PEst-OE/EEI/LA0008/2013.

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