**1. Introduction**

16 Will-be-set-by-IN-TECH

[32] R. Ahuja, T. Magnanti, J. Orlin (1993) Network flows: theory, algorithms, and

[34] S. Jung et al. (2008) A geographic routing protocol utilizing link lifetime and power

[35] C. Perkins, E. Belding-Royer, S. Das. Ad hoc on-demand distance vector (AODV)

control for mobile ad hoc networks. in: Proc. ACM FOWANC.

routing. http://www.ietf.org/rfc/rfc3561.txt. RFC 3561.

applications. Prentice hall.

[33] NS-2. http://www.isi.edu/nsnam/ns/.

*Research background:* Next generation fixed wireless broadband networks have immensely been deployed as mesh networks in order to provide and extend access to the internet. These networks are characterised by the use of multiple orthogonal channels available within the industrial, scientific and medical (ISM) liscensed-free frequency bands. Nodes in the network have the ability to simultaneously communicate with many neighbours or stream different versions of the same data/information using multiple radio devices over orthogonal channels thereby improving effective "online" channel utilisation (Kodialam & Nandagopal, 2005). The ability to perform full duplex communication by individual multiradio nodes without causing network interference has also been achieved through decentralized transmission power control schemes in (Olwal, 2010; Olwal et al., 2011). Allen et al. (2007) alluded that multiple radios that receive versions of the same transmission may together correctly recover a frame that would otherwise be lost based on multipath fading, even when any given individual radio cannot. Many such networks emerging from standards such as IEEE 802.11 a/b/g/n and 802.16 are already in use, ranging from prototype testbeds (Eriksson et al., 2006) to complete solutions (Mesh Dynamics, 2010).

The increasing question is how the theoretical capacity of such static multi-radio multichannel (MRMC) network scales with the node density, irregularity of the terrain and the presence of tree foliage (Intini, 2000). In their seminal work, Gupta and Kumar (2000) determined the capacity of single radio single channel networks. Their findings have been later extended to derive the capacity bounds of the MRMC configurations of a network scope by Kyasanur and Vaidya (2005). In addition, the link throughput performance parameters in IEEE 802.11 networks have also been discussed in Berthilson & Pascual (2007). However, the considered MRMC network architecture has so far been presented with a number of impractical assumptions. The first assumption asserts that the location of nodes and traffic patterns can be controlled in arbitrary networks. The second assumption claims that channel fading

© 2012 Olwal et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

can be excluded in the capacity analysis such that each frequency channel can support a fixed data rate. Lastly, nodes are randomly located on the surface of a torus of unit area to avoid technicalities arising out of edge effects. However, in realistic networks, location of nodes is determined by the irregularity of the terrain, the presence of tree foliage (Tse & Viswanath, 2005), and users' needs and their locations (Makitla et al., 2010). Moreover, typical rural based wireless networks can be described by (i) long single hop links, (ii) limited and unreliable energy sources, and (iii) clustered distribution of Internet users (Ishamel et al., 2008).

Achievable Capacity Limit of High Performance Nodes for Wireless Mesh Networks 151

**Figure 2.** Block diagram of HPNTM (Makitla et al., 2010)

and Node B) with end to end (E2E) Ethernet cable.

**Figure 3.** Single link architecture of HPNs

In this study, we shall concentrate on the backhaul terminal connectivity of the HPNs. The backhaul terminal connectivity offers aggregated traffic volumes of all flows within the network. The traffic flows traverse long links between any two HPNs and are faced with severe climatic conditions. Thus, evaluating the capacity limits of such links provides useful inputs toward optimal design of the cross-layer protocols. Figure 3 illustrates the broadband for all (BB4allTM) architecture of a single wireless link based on two HPNs (that is, Node A

In order to address some of these issues and obtain high network throughput performance, high performance nodes (HPNs)TM for community-owned wireless mesh networks, have been implemented in most parts of rural South Africa (Kobus, et al., 2009). The innovation as shown in Figure 1, has been developed by the CSIR Meraka Institute and it provides high network throughput (capacity). The HPNTM is an IEEE 802.11 based multi-interface node made up of three interfaces or radio devices and controlled by an embedded microcontroller technology (Makitla et al., 2010). To ensure high speed performance, the innovation has the first radio interface card attached to a 5 GHz directional antenna for backhaul mesh routing, the second interface card is connected to a 5 GHz omni-directional antenna for backhaul mesh connectivity and access. The third radio interface card is attached to a 2.4 GHz omnidirectional antenna for mesh client access network. As shown in Figure 2, the HPN block diagram has a weather proof Unshielded Twisted Pair (UTP) connector at the bottom of the node that provides Power-Over-Ethernet (PoE) and Ethernet connectivity to the HPN. To attach the HPN to a pole or a suitable structure, a mounting bracket is fixed at the back of the router (See Makitla et al., 2010) for other operational details. The HPNs are often installed on roof tops, street poles and buildings of villages, local schools, clinics, museums and agricultural farmlands.

**Figure 1.** High performance node (HPN)TM (Makitla et al., 2010)

Achievable Capacity Limit of High Performance Nodes for Wireless Mesh Networks 151

**Figure 2.** Block diagram of HPNTM (Makitla et al., 2010)

150 Wireless Mesh Networks – Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

al., 2008).

and agricultural farmlands.

**Figure 1.** High performance node (HPN)TM (Makitla et al., 2010)

can be excluded in the capacity analysis such that each frequency channel can support a fixed data rate. Lastly, nodes are randomly located on the surface of a torus of unit area to avoid technicalities arising out of edge effects. However, in realistic networks, location of nodes is determined by the irregularity of the terrain, the presence of tree foliage (Tse & Viswanath, 2005), and users' needs and their locations (Makitla et al., 2010). Moreover, typical rural based wireless networks can be described by (i) long single hop links, (ii) limited and unreliable energy sources, and (iii) clustered distribution of Internet users (Ishamel et

In order to address some of these issues and obtain high network throughput performance, high performance nodes (HPNs)TM for community-owned wireless mesh networks, have been implemented in most parts of rural South Africa (Kobus, et al., 2009). The innovation as shown in Figure 1, has been developed by the CSIR Meraka Institute and it provides high network throughput (capacity). The HPNTM is an IEEE 802.11 based multi-interface node made up of three interfaces or radio devices and controlled by an embedded microcontroller technology (Makitla et al., 2010). To ensure high speed performance, the innovation has the first radio interface card attached to a 5 GHz directional antenna for backhaul mesh routing, the second interface card is connected to a 5 GHz omni-directional antenna for backhaul mesh connectivity and access. The third radio interface card is attached to a 2.4 GHz omnidirectional antenna for mesh client access network. As shown in Figure 2, the HPN block diagram has a weather proof Unshielded Twisted Pair (UTP) connector at the bottom of the node that provides Power-Over-Ethernet (PoE) and Ethernet connectivity to the HPN. To attach the HPN to a pole or a suitable structure, a mounting bracket is fixed at the back of the router (See Makitla et al., 2010) for other operational details. The HPNs are often installed on roof tops, street poles and buildings of villages, local schools, clinics, museums

In this study, we shall concentrate on the backhaul terminal connectivity of the HPNs. The backhaul terminal connectivity offers aggregated traffic volumes of all flows within the network. The traffic flows traverse long links between any two HPNs and are faced with severe climatic conditions. Thus, evaluating the capacity limits of such links provides useful inputs toward optimal design of the cross-layer protocols. Figure 3 illustrates the broadband for all (BB4allTM) architecture of a single wireless link based on two HPNs (that is, Node A and Node B) with end to end (E2E) Ethernet cable.

**Figure 3.** Single link architecture of HPNs

Achievable Capacity Limit of High Performance Nodes for Wireless Mesh Networks 153

Here, *R* is the single link rate in bits/s computed by taking into account multipath effects and innovative HPNs built-in structure, *n* is the number of HPNs, *m* is the number of radio interface cards per each HPN, *c* is the number of frequency channels that do not cause interference in duplex communication, 0 1 *p* is the irregularity rate (probability) of HPN

The rest of the chapter is organised as follows. Section 2 provides a description of a typical rural community mesh network in which the BB4allTM architecture proposal can be applied. In Section 3, issues of theoretical capacity limits for single links are discussed. Section 4 analyses upper bound capacity limits for mesh networks in real deployments. Section 5 furnishes the numerical capacity limit of a selected real network in a given rural area size. The chapter is concluded with highlights of the main contribution of this study and future

**2. Rural community mesh network: A case of Peebles valley mesh in** 

counseling, testing and Anti-retroviral (ARV) treatment (Johnson & Roux, 2008).

Figure 4 demonstrates architecture of the PVM network when HPNs are deployed. In this figure, the clinic connects to surrounding schools, homes, farms and other clinic infrastructure through a mesh network. The network is seen as community asset with some of the equipment at key nodes are actually belonging to the community. In this area mesh connec-

Scalable connectivity to the hilly terrain, over multiple hops based long distances and

 Auto-configurable traffic routing mechanisms with minimal human interventions. This feature ensures network sustainability in an area with apparent low skilled technical-

Peebles valley mesh (PVM) is a typical rural community mesh network that is funded by the International Development Research Centre (IDRC) and is deployed in Mpumalanga province in South Africa (Johnson, 2007). The conventional PVM network, consists of nine (9) single radio nodes, and covers an area of about 15 square kilometres in Masoyi tribal land. The Masoyi tribal land is located at the North East of White River along the road to the Kruger National Park in South Africa. The land is hilly with some large granite outcrops and it has a valley that stretches from the AIDS care training and support (ACTS) clinic and divides the wealthy commercial farms from the poorer Masoyi tribal area. The Masoyi community is underserviced with lack of tarmac roads and most houses are lacking running water. However, there is unreliable electricity present in the Masoyi area. The power outages occur on average one outage in seven days and might even last up to a full day (i.e., 24 hours). Albeit the government subsidizes the cost of electricity, a large population cannot afford electricity fees due to the low economic levels of the area. ACTS clinic (a nongovernmental organization sponsored clinic) provides medical services to AIDS patients,

is the HPN distribution density that is varied over a fixed deployment area.

placement, and

**South Africa** 

tivity offers:

through non line of sight (NLOS).

personnel who cannot regularly maintain the network.

research and development (R&D) perspectives.

*Research problem or questions*: The main problem constitutes the need to increase capacity of community owned existing wireless broadband networks so that network users can scale without losing any connectivity and multimedia services can be provided in remote and rural areas (Mekuria et al., 2012). This problem is further subdivided into a number of research questions. Firstly, what is the achievable capacity limit of the HPNs based on IEEE 802.11a air interface under multipath fading channels (Tse & Viswanath, 2005)? Secondly, what is the achievable capacity limit of the HPNs based on IEEE 802.11n air interface under multi-input multi-output (MIMO) fading channels? Thirdly, what is the achievable end-toend (E2E) capacity limit in HPNs in community mesh networks under: regular, irregular, and clustered node placements? The study assumes that there is no frequent channel switching even though the number of channels may be greater than the number of radios per node (Olwal, 2010). This implies that, non overlapping channels are assigned statically to available radio devices over a transmission period. Statically assigned channel over a given interval is a reasonable consideration since there is high probability that traffic volumes in rural areas are low compared to urban areas most of the times.

*Research objectives:* In order to investigate the capacity performance of the HPNs, the first aim of this chapter is to characterize the impact of multipath and MIMO fading channels on achievable theoretical capacity limits of single links IEEE 802.11a and IEEE 802.11n based standards. The second aim is to derive the impact of number of interfaces and channels per each HPN on the E2E capacity limits of BB4allTM mesh networks. This objective is achieved by considering a varying node density over a fixed deployment area, and the rate of a single wireless link that depends on the physical communication barriers.

*Methodology:* In order to achieve these objectives, firstly, the per link capacity limit under frequency selective channel is developed using conventional approaches in literature. The analytical capacity results of the BB4allTM architecture are numerically compared to IEEE 802.11a standard data sheet in order to understand the performance gain of HPNs. Secondly, the per link capacity limit for MIMO fading channels is developed and the results are numerically compared to IEEE 802.11n standard data sheet in order to show case the benefits of HPNs. Thirdly, given a typical rural community network with a pre-defined deployment area having varying node density, the impact of interfaces and channels per node on the capacity of BB4allTM mesh architecture is derived. The capacity limits of regular, irregular and cluster network topologies are obtained and compared with results from Kyasanur and Vaidya (2005) for arbitrary networks.

*Research results:* Analytical results indicate that the multipath fading channels and MIMO channels can be exploited to improve channel diversity in community mesh networks. Diversity improves capacity of wireless links over multiple paths and through multiple frequency channels. For regular, irregular and clustered node placements, the following analytical results were obtained for *the upper bound end-to-end capacity limit*, respectively,

$$\mathcal{O}\left(nR\sqrt{\frac{mc}{\delta}}\right), \mathcal{O}\left(Rn\sqrt{\frac{mc}{\delta p}}\right), \text{and } \mathcal{O}\left(R\sqrt{\frac{nmc}{1}\left(\frac{n\_1}{\delta\_1} + \frac{n\_2}{\delta\_2}\right)}\right).$$

Here, *R* is the single link rate in bits/s computed by taking into account multipath effects and innovative HPNs built-in structure, *n* is the number of HPNs, *m* is the number of radio interface cards per each HPN, *c* is the number of frequency channels that do not cause interference in duplex communication, 0 1 *p* is the irregularity rate (probability) of HPN placement, andis the HPN distribution density that is varied over a fixed deployment area.

152 Wireless Mesh Networks – Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

volumes in rural areas are low compared to urban areas most of the times.

wireless link that depends on the physical communication barriers.

Kyasanur and Vaidya (2005) for arbitrary networks.

*Research problem or questions*: The main problem constitutes the need to increase capacity of community owned existing wireless broadband networks so that network users can scale without losing any connectivity and multimedia services can be provided in remote and rural areas (Mekuria et al., 2012). This problem is further subdivided into a number of research questions. Firstly, what is the achievable capacity limit of the HPNs based on IEEE 802.11a air interface under multipath fading channels (Tse & Viswanath, 2005)? Secondly, what is the achievable capacity limit of the HPNs based on IEEE 802.11n air interface under multi-input multi-output (MIMO) fading channels? Thirdly, what is the achievable end-toend (E2E) capacity limit in HPNs in community mesh networks under: regular, irregular, and clustered node placements? The study assumes that there is no frequent channel switching even though the number of channels may be greater than the number of radios per node (Olwal, 2010). This implies that, non overlapping channels are assigned statically to available radio devices over a transmission period. Statically assigned channel over a given interval is a reasonable consideration since there is high probability that traffic

*Research objectives:* In order to investigate the capacity performance of the HPNs, the first aim of this chapter is to characterize the impact of multipath and MIMO fading channels on achievable theoretical capacity limits of single links IEEE 802.11a and IEEE 802.11n based standards. The second aim is to derive the impact of number of interfaces and channels per each HPN on the E2E capacity limits of BB4allTM mesh networks. This objective is achieved by considering a varying node density over a fixed deployment area, and the rate of a single

*Methodology:* In order to achieve these objectives, firstly, the per link capacity limit under frequency selective channel is developed using conventional approaches in literature. The analytical capacity results of the BB4allTM architecture are numerically compared to IEEE 802.11a standard data sheet in order to understand the performance gain of HPNs. Secondly, the per link capacity limit for MIMO fading channels is developed and the results are numerically compared to IEEE 802.11n standard data sheet in order to show case the benefits of HPNs. Thirdly, given a typical rural community network with a pre-defined deployment area having varying node density, the impact of interfaces and channels per node on the capacity of BB4allTM mesh architecture is derived. The capacity limits of regular, irregular and cluster network topologies are obtained and compared with results from

*Research results:* Analytical results indicate that the multipath fading channels and MIMO channels can be exploited to improve channel diversity in community mesh networks. Diversity improves capacity of wireless links over multiple paths and through multiple frequency channels. For regular, irregular and clustered node placements, the following analytical results were obtained for *the upper bound end-to-end capacity limit*, respectively,

*mc mc nmc n n nR O Rn <sup>R</sup>*

 *p*

, ,and . <sup>1</sup>

1 2 1 2

  The rest of the chapter is organised as follows. Section 2 provides a description of a typical rural community mesh network in which the BB4allTM architecture proposal can be applied. In Section 3, issues of theoretical capacity limits for single links are discussed. Section 4 analyses upper bound capacity limits for mesh networks in real deployments. Section 5 furnishes the numerical capacity limit of a selected real network in a given rural area size. The chapter is concluded with highlights of the main contribution of this study and future research and development (R&D) perspectives.
