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Tegawendé F. Bissyandé
Gertjan van Stam (Eds.)

135

e-Infrastructure
and e-Services
for Developing Countries
5th International Conference, AFRICOMM 2013
Blantyre, Malawi, November 25–27, 2013
Revised Selected Papers

123
Lecture Notes of the Institute
for Computer Sciences, Social Informatics
and Telecommunications Engineering 135

Editorial Board
Ozgur Akan
Middle East Technical University, Ankara, Turkey
Paolo Bellavista
University of Bologna, Bologna, Italy
Jiannong Cao
Hong Kong Polytechnic University, Hong Kong, Hong Kong
Falko Dressler
University of Erlangen, Erlangen, Germany
Domenico Ferrari
Università Cattolica Piacenza, Piacenza, Italy
Mario Gerla
UCLA, Los Angels, USA
Hisashi Kobayashi
Princeton University, Princeton, USA
Sergio Palazzo
University of Catania, Catania, Italy
Sartaj Sahni
University of Florida, Florida, USA
Xuemin (Sherman) Shen
University of Waterloo, Waterloo, Canada
Mircea Stan
University of Virginia, Charlottesville, USA
Jia Xiaohua
City University of Hong Kong, Kowloon, Hong Kong
Albert Zomaya
University of Sydney, Sydney, Australia
Geoffrey Coulson
Lancaster University, Lancaster, UK
More information about this series at http://www.springer.com/series/8197
Tegawendé F. Bissyandé Gertjan van Stam (Eds.)
•

e-Infrastructure
and e-Services
for Developing Countries
5th International Conference, AFRICOMM 2013
Blantyre, Malawi, November 25–27, 2013
Revised Selected Papers

123
Editors
Tegawendé F. Bissyandé Gertjan van Stam
University of Luxembourg Scientific and Industrial Research
Luxembourg and Development Centre
Luxembourg Harare
Zimbabwe

ISSN 1867-8211 ISSN 1867-822X (electronic)

ISBN 978-3-319-08367-4 ISBN 978-3-319-08368-1 (eBook)
DOI 10.1007/978-3-319-08368-1

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 Preface

Africomm 2013, the 5th version of the EAI Conference on e-Infrastructure and e-
Services for Developing Countries, held during November 25–28 in Blantyre, Malawi,
proved to be a unique and truly African meeting place. Scientists, practitioners, stu-
dents, and professionals met to discuss research and development of efficient and
effective infrastructures and solutions in situations of limited resources. This work is
of utmost importance as it constitutes a key-enabler for the diffusion of ICT in
developing countries.
In a concerted effort, participants interacted to discuss issues and trends, recent
research, innovation advances and in-the-field experiences related to e-Governance, e-
Infrastructure, and e-Business with a focus on developing countries.
This volume of papers testifies of the exemplary efforts and sacrifices made by
participants and the Organizing Committee. The excellent work supported an exciting
program, and provides a unique insight in appropriate technology and practice. We
thank the peer-reviewers and all involved for a job well done. Thanks to our Malawian
colleagues for excellent arrangements.
The papers, the commitment to participate, the sending communities, and the
representation of research, practise, and interaction, made Africomm 2013 a milestone
event.
Special thanks to the program chair, Tegawendé Bissyandé, for the excellent
preservation of academic ethics and value.
Let these conference proceedings of Africomm 2013 be a milestone of agency and
empowerment for cultural aligned practices in e-Infrastructure and e-Services in
developing countries.

May 2014 Gertjan van Stam

 Organization

Steering Committee
Imrich Chlamtac (Chair) CREATE-NET, Italy
Salomao Julio Manhica UTICT, Mozambique
Fausto Giunchiglia University of Trento, Italy
Paolo Traverso FBK, Italy

Conference Organization Committee

General Chair

Gertjan van Stam SIRDC, Zimbabwe

PC Chair

Tegawendé F. Bissyandé University of Luxembourg, Luxembourg

Local Chair

Chomora Mikeka University of Malawi, Malawi

Publicity Chair

Philip Lutaaya MTAC-MTTI, Uganda

Student Affairs

Isaac Munyaradzi GISP, Zimbabwe

Technical Program Committee

Bissyande F. Tegawendé University of Luxembourg, Luxembourg

Chedom Fotso Donatien University of Pau, France
Chikumba Patrick Albert University of Malawi, Malawi
VIII Organization

Dearden M. Andrew Sheffield Hallam University, UK

Dieng Youssou University of Ziguinchor, Senegal
Djotio Ndie Thomas University of Yaoundé I, Cameroon
Fomena Romeo Tatsambon University of Alberta, Canada
Ghinea George Brunel University, UK
Glitho Roch Concordia University, Canada
Gourhant Yvon Orange Labs, France
Gueye Bamba University Cheick Anta Diop, Senegal
Lutaaya Philip MTAC—MTTIC, Uganda
Makpangou Mesaac Inria, France
Mikeka Chomora University of Malawi, Malawi
Mitton Nathalie Inria, France
Mutale Joseph University of Manchester, UK
Nguena-Timo Omer University of Bordeaux, France
Ouoba Jonathan University of Bordeaux, France
Poda Pasteur University of Bobo Dioulasso, Burkina Faso
Rai A. Idris State University of Zanzibar, Tanzania
Some Borli Michel Jonas University of Bobo Dioulasso, Burkina Faso
Tchana Alain University of Grenoble, France
Unwin Tim Commonwealth Telecommunications Union, UK
van Niekerk Johan Nelson Mandela Metropolitan University,
South Africa
van Stam Gertjan SIRDC, Zimbabwe
 Contents

Exploiting IEEE802.11n MIMO Technology for Cost-Effective

Broadband Back-Hauling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Michael Rademacher, Mathias Kretschmer, and Karl Jonas

Review of Power Line Communications Standards in Africa . . . . . . . . . . . . 12

A.R. Ndjiongue, A.J. Snyders, Hendrick C. Ferreira, and Suvendi Rimer

Simulation of Wireless Sensor Node Transmission Over a Multiple

Access Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Katleho Kanyane, Babu Sean Paul, Suvendi Rimer, and Khmaies Ouahada

Communication Costs and Trade in Sub Saharan Africa: A Gravity Approach . . . 27

Evans Mupela and Adam Szirmai

On Software-Defined Networking for Rural Areas: Controlling Wireless

Networks with OpenFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Sami Ruponen

Thought Leadership in e-Governance, e-Infrastructure, and e-Business

in Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Gertjan van Stam

Towards Securing Communications in Infrastructure-Poor Areas . . . . . . . . . 59

Daouda Ahmat, Tegawendé F. Bissyandé, and Damien Magoni

A Pairing-Free Public Key Encryption with Keyword Searching for Cloud

Storage Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Thokozani Felix Vallent and Hyunsung Kim

A Pairing Based Authentication and Key Establishment Scheme

for Remote Patient Monitoring Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Kambombo Mtonga, Eun-Jun Yoon, and Hyunsung Kim

Security Analysis of Some Output of RC4 Cryptosystem. . . . . . . . . . . . . . . 90

Kondwani Thangalimodzi, Gift Khangamwa, and Patrick Albert Chikumba

Sustainable ICT4D in Africa: Where Do We Go from Here? . . . . . . . . . . . . 95

Tegawendé F. Bissyandé, Daouda Ahmat, Jonathan Ouoba,
Gertjan van Stam, Jacques Klein, and Yves Le Traon
X Contents

Improving Rural Emergency Services with Cognitive Radio Networks

in Sub-Saharan Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Dramane Ouattara, Francine Krief, Mohamed Aymen Chalouf,
and Tegawendé F. Bissyandé

Unified Solution Towards Deployment of TV White Space in Africa . . . . . . 115

Philipp Batroff, Osianoh G. Aliu, Eric Schütz, Mathias Kretschmer,
Christian Niephaus, and Karl Jonas

Medicines Identification for African Illiterate Patients Using

Near Field Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Mahlaku Mareli, Suvendi Rimer, Babu Sean Paul, and Khmaies Ouahada

Spatial Analysis of Location of Mother’s Choice for Delivery:

A Case of Blantyre and Mwanza Districts in Malawi . . . . . . . . . . . . . . . . . 130
Priscilla Maliwichi and Patrick Albert Chikumba

Diabetes Advisor – A Medical Expert System for Diabetes Management . . . 140

Audrey Mbogho, Joel Dave, and Kulani Makhubele

Assessing Spatial Distribution of Maternal Health Related Resources in Health

Facilities in Malawi: Case of Skilled Birth Attendants in Zomba District . . . . . 145
Chipiliro Awali and Patrick Albert Chikumba

Reporting in the Health Systems: Case Study of Ghana. . . . . . . . . . . . . . . . 153

Ivy de-Souza

Real-Time Communication over Wireless Sensor Network – A Prototype

for Disaster Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Sarantorn Bisalbutra and Elisa Jimeno

Wireless Sensor Networks and Advanced Metering Infrastructure

Deployment in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Omowunmi M. Longe, Khmaies Ouahada, Hendrick C. Ferreira,
and Suvendi Rimer

Mobile Sink Wireless Underground Sensor Communication Monitor . . . . . . 172

Cedrick S. Nomatungulula, K.G. Ngandu, Suvendi Rimer,
Babu Sean Paul, Omowunmi M. Longe, and Khmaies Ouahada

Consumer Adoption of Mobile Payment Systems in Malawi:

Case of Airtel Malawi ZAP in Blantyre City . . . . . . . . . . . . . . . . . . . . . . . 178
Menard Nyirenda and Patrick Albert Chikumba

Stakeholder Analysis and Sustainability of Telecenter Projects

in Rural Malawi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
Christopher Banda and Patrick Albert Chikumba
 Contents XI

The NOAH Project: Giving a Chance to Threatened Species in Africa

with UAVs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Miguel A. Olivares-Mendez, Tegawendé F. Bissyandé, Kannan Somasundar,
Jacques Klein, Holger Voos, and Yves Le Traon

Geographic Information System as a Tool for Enriching Drug Logistics

Information at District Level in Malawi: Challenges on Data Quality . . . . . . 209
Patrick Albert Chikumba

GrAPP&S, a Distributed Framework for E-learning Resources Sharing . . . . . 219

Thierno Ahmadou Diallo, Olivier Flauzac, Luiz Angelo Steffenel,
Samba N’Diaye, and Youssou Dieng

Y Nut, a Phonetic-Based Learning System for Spoken Languages . . . . . . . . 229

Omer L. Nguena Timo and Tegawendé F. Bissyandé

Modeling Diffusion of Blended Labs for Science Experiments Among

Undergraduate Engineering Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Raghu Raman, Krishnashree Achuthan, Prema Nedungadi,
and Maneesha Ramesh

The Impact of Internet Banking on Service Quality Provided

by Commercial Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
Angeline Liza Ndachiphata Chima and Vanwyk Khobidi Chikasanda

Power Instability in Rural Zambia, Case Macha . . . . . . . . . . . . . . . . . . . . . 260

Consider Mudenda, David Johnson, Lisa Parks, and Gertjan van Stam

Considering Misconceptions in Automatic Essay Scoring

with A-TEST - Amrita Test Evaluation and Scoring Tool . . . . . . . . . . . . . . 271
Prema Nedungadi, Jyothi L, and Raghu Raman

E-education – Using the Simplest Technologies to Empower

Local Communities for Development in Malawi. . . . . . . . . . . . . . . . . . . . . 282
Alexander Mtembenuzeni and Chomora Mikeka

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

 Exploiting IEEE802.11n MIMO Technology
for Cost-Effective Broadband Back-Hauling

Michael Rademacher(B) , Mathias Kretschmer, and Karl Jonas

Fraunhofer FOKUS, Sankt Augustin, Germany

{michael.rademacher,mathias.kretschmer,karl.jonas}@fokus.fraunhofer.de

Abstract. The lack of affordable broadband Internet connectivity in

rural areas, especially in emerging regions, is seen as a major barrier
for access to knowledge, education or government services. In order to
reduce the costs of back-hauling in rural regions, often without access to
a stable power grid, alternative solutions are required to provide high-
bandwidth back-hauling at minimal power consumption to allow solar-
powered operation. In this paper, we show that cost-effective low-power
IEEE802.11n (MIMO) hardware together with a single cross-polarized
antenna can be a viable solution to the problem. Our study shows that
up to 200 Mbps of actual throughput can be achieved over distances
larger than 10 km while the power consumption of a typical forward-
ing node is well below 10 Watts (http://wiback.org/repeater) - suitable
for a cost-effective solar-powered operation. Through theoretical analysis
and extensive measurements we show that such a low-cost setup can be
used to establish reliable long-distance links providing high-bandwidth
connectivity at low latencies and consequently providing the capacity
demanded by today’s services - everywhere. Exploiting these findings
we are in the process of extending existing fiber-based infrastructures in
rural Africa with our Wireless Back-Haul (WiBACK) architecture.

Keywords: IEEE 802.11n · MIMO · Long-distance · Wireless back-haul

1 Introduction
Operators in rural areas often face the challenge to support the bandwidth and
QoS demands of today’s on-line service offerings. While in urban areas back-
hauling capacity can be increased at relatively low costs via a wire-line infrastruc-
ture, in rural areas wireless technologies are often the only affordable means to
establish back-hauling connectivity. Especially in emerging regions, the poten-
tial lack of access to a stable power grid is another crucial factor regarding the
CAPEX and OPEX considerations of candidate technologies.
In such scenarios, back-hauling networks are often built based on commercial-
of-the-shelf IEEE802.11 WiFi technology, to due its relatively high capacity and
a low energy footprint. Managed by Wireless Mesh Network (WMN)-style pro-
tocols such architectures, for example our carrier-grade WiBACK1 architecture,
1
http://www.wiback.org

c Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014
T.F. Bissyandé and G. van Stam (Eds.): AFRICOMM 2013, LNICST 135, pp. 1–11, 2014.
DOI: 10.1007/978-3-319-08368-1 1
2 M. Rademacher et al.

offer the potential to reduce CAPEX and OPEX tremendously due to their
self-configuration and self-management features, thus providing a resilient and
fault-tolerant network [1–3].
Legacy IEEE802.11a technology typically supports a maximum effective data
rate of approximately 30 Mbps [4], which can easily become a bottle neck in the
network, especially if triple-play services are to be supported. The more recent
IEEE802.11n standard [5] promises a tremendous increase of the actual through-
put by introducing more efficient Modulation and Coding Schemes (MCSs),
frame aggregation and Multiple Input Multiple Output (MIMO) support. The
main focus of the IEEE 802.11n standard is rather short distance communication
and many devices with the ability of using those advanced features can already
be found in consumer electronic devices.
The topology of a typical WiBACK scenario, however, is based on point-
to-point links with distances reaching from a few hundred meters up to several
kilometers defining a completely different scenario as intended by the IEEE
standard. In this paper we explore the applicability of IEEE 802.11n for long-
distance WiFi links and therefore as an option to increase the overall capacity
inside a WiBACK network. Our major focus is on exploiting the capacity gains
introduced by the MIMO capabilities using a single cross-polarized antenna,
which would allow to a very cost-effective design of multi-radio forwarding nodes.
The remainder of the paper is structured as follows. In Sect. 2 we intro-
duce related work and briefly summarize the main concepts of IEEE802.11n
and MIMO including upcoming challenges with long distance links. Section 3
describes our experiments with long distance 802.11n MIMO links and their
results which we conclude in Sect. 4.

2 Related Work and Background

In [6] experiments with long distance MIMO links focusing on polarized anten-
nas2 are presented. Using .11n draft 2.0 and a maximum link distance of 700
m, they show that polarized antennas improve MIMO for long distance and a
maximum throughput of 60 Mbps was reached. In [7] the authors show that even
for long distance MIMO links high ranked channel matrices are possible. The
focus in [8] is the definition of a model describing the coverage and capacity of a
.11n cell based approach. In [9] similar considerations about 802.11n links were
done however, their results differ from ours. The maximum throughput reached
was 40 Mbps exploiting all 802.11n features over 1.8 km and they measured a
significant gradual decrease over the link distance. This throughput decrease
seems to be related to a low SNR rather than to the applicability of 802.11n
features on long distance links. To the best of our knowledge, no prior research
has investigated the maximum possible throughput of IEEE802.11n MIMO long-
distance links (>10 km) using a single cross-polarized antenna while taking QoS
considerations into account.

2
And the influence of the “Keyhole Effect”.
 IEEE 802.11n MIMO Links for Broadband Back-Hauling 3

2.1 Technology Enhancements of IEEE802.11n

Rather than summarizing the standard in general the purpose of the following
section is to theoretically identify the main difference between .11a and .11n
so previous knowledge about the concepts of WiFi is desirable. All following
information are taken from the current standard itself [5] as well as [10].

Physical Layer. Regarding the main concepts of the PHY layer .11a and .11n
use the same principles to ensure interoperability. However, 802.11n extends the
concepts in every parameterizable value aiming at a throughput increase of the
current maximum 54 Mbps as described in the following.
Although IEEE802.11a allocated a channel width of 20 MHz only 16.56 MHz
are used divided into 53 subcarriers (0.3125 MHz each) with 48 of them con-
taining data bits. IEEE802.11n exploits the 20 MHz more efficiently by adding
two additional data sub-carriers on each side increasing the maximum physical
throughput to 54 Mbps ∗ 5248 = 58.5 Mbps.
To detect a limited number of errors after the transmission .11a use convolu-
tional codes with a maximum coding rate of 3/4. With less redundancy, 802.11n
introduces an additional coding rate of 5/6 increasing the maximum physical
data rate to 58.5 Mbps ∗ 43 ∗ 56 = 65 Mbps.
Intersymbol interference is an unwanted phenomena in telecommunications
where one symbol interferes with subsequent ones. For IEEE802.11a one OFDM
symbol last 4 µs consisting of 3.2 µs data and a guard period of 0.8 µs, .11n
introduced the optional feature of a shortened guard-interval lasting 0.4 µs and
decreasing the overall symbol duration to 3.6 µs and therefore increase the max-
imum throughput to 65 Mbps ∗ 3.6 4
= 72.2 Mbps.
To overcome the limits proposed by the Shannon-Hartley theorem, the most
obvious move to increase the throughput is to use a wider communication chan-
nel, while .11a defines a maximum channel width of 20 MHz3 .11n allows to
double this capacity to 40 MHz. Two direct 20 MHz neighbor channels can be
bundled to overall 116 OFDM sub-carriers (108 containing data) increasing the
maximum physical data rate to 72.2 Mbps ∗ 108 52 = 150 Mbps.
All introduced enhancements in this sections apply equally to all lower
802.11n modulations as well and are, for the 802.11n case, called Modulation
and Coding Scheme (MCS) labeled from zero to seven.

Medium Access Control Layer. After applying the high throughput enhance-
ments to the physical layer, changes on the MAC layer were mandatory due to the
poor scaling of throughput at the MAC layer, especially when using high physi-
cal data rates [10]. Some of the following MAC enhancement were already intro-
duced by the IEEE802.11e standard nevertheless they are consistently extended
for .11n.
Between every transmission a small period of time (SIFS - 16 µs) is added to
ensure the receiver has the chance to sent an acknowledgement or other stations
3
With the exception of the Atheros proprietary “Super-G” mode.
4 M. Rademacher et al.

A-MSDU A-MPDU
PSDU PSDU
MSDU- MSDU- MSDU- MPDU- MPDU- MPDU-
PHY-HDR MAC-HDR FCS PHY-HDR
sub 1 sub ... sub N sub 1 sub ... sub N

MPDU
DA SA Length MSDU PAD MAC-HDR MSDU FCS PAD
Delimiter

Fig. 1. Frame aggregation: A-MSDU and A-MPDU [11]

can apply for a transmission (back-off). Arbitration interframe spaces (AIFS)

lead to the possibility of prioritizing traffic from different classes (i.e. voice over
best effort) and Reduced Interframe spaces (RIFS - 2 µs) provide the possibility
of a so called burst transmission (Fig. 1).
IEEE802.11n further increases the efficiency by surrendering inter-frame
spaces between data frames leading to the main .11n MAC layer technique -
frame aggregation. The standard distinguish between two different types of aggre-
gation: the aggregate MAC protocol service unit (A-MSDU) and the aggregate
MAC protocol data unit (A-MPDU) logically residing at the top (A-MSDU) or
the bottom (A-MPDU) of the MAC layer [11]. The A-MPDU method aggregates
completely formatted MAC frames including a MAC header for every sub-frame
which consequently make the A-MSDU method more efficient. Both mechanisms
share the same restriction that each sub-frame in one block has to share the
same addresses and traffic class. For the A-MPDU case the Block ACK protocol
efficiently confirms sub-frames through a bitmap to acknowledge or demand a
retransmission.

Multiple Input Multiple Output. MIMO describes a system using a trans-

mitter and receiver with multiple antennas communicating through a propaga-
tion environment [10]. Theoretically, MIMO promises an extraordinary increase
in the capacity of wireless networks and has therefore drawn considerable atten-
tion in the last decade [7]. Although MIMO can be considered as a physical layer
enhancements it is not limited to WiFi and a central issue in this report which
justifies the approach in this separate section.
To understand the potential as well as the challenges of the MIMO signal
transmission technique a further simplified communication channel model taken
from [10] is given for a Single Input Single Output in Eq. 1 and for a 2 × 2 MIMO4
system in Eq. 2.
y =h∗x+z (1)
       
y1 h11 h12 x1 z
= ∗ + 1 (2)
y2 h21 h22 x2 z2
These equations may be used to describe each OFDM sub-carrier in the fre-
quency domain. Different approaches can be found in [7,10,12]. In this model x
describes the transmitted, y the received signal and z is Additive White Gaussian
4
Two transmitting and receiving antennas.
 IEEE 802.11n MIMO Links for Broadband Back-Hauling 5

Noise (AWGN). Variable h is defined as the channel fading coefficient, a complex

scalar element representing the gain and phase of the channel usually modeled as
Rayleigh fading. A simplified but here still sufficient approach is to define fading
as the attenuation deviation affecting the signal. Deviations occurs for example
by geographical conditions, the used radio frequency or multi-path propagation.
For the MIMO case (2) hij describes the fading coefficient occurring for the
transmission from antenna i to j. To extract the wanted information x from the
received signal y the receiver needs to challenge two main tasks. First the coef-
ficients hij needs to be specified which is for .11n realized by the so called pilot
based channel estimation where predefined symbols are attached in the beginning
of every OFDM frame. The second task is rather mathematical but explains the
main challenge for MIMO well. To extract the information, the matrix H needs
to be inverted [10] which is only possible if the matrix is non-singular (hii = hij ).

 − z ∗ h−1
x=x (3)
  
   −1
x1 x1 h11 h12
= −z∗ (4)
x2 x2 h21 h22

This process is mathematical described through Eqs. 3 and 4 where X  = Y ∗H −1

5
describes the noisy estimate of the received signal . From a practical point of view
this means that the transmitted signal (x1 ) needs to be received in a different
way on every receiving antenna (y1,2 ) to creat a non-singular channel matrix.
This so called well conditioned matrix provides the possibility to distinguish
between different streams from the MIMO transmission6 . In an typical indoor
environment this de-correlation of the signals mainly arise from rays bouncing
from walls and obstacles as well as short distance between the antennas leading
to randomly distributed coefficients [10].
To exploit the possibility of MIMO, different signal processing techniques
are included in the latest WiFi standard [5] namely Maximal-Ratio Combining
(MRC), Space-Time Bloc Coding (STBC) and Spatial Multiplexing where MRC
and STBC offer a gain in diversity and only spatial multiplexing has the ability of
increasing the maximum capacity of the channel, in theory, linear by the number
of antennas located at transmitter and receiver. Spatial Multiplexing with two
antennas leads to additional MCS numbered from 8 to 15 with modulations and
codings accordingly to the SISO equivalent 0–7.

Long Distances. Following the theoretical considerations regarding MIMO,

different options to exploit this technique for long distance WiFi links arise where
the main challenge is the de-correlation of the streams to reach a throughput
increase by spatial multiplexing. The first option is spatial antenna diversity in
combination with high gain directional antennas. The disadvantage of this option
is the large spacing needed between the two antennas which is relative to the
5
We use capital letters to describe the SISO and MIMO case.
6
We can describe Eqs. 2 and 4 as the solution to a system of linear equations.
6 M. Rademacher et al.

distance between the receiver and the transmitter. In [7] a model predicting
the spacing needed and in [6] practical examples are given but both conclude,
that such a deployment is not practical also because of the need for long coaxial
cable. The second option is the usage of two antennas where one points to a
large obstacle to force a multi path propagation due to reflexions. The typical
WiBACK use-case bar this option on the one hand due to the rural environ-
ment and on the other hand because of complex process of antenna pointing not
suitable for untrained persons. The third option is the usage of a single antenna
with the ability of sending two streams with different polarizations called cross-
polarized antennas. Depending on the quality and kind of antenna, there is an
attenuation between the two signals of approximately 20–30 dB. This attenu-
ation should lead to sufficient de-correlation of the signals in a long-distance
environment to enable MIMO operation to increase the maximum throughput.
This option has no known practical disadvantage and only a minor affect on the
costs of our network equipment (CAPEX)7 .

3 Measurements
To evaluate and compare the behavior of .11n techniques on long distances dif-
ferent test links have been set up and utilized. A short link in a laboratory envi-
ronment using stubby antennas serves as reference to evaluate the long distance
influence. Two different long distance links have been installed, both originating
at the Fraunhofer Campus in Birlinghoven, Germany. The first link terminates
with non-perfect8 conditions and a distance of 5 km at tree nursery while the
second ends with perfect propagation conditions 10.3 km away on a radio tower.
All three links with exception of the radio tower9 use the same hardware, a
tailor-made embedded computer equipped with dual Intel Atom N2800 CPUs
and three Ubiquity SR71 wireless cards based on the Atheros AR9280 chipset.
Two different kinds of MIMO antennas were used - a Ubiquity Rocket Dish 5G30
offering enough gain for high modulations over 10.3 km and a Mars MA-WA56-
DP25NB at both sides of the 5 km link. The operating System is Debian Squeeze
using a modified kernel which is optimized for long distance links in terms of
MAC layer timings, contention window sizes and transmission buffer to ensure
that enough packets are available for A-MPDU aggregation. As wireless driver
serves ath9k and the rate control algorithm is PID while most of the time a
fixed rate is chosen to prevent instable performance as shown in [13]. Some test
beforehand proofs that with two notable exception every 802.11n-enhancement
is already implemented in the ath9k driver - short guard interval and A-MSDU
aggregation are not available in ad hoc mode. All measurements were done using
a tool called 80211Analyzer developed at Fraunhofer FOKUS at the receiver and
the mgen traffic generator at the transmitter. The 80211Analyzer receives WiFi
frames via the monitor device which is working parallel to the standard interface
7
The price of the cross-polarized antenna is marginally higher.
8
LOS with obstacles looming in the Fresnel Zone.
9
Slower CPU: AMD Geode LX 800.
 IEEE 802.11n MIMO Links for Broadband Back-Hauling 7

Table 1. Fixed parameters for all measurements

Parameter Value (default)

Distance (ACK-Timeout and Slot) 5 km/10 km (0.3 km)
Chance to retry a packet 1(7)
Transport layer protocol UDP
Payload 1450 Bytes
Packets per second =Physical-rate/Payload

offering the ability to evaluate all lower packet headers as well as - after reorder-
ing - any possible retransmissions and losses. To ensure comparability Table 1
shows a fixed set of parameters used for all following measurements.

3.1 Results

Utilizing the three introduced test scenarios this section describes the performed
experiments and their results. By stepwise enabling the .11n features intro-
duced in Sect. 2.1 we are in the situation of evaluating their applicability for
long distance links separately. Figure 2 shows the result for enabling the OFDM-
enhancements as well as A-MPDU aggregation with a maximum size of 216
byte. By stepwise increasing the Modulation and Coding Scheme (MCS) from
0–7 every minute the physical data rate increases to 65 Mbps. The MAC layer

70 100
phy-rate Lab 10.3km 5km
60
Througput [Mbps]

50
10

Loss [%]
40
30
1
20
10
0 0.1
0 1 2 3 4 5 6 7
MCS

Fig. 2. OFDM-enhancements and A-MPDU

60 40
10.3km Delay phy-rate
50 35
Througput [Mbps]

30
Delay [ms]

40 25
30 20
20 15
10
10 5
0 0
210 211 212 213 214 215 216
A-MPDU factor

Fig. 3. Influence of the A-MPDU factor (fixed MCS 5)

8 M. Rademacher et al.

aggregation successfully closes the gap between physical and real throughput
induced in the .11a standard [14] by back-offs and inter-frame spaces so that
the throughput for the laboratory and 10.3 km link raises simultaneously to 60
Mbps. A weak RSSI trough bad propagation conditions10 causes that MCS 5
is the best rate for the 5 km link but the behavior for lower or equal to MCS
5 is identical to the other scenarios. To evaluate the inevitably increasing delay
induced by aggregation we applied all available A-MPDU factors to the 10 km
link as shown in Fig. 3. The first two A-MPDU factors have no difference in
throughput and latency due to a large payload in combination with a MTU
of 1470 byte. After that, the throughput increases with every doubling step of
the A-MPDU factor. The increase is not linear, it is steep at the beginning
and flattens at the end because of the fixed time for back-offs, IFS and block
acknowledgments. As expected the latency rises with increasing A-MPDU fac-
tor which occurs due to the longer buffering of the packets before transmitting
them in an aggregated way. While the relative throughput increase between the
two highest A-MPDU factors is low, the increase in the latency is with 6 ms
high in comparison, but also approximately computable11 . Figure 4 pictures the
applicability of cross polarized antennas to use .11n with spatial multiplexing.
As described in Sect. 2.1 MIMO is a physical layer enhancement therefore loss
is included in the plot instead of delay. It can be observed that the throughput

140 100
phy-rate Lab 10.3km 5km
120
Througput [Mbps]

100
10

Loss [%]
80
60
1
40
20
0 0.1
8 9 10 11 12 13 14 15
MCS

Fig. 4. Multiple input multiple output (20 MHz)

300 100
Phy-rate Lab 10.3km 5km
250
Througput [Mbps]

200 10
Loss [%]

150
100 1
50
0 0.1
8 9 10 11 12 13 14 15
MCS

Fig. 5. 802.11n on long distance links

10
This applies to all forthcoming measurements as well.
11 215 byte
40M bps
= 6.5 ms.
 IEEE 802.11n MIMO Links for Broadband Back-Hauling 9

increased nearly by factor two using the aerials in the laboratory as well as the
cross polarized antennas on the long distance links. This indicates that the cross
polarized antennas are suitable for long distance .11n MIMO links. The attenu-
ation between the two streams on different polarizations is sufficient to form a
nearly perfect channel matrix, which is an unexpected result. By increasing the
channel width to 40 MHz our last measurement provides a complete view using
all available 802.11n enhancements at the same time. Figure 5 pictures that the
throughput in the laboratory environment increases to nearly 200 Mbps. As
mentioned, the hardware at the radio tower is older with less CPU power than
the other systems. With the usage of MCS 14 the CPU was saturated and the
mgen process failed creating the amount of packets needed for this data rate.
Nevertheless, the throughput rises up to 170 Mbps over 10km using 802.11n and
by evaluating the lower modulation there are no reasons to assume that 200
Mbps are not possible.

4 Conclusion

By exploiting the main features of the IEEE802.11n standard, the maximum

actual throughput for a 10 km link inside our WiBACK network increases from
30 Mbps to 170 Mbps measured in a real scenario. Hence, our results indicate
that, for our investigated use cases, the IEEE802.11n physical enhancements
are also applicable to long-distance links. With such data rates we are able
to provide a cost and energy efficient alternative to existing technologies, such
as fixed microwave links, in wireless Back-Hauling. The main difference between
the laboratory environment and the two real point-to-point links is the increased
acknowledgement timeout, which results in a slightly lower throughput. Espe-
cially the usage of a single cross polarized antenna provides a very cost-effective
solution for increasing the throughput by nearly 100 % without any changes to
other QoS parameters such as latency. Doubling the bandwidth to 40 MHz would
have the same effect, but is often discouraged due to possible interferences with
other links within the scarce frequency spectrum. Additional key features are
the newly introduced MAC layer using packet aggregation and selective block
ACKs. Considering the latency, this feature has the disadvantage of increasing
the airtime of an aggregated frame, which may cause medium access latencies for
concurrent traffic. However, the queuing time is predictable and packet aggre-
gation is controllable in several ways by setting the A-MPDU factor and by
enabling this feature just for certain traffic classes.

4.1 Future Work

Optimizing parameters such as back-off timings, AIFS for traffic class separation
as well as various queue lengths is the next important step to further increase the
Quality of Service and thereby the user experience in our WiBACK network. This
optimization should be based on a traffic mix including different packet sizes,
acknowledgments and face challenges occurring with protocols like TCP. We
10 M. Rademacher et al.

plan to apply the findings of this paper to sub-GHz WiFi (i.e. TVWS, 802.11ah)
to increase the efficiency of this high potential frequency ranges.

Analytical Model. To perform this optimization we are currently evaluating,

exploiting and extending different analytical models describing the IEEE802.11
MAC layer such as the one by Bianchi [15]. Describing our point-to-point links
with an accurate model will provide us besides the optimization with the oppor-
tunity of a centralized capacity estimation of our network.

Acknowledgment. This work has been funded by the Federal Ministry of Education
and Research of the Federal Republic of Germany (Förderkennzeichen 01 BU 1116,
SolarMesh - Energieeffizientes, autonomes großflächiges Sprach- und Datenfunknetz
mit flacher IP- Architektur). The authors alone are responsible for the content of this
paper.

References
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2011), Kota Kinabalu, Sabah, Malaysia, Oct 2011
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nected - economical constraints and technical requirements towards a Back-
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11. Skordoulis, D., Ni, Q., Chen, H.-H., Stephens, A., Liu, C., Jamalipour, A.: IEEE
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 Review of Power Line Communications
Standards in Africa

A.R. Ndjiongue, A.J. Snyders, Hendrick C. Ferreira, and S. Rimer(&)

Faculty of Engineering and the Built Environment, University of Johannesburg,

P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa
{ndjiongue,ajsnyders,hcferreira,suvendic}@uj.ac.za

Abstract. The standards in power line communications (PLC) calibrate

parameters such as frequencies allocation, signal level, security, topology of the
network and many others parameters. The leap forward of power line com-
munications technology is motivated by the willingness of the standardization
organizations (SDO)s such as ITU, IEC, ISO, IEEE, CENELEC to define how
the technologies are going to be deployed. This paper presents the different
SDOs, Alliances and groups regulating the PLC sector. The interoperability and
coexistence for some technologies are underlined. The process of developing
PLC standards by ITU, IEEE 1901, CENELEC is described. The advantages
and disadvantages of using PLC technology in Africa are discussed.

Keywords: Standards
SDOs
Alliances
Technologies
PLC
Commu-

nication
Frequency

1 Introduction

To provide efficient transparent communication over the power line network, stan-
dards are fundamental. A standard is a document providing rules requirements and
guidelines for a product, process and services [1, 2]. The document is prepared and
produced by the standardization organizations (SDOs). In power line communications
(PLC), the SDOs focus on the physical (PHY) and on the data link (DLL) layers as
shown on Fig. 1. The other layers are reserved for the application. The power line
communications technology shares some regulations with the wired based commu-
nication technologies such as telephone wire.
Power line communications is characterized by two regulated segments named
narrow band power line communications (NBPLC) and broad band power line
communications (BBPLC). BBPLC deals with frequencies above 1 MHz while
NBPLC deals with frequencies less than 500 kHz. The range of frequencies between
3 kHz and 145.8 kHz is regulated by the European committee for electrotechnical
standardization (CENELEC) [3, 4]. It is suitable for low data transmission over power
line. It is possible to deploy high data transmission over NBPLC in the range of
frequencies between 145.3 kHz and 478.125 kHz, which corresponds to the second
frequency band of the federal communication commission (FCC) [5]. This paper
presents in Sect. 2 the repartition of the frequencies between SDOs. Forthwith, some
applications of power line communications for both NBPLC and BBPLC are

Ó Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014
T.F. Bissyandé and G. van Stam (Eds.): AFRICOMM 2013, LNICST 135, pp. 12–21, 2014.
DOI: 10.1007/978-3-319-08368-1_2
 Review of Power Line Communications Standards in Africa 13

PLC layers Application based layers

Medium

Transport
Network
PHY

DLL
OSI

Physical connection Media Access Protocols

to the medium (CSMA/CA/CD, TDMA)

Fig. 1. Illustration of the PLC layers

presented in Sect. 3. The Sect. 4 talks about coexistence of standards and interoper-
ability between technologies. Section 5 gives the advantages and disadvantages of
PLC technology in African context.

2 Frequency Allocation Chart

The international telecommunication union (ITU), the international organization for

standardization (ISO), the international electrotechnical commission (IEC), the
institute of electrical and electronics engineers (IEEE) and CENELEC are the main
SDOs that develop and propose standards in communication sector. G3-PLC, Pow-
erline intelligent metering evolution (PRIME), the american national standards
institute (ANSI), KNX and HOMEPLUG are some groups and alliances that develop
and deploy standards and technologies in power line communications sector. These
SDOs, groups and alliances are characterized by their frequency band. Some work on
NBPLC while others are on BBPLC. SDOs such as IEEE and ITU are proposing
standards and technologies for both NBPLC and BBPLC. The authors analyzed the
frequency band of each SDO and alliance and the summary of the bands occupied are
presented on Fig. 2. The IEEE organization proposes for NBPLC to work in the range
of frequencies between 9 kHz and 500 kHz [6]. The ITU organization works on the
range of frequencies from 3 kHz to 490 kHz, while CENELEC proposes the fre-
quencies between 3 kHz and 148.5 kHz. Over BBPLC, IEEE works on the range of
frequencies between 2 MHz and 50 MHz while the ITU organization proposes a
wider range of frequencies: from 2 MHz to 100 MHz [7, 8]. The CENELEC orga-
nization does not work on BBPLC [2, 9]. G3-PLC, PRIME, ANSI and KNX propose
technologies using the CENELEC’s frequency bands. G3-PLC works on the range
between 35 kHz–90 kHz while PRIME alliance uses the frequency band 42 kHz–
90 kHz. ANSI proposes technologies for the frequencies from 86 kHz–131 kHz and
KNX works with the band of frequencies between 125 kHz to 140 kHz [10].
HomePlug is present over the whole PLC’s frequency band. It proposes HomePlug
Green PHY (HPGH NB), standards and technologies over narrow band occupying the
14 A.R. Ndjiongue et al.

a Frequencies between 3 kHzand 2 MHz

b Frequencies between 2 MHzand 300 MHz

Fig. 2. Power line communications frequencies allocation by SDOs, alliances and groups

range of frequencies between 120 kHz and 400 kHz. HomePlug also proposes
HomePlug Green PHY (HPGP BB) and HomePlug AV (HPAV) using the frequency
band 1.8 MHz to 30 MHz [11]. HomePlug AV2 technologies extends the HPAV’s
frequencies to 86 MHz [10, 12]. HPGP BB differs from HPAV on the modulation
schemes used, the forward error correction (FEC) methods and the access modes [10].
 Review of Power Line Communications Standards in Africa 15

The IEEE 1901-2010 standard uses the frequencies between 2 MHz and 50 MHz.
The IEEE 1901.2 provides standards for low frequency, less than 500 kHz, narrow
band power line communications for smart grid applications. Its uses the frequency
band 10 kHz–490 kHz for low and high data transmission [6]. The ITU organization
separates standards for the physical layer (PHY) and for the data link layer (DLL).
It proposes a technology named ITU-T G.hnem for NBPLC. The ITU-T G.9955
standard specifies the regulations for PHY while the ITU-T G.9956 standard gives rules
for DLL [8]. The technology proposed by the ITU organization on BBPLC is ITU-T
G.hnem. The ITU-T G.hnem complies with two different standards: The ITU-T G.9960
standard developed for PHY layer and the ITU-T G.9961 standard proposed for DLL.
The ITU organization on BBPLC specifies a platform for smart grid (2 MHz to
30 MHz), it also gives specifications to differentiate between two cases: when the
distance between two modules is greater than 50 m (2 MHz to 50 MHz) and when the
distance is greater than 100 m (2 MHz to 100 MHz) [8]. The CENELEC organization
is composed of many technical bodies. One of them is in charge of developing
EN50065-1 standard (Signaling on low-voltage electrical installations in the frequency
range 3 kHz to 148.5 kHz - Part 1: General requirements, frequency bands and elec-
tromagnetic disturbances) [3, 4]. The EN50065-1 standard divides its frequency band
in 4 sub-bands [3, 4]: CENELEC-A (3 kHz–95 kHz), reserved exclusively for energy
providers, and respectively CENELEC B-C-D (95 kHz–125 kHz; 125 kHz–140 kHz
and 140 kHz–148.5 kHz). The CENELEC B-C-D sub-bands are opened for end user
applications. The 2010’s version of the EN50065-1 is superseded by the EN50065-1:
2011. The EN50065-1: 2011 standard was ratified on 2011-03-21; it was available on
2011-04-22, announced on 2011-09-21 and published on 2012-03-21 [13].
The CENELEC organization shares some information with the international
electrotechnical commission (IEC), with the european committee for standardization
(CEN) and with the european telecommunication standards institute (ETSI). Some
PLC’s projects under IEC are developed in collaboration with the international
organization for standardization (ISO).
The IEC NBPLC’s standards propose to work between 3 kHz and 76 kHz [6]. The
special international committee on radio interference (CISPR) acting under IEC gives
the limits of the interferences. The disturbances limits for PLC systems are defined by
the CISPR22 standard. The electric power research institute (EPRI) organization in
China prefers the range between 3 kHz and 90 kHz while the FCC organization in
USA works over the range from 10 kHz to 490 kHz [14]. The association of radio
industries and businesses (ARIB) organization in Japan proposes the ARIB STD –
TB4 standard, using the range between 10 kHz and 450 kHz. In Canada, the inter-
ference causing equipment standard (ICES) proposes ICES – 006 standards working
in the range of frequencies between 0 and 535 kHz [15]. The transmission level for all
the standards is compatible with the graph proposed on Fig. 3. It is proposed by the
CENELEC and the FCC organizations [3, 4]. In NBPLC frequency bands, the ITU-T
G.hnem and the IEEE P1901.2 technologies, ANSI, HomePlug, PRIME, ISO and IEC
use the signal level proposed in EN50065-1 by the CENELEC organization, they
use FCC’s signal level when the frequency is out of the CENELEC’s range of fre-
quencies. In certain applications, the IEC standards use exclusively the CENELEC’s
16 A.R. Ndjiongue et al.

Transmission level
NB
PLC
BB PLC

CENELEC IEEE IEC/ISO ITU -T G

FCC

134 dBµV Voltage limits forspurious outputs [1]

116 dBµV
89 dBµV
- 1 D, FCC
1 A,B, C

CENELEC EN50065
CENELEC EN50065

60 dBµV
-

56 dBµV

IEEE 1901 IEEE 1901

30 Frequency MHz
0.01 0.2 0.5 2 5

Fig. 3. Transmission level spurious output [3, 4]

transmission level. The standards’ elaboration methods within the main IDOs were
analysed by the authors and the summary is presented on Fig. 4.

3 Applications of Power Line Communications

The applications of power line communications technologies cover the whole axis of
frequencies proposed in Fig. 2. In accordance with the application, the axis is divided
into three portions as indicated in Fig. 5. Any portion is suitable for some specific
applications.
The broad band PLC uses the frequency band between 1 MHz and 300 MHz. It is
suitable for high data rate transmission, more than 10 Mbps. The NBPLC’s portion
uses the CENELEC bands for low data rate transmission, less than 50 kbps. Over the
frequencies from 145.3 kHz to 500 kHz, the NBPLC’s technologies are used to
perform high data rate transmission between 50 kbps and 1 Mbps. Thereby, the
applications of the power line communications technology are related to the frequency
band used. The applications of BBPLC can not be deployed over NBPLC frequencies.
The deployment of the technology over each range of frequencies is supported by
many companies and organisations. Table 1 gives some specifications on the appli-
cation of the power line communications technology. It shows the frequency band,
some modulation schemes proposed by the SDOs for both NBPLC and BBPLC. The
complexity of the forward error correction (FEC), the access method and some
companies and organisations supporting the standards are also mentioned.
The NBPLC frequency bands are used for metering, lighting, energy and grid
management.
 Review of Power Line Communications Standards in Africa 17

a: Building up for ITU-T G.hn and G.hnem

b: Building up for IEEE1901, CENELEC and IEC

Fig. 4. PLC standards building up

The BBPLC frequency band is used for applications such as last mile telecom,
voice over IP and high definition television. Companies such as Gorlitz and alliances
such as G3-PLC, PRIME are specialised in meter manufacturing. The KNX organi-
sation is the standard for home and building control.
18 A.R. Ndjiongue et al.

50 kbps > DR 148.5 kHz 500 kHz 10 Mbps < DR

3 kHz
145.3 kHz 50 kbs < DR < 1Mnps 1 MkHz 300 MkHz

Fig. 5. Data rate (DR) repartition over PLC frequencies

Table 1. Applications of PLC [6, 9, 12, 15, 16]

4 Interoperability and Coexistence

This section presents the practical issue related to the deployment of standards and
technologies in power line communications sector. What are the standards and
technologies that can work together in the same network and in the same frequency
band? The ITU organisation has developed the inter system protocol (ISP) scheme
named ITU-T G.9972. The ITU-T G.9972 standard allows at least four technologies
simultaneously [10]. The recommendations of ITU were accepted by some other
organisations such as IEEE. The IEEE organisation has developed the IEEE 2030
standard for smart grid interoperability. The IEEE 1901.2 devices integrate the
interoperability option for PRIME and G3 legacy specifications. Those devices must
implement CENELEC A band [16].
The coexistence must be seen as the ability of more than one technology, network,
system or device to exchange data and to be able to use the information exchanged.
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