Capacity and Coverage Analysis of Rural
Multi-Radio Multi-hop Network Deployment using
IEEE802.11n Radios
Alvin Ting, David Chieng and Kae Hsiang Kwong
Wireless Communications MIMOS Berhad Kuala Lumpur, Malaysia
{kee.ting,ht.chieng,kh.kwong}@mimos.my
Abstract— This paper presents the capacity and coverage performance analysis of a multiradio multihop network deployment using IEEE802.11n radios for a typical rural area in Malaysia. Insights on the relationships between various key design parameters particularly backhaul link rate, backhaul link distance/multihop distance, coverage size per MAR/total coverage size, number of MIMO spatial multiplex (SM) stream, number of MAR per branch and effective capacity per access point or per user are acquired. Two optimization objectives namely 1) to maximize coverage size and 2) to maximize backhaul distance are introduced.
Keywords-component; Wireless Mesh/Multihop Network; Rural Deployment; IEEE 802.11n; Backhaul
I. INTRODUCTION
Accelerating broadband penetration and bridging the digital divide between rural and urban communities have long been one of the main agenda of governments worldwide. Over the years, various initiatives have been launched in Malaysia to provide Internet access to the rural communities. More recently Ministry of Information, Communications and Culture and the Malaysian Communications and Multimedia Commission launched the “Kampung WiFi” initiative [1], which aims to accelerate the national broadband penetration at the rural areas. According to NBI, the broadband access in rural areas shall include Basic Telephony Access (via fixed and mobile networks), broadband connected Community Broadband Libraries (CBLs) and Community Broadband Centres (CBCs). Motivated by the lacked of wired infrastructure in these areas, Wireless Mesh Network (WMN) has become a highly promising means to provide broadband access. As for the radio technology, IEEE802.11 WLAN or WiFi is naturally preferred due to cost factor and wide spread market adoption.
Over the years WMN technologies, particularly those based on WiFi radios, have evolved from single radio systems to multiradio systems involving heterogeneous radio interfaces such as IEEE802.11a, b, g and n. The most commonly known industrial practice adopts the architecture which comprises of IEEE802.11a radios at the backhaul and IEEE802.11g for the access. This is largely motivated by the fact that IEEE802.11a has more non overlapping channels and much less congested spectrum band. Although WMN offers a wide range of benefits, it continues to suffer capacity limitation due to excessive sharing of capacity as the number of hop increases. To this end
the recently approved IEEE 802.11n standard which offers physical data rates up to 600Mbit/s and higher resiliency towards interference via MIMO technology may change the perception to a certain extent. To date there are already a wide range of 11n-based wireless mesh/multihop network products and solutions in the market. However, the performance of such network is not well understood especially from the capacity and range (coverage) viewpoints. In particular this paper aims to understand the relationships between various key design parameters such as backhaul link rate, backhaul link distance/multihop distance, coverage size per MAR/total coverage size, number of MIMO spatial multiplex (SM) stream, number of MAR per branch and effective capacity per access point which can later be translated into data rate per user using IEEE802.11n radios. The study takes into consideration the unique characteristics in rural deployments, i.e. distribution of the users is rather concentrated like a hotspot but distributed. Unlike the urban case, the distance between hotspots may range from hundreds of meter to tens of km (between villages). Also unlike the urban zones, contiguous coverage is not required. Fig. 1 provides a snapshot of a typical rural deployment in Malaysia.
Figure 1. Case Study: Kampung (village) Ulu Dusun , Sabah, Malaysia.*courtesy of Google Maps.
We first, we developed an analytical model that includes physical layer, mac layer and propagation model to evaluate the capacity and coverage for a multi-radio multihop infrastructure network.. In this model, access radio and
Branch 2
Branch 1
Branch 3
2011 IEEE 10th Malaysia International Conference on Communications (MICC) 2nd – 5th October 2011 | Sutera Harbour Resort, Kota Kinabalu, Sabah, Malaysia
backhaul radio are considered to operate in two different frequencies and not interfering to each other. Inter relationship between coverage and capacity between access and backhaul planes is then derived. Based on the model, we apply the greedy algorithm to find the optimal coverage or backhaul distant for the considered multihop network.
The reminder of this paper is organized as follows. Next section discussed on the related works. Section 3 and 4 presents the assumptions and system model respectively. Results are discussed in section 5 and finally the conclusions and future work is drawn in section 6.
II. RELATED WORKS
Rural WMN deployments both large and small, especially those based on WiFi radio have been widely reported worldwide. [2] offers an insightful study on deploying a rural long distance mesh network in the Scottish Highlands and Islands using IEEE802.11a backhaul and 11b/g access in the 5GHz and2.4GHz bands respectively. In this work, two key aspects are studied namely effect of water level on long distance links and the practicality of using energy sources beyond solar. [3] deployed a WMN in the village of Wray, England that aimed at providing Internet service to the community. The study shares their experiences which cover a wide range of issues from both technical and social aspects. A similar initiative was undertaken in Macha village, Zambia that focuses entirely on proof of concept and investigation into a wide range of issues covering social, geographical, economic, business models and sustainability on top of technical challenges [4]. From their deployments in Digital Gangetic Plains and Ashwini, [6] presents their experiences and insights on network planning, MAC protocol enhancements, network management, power savings, applications and services. A deployment of a similar nature as to the above was reported in [5] but limited to experience gained from commercial single radio 11b/g nodes. To the best of our knowledge no existing work provides WMN capacity and coverage analysis using IEEE802.11n radios with aim to understand the relationships between various key design parameters listed in the previous section.
III. ASSUMPTIONS
A. Architecture
The architecture consists of many Multiradio Access Routers (MARs) that are interconnected via backhaul radios to form a mesh network. Here it assumed that all the traffics flow in multi-hop fashion between MARs and the Gateway (GW) node, which has the access to the core network. The basic architecture is illustrated in Fig. 2 as follows:
Figure 2. Multiradio Multihop Architecture
From this basic architecture, different topologies as shown in Fig. 3 can be formed.
B. Topology Formation
An area of 2.25 square km (1.5x1.5 km) of a village called Kampung Ulu Dusun as depicted in Fig. 1 is selected for our study. It can be observed that houses are not uniformly distributed in this village and they tend to group in some specific areas such as near to the main roads. We call a group of houses as a cluster and there are several clusters in the area. MARs are deployed in the centre of those clusters to provide strongest radio signal to most of houses. In total, only 9 MARs (including gateway) are required to provide full coverage with an average of 25 houses covered by each MAR. All MARs are assumed to be deployed outdoor and the devices are receiving the MAR signal indoor. Also shown in Fig. 1 there are three main branches. Each branch forms a simple chain or tree topology with 1, 2 and 5 MARs respectively.
(a) 1 MAR
(b) 2 MARs (c) 3 MARs
(d) 4 MARs
Possible topology with any additional 1 dimmed MR to make up 5 MRs in total
(e) 5 MARs (due to limitation of the space, not all scenarios are shown) Figure 3. Possible topologies with different number of MAR per GW The dotted lines indicate the possible alternative links that transform a multihop into mesh. From capacity viewpoint, a mesh network is assumed to behave like a multihop tree when the whole network is operating at maximum load. In other words the alternative paths merely provide resiliency and load
With access Without access 5GHz 2.4GHz 2.4GHz 2.4GHz GW MAR GW
backhaul link distance
MAR
access coverage cell with multiple
balancing but not additional capacity [13]. Also since we assume that all MARs provide access, the terms MAR and access cell shall be used interchangeably in the following sections.
C. User Distribution and Traffic Assumption
Even though users are distributed sporadically across the whole deployment area, users are assumed to be uniformly distributed within the radio cell. The coverage size per cell is decided by two factors. Firstly, we need to make sure that the capacity of the MR is sufficient to support all users for a chosen coverage size. Otherwise, a smaller coverage size is needed. Secondly, the coverage is determined by the transmission range of the desired MCS that meets the target data rate to be supported at the cell edge. Target data rate per user represents the maximum data rate (uplink and downlink) that can be enjoyed by each end user. This is representative of the connection speed (or headline speed) typically advertised by a network operator. Since WiFi radio is the focus of this paper, we therefore only consider a generalized capacity.
The overbooking factor is the ratio of the potential maximum demand to the actual bandwidth consumed. Typically the overbooking factor ranges from 50:1 to 10:1 [13] where factor of 50 or 40:1 is usually associated with web browsing application. In other words, the lower the factor, the higher the average bandwidth or QoS demand for that service.
D. Interference
Based on the observations found in [2], [7] and [8], interferences resulting from external WiFi or non WiFi transmitters are minimal and even non-existence in some cases in rural environment. This leaves the major contribution of interference by own networks in form of co channel. Such interference can be mitigated via careful channel planning and directional antenna orientation, which is can be a pretty straightforward task due to wide availability of non-overlapping channels in the 5GHz backhaul.
IV. SYSTEM MODEL
A. Capacity Per User and Per MAR
Calculation of average capacity per user within a MAR is based on equation 1 [11]:
∑
= = MCS N i i eff i user user R N C 1 , , 1 (1)Where
N
user,iis number of users in specific Modulation and Coding Scheme (MCS) zone i, , is the effective data rateof the MCS zone and is the total number of MCS considered. , is defined by:
, , (2)
Where is the link efficiency and , is the raw physical
data rate given by MCS type i.
For the calculation of mean capacity per user, it is assumed that the time of packet transmission is inversely proportional to the access link speed. Therefore, for the packets of the same size, users in high data rate zone will occupy less air-time than
users in lower data rate zone so that equal capacity can be distributed to all users to achieve fair capacity sharing. Hence, the effective capacity per MAR is simply the product of mean capacity per user and total number of users covered by the MAR:
(3)
Assuming MARs can simultaneously transmit (or receive) traffic to each of its neighbouring MARs as well as to its own users, the maximum mean capacity per MAP is then limited by the first hop link capacity, :
max (4)
Where is the number of MAR per branch. In this study, we assumed that the rural houses were mainly made of thick wood which incurred a wall penetration loss of 1.609dB according to [14]. As for the propagation model, the free space path loss model is adopted and given in equation 4:
(
log
(
)
3
)
20
log
(
)
147
10
10+
+
10−
=
d
f
PL
propagationα
(5)Where α is the propagation coefficient, d is the distance in kilometres and f is the carrier frequency in hertz.
The IEEE802.11n standard is used to represent WiFi and its receiver sensitivity is obtained from [15] and the values per MCS are tabulated in TABLE I. The general link budget is given in eqn 5. wall rx sen
G
FM
L
R
EIRP
PL
max=
−
+
−
−
(6)Where PLmax is link budget in dB, EIRP is the effective
isotropic radiated power, Grx is the receiver antenna gain, FM is
fade margin in dB, Rsen is the receiver sensitivity and Lwall is the
wall penetration loss in dB. For backhaul link, wall loss is set to zero and 28dB fade margin is considered instead to ensure link availability of 99.9% as according to [16] based on rayleigh fading model.
TABLEI.MCS AND RECEIVER SENSITIVITY
MCS Sensitivity (dBm)[15] 802.11n Receiver Type Index(i) 20MHz 40MHz BPSK 1/2 8 -95.0 -91.0 QPSK 1/2 7 -93.0 -90.0 QPSK 3/4 6 -90.0 -87.0 16-QAM 1/2 5 -87.0 -84.0 16-QAM 3/4 4 -84.0 -82.0 64-QAM 2/3 3 -80.0 -78.0 64-QAM 3/4 2 -79.0 -76.0 64-QAM 5/6 1 -77.0 -74.0 B. Optimization
First Hop BH Optimisation Algorithm Begin
Define MCS supported by MAR where i 1,2 … ,
Where i=1 is highest order MCS type considered and J is the lowest order MCS considered (typically at the edge of cell) Repeat
Step 1: Find the coverage are of MCS type i, A , i=1 Ai=2, .., Ai=J (refer TABLE I)
, 1 ,
where is coverage radius of MCS type i
Step 2: Find the total number of users supported in coverage area
∑
= × = J i i user A N 1 ρwhere is the user density
Step 3: Find effective capacity per MAR using equation 2 Step 4: Find aggregated capacity requirement for all MARs at first hop: MAR MAR eff hop eff C N C , 1= , ×
Where: NMARis the number of mesh nodes per branch, NMAR= 1-5 nodes in this study
Step 5: Map total MARs capacity requirement to first hop BH link and select BH with maximum distance that support MARs’ capacity requirement. Maximum BH distance is chosen by selecting BH where:
{
CBH Ceff,hop1}
Min ≥
and
BH Distance = 0, if
C
BH<
C
eff,hop1; ∀MCS Types Until meetinglowest MCS, JEnd
Greedy BH Distance Optimisation Algorithm (maximizing multiple hop distance)
Begin 1 ← MAR
N , initial number of MAR or coverage cell.
0 ←
total
d , initial total backhaul distance
1 ←
total
A , initial total coverage size
(
1)
max max 5 hop MAR N d d =← ; //derive max 1st hop BH distance
)
( max
5 Ad
AperMAR
NMAR= ← ; //derive corresponding coverage area size
5 * 5 perMAR N total AMAR A ← = repeat ) ( perMAR N NMAR d A MAR
d ← ; //find corresponding BH distance
//correspond to max coverage
MAR N total total d d d ← + 1 + ← MAR MAR N N
untiltotal number of MAR is fulfilled End
Greedy Coverage Optimisation Algorithm Begin
1 ← MAR
N , initial number of MAR
0 ←
total
d , initial total backhaul distance
1 ←
total
A , initial total coverage size
(
perMAR)
N perMARMAR
A
Amax ←max =5 ; //derive max coverage per MAR, 5
MARs 5 * maxperMAR total A A ← repeat ) ( maxperMAR N d A d
MAR← ; //find corresponding BH distance
//correspond to max coverage
MAR N total total d d d ← + 1 + ← MAR MAR N N
until total number of MARs is fulfilled End
V. RESULTS AND ANALYSIS
A. General Parameters
Table II summarizes the parameters used used in the study. TABLEII.RADIO AND ENVIRONMENTAL PARAMETERS
Parameters Units 40MHz 11n
(Backhaul)
11n 40MHz (Access)
Receive antenna gain dB 15 3
RF frequency GHz 5.8 2.4
Channel bandwidth MHz 40 20
Transmit EIRP power dBm 30 27
Link layer efficiency( ) 0.5 0.5
Propagation Coefficient 2.0 (LOS) 2.6 (rural)
Margins/Loses
Interference dB 0 3
Fading dB 28 8
Wall Penetration Loss dB - 1.609
User Requirements
Overbooking Factor - 10:1
Data Rate Mbps - 1, 2, 5, 10
User Density user/SqKm - 100
The EIRP limits used are based on the guideline given by Malaysian Communications and Multimedia Commission. In this study the effective capacity represents the IP layer throughput where in most cases is approximately 50% of the raw PHY data rate. The overbooking factor is set at 10:1 to represent higher bandwidth applications such as video streaming or FTP type applications. User density is derived from the selected scenario in Fig. 1.
B. Effective Capacity vs. Distance
Fig. 4 shows the ideal effective capacity of a MR’s backhaul and access link versus distance in km when 4 spatial streams are used.
Figure 4. Effective capacity of IEEE802.11n vs. distance
As shown, due to the usage of high gain antenna and maximum EIRP limit, the backhaul link can reach almost 1km and up to 400m for access coverage radius using the most robust (lowest order) MCS. As for the access, this is only true if
0 50 100 150 200 250 300 350 0 0.2 0.4 0.6 0.8 1 1.2 Distance (km ) E ff ec ti ve Ca p acit y ( M b p s)
there are four antennas at the user end which is unlikely to be the case in practise.
C. First HopBackhaul Distance vs. Coverage vs. User Data
Rate with Different Number of MAR per Branch
Fig. 5 shows that with 1-MAR branch case (branch 1), the first hop backhaul link can support up to 0.43sqkm of coverage cell size with the maximum distance between MARs equal to 0.92 km when user data rate is 1 Mbps. When user data rate is increased to 2Mbps, the maximum coverage size 0.43sqkm can no longer be supported at the same backhaul distance. A slightly smaller coverage size per MAR (0.36sqkm) has to be tolerated. When the user data rate is further increased to 5 and 10Mbps, several coverage sizes can no longer be supported. For the 10Mbps case, backhaul distance is reduced to 0.8 km to support coverage size of 0.21 sqkm. Smaller coverage implies no service for some users.
Figure 5. Backhaul distance vs. coverage size vs. user data rate for 1-MAR branch using 1 spatial stream at backhaul link (refer branch 1 in Fig.1) Fig. 6 shows for the 2-MAR branch case (branch 2), similar trend as in 1-MAR case is observed but with noticeable reduction in coverage size and backhaul link distance per user date rate. From the result, we can deduce that 10Mbps can be supported by branch 2 provided the cell size is not more than 0.123sqkm.
Figure 6. Backhaul distance vs. coverage size vs. user data rate for 2-MAR branch using 1 spatial stream at backhaul link. (refer branch 2 in Fig.1)
Fig. 7 shows that the date rates of 1, 2, 5 and 10Mbps can still be supported by branch 3 but with much reduced coverage size.
D. Backhaul Distance/Coverage Maximization
In this section we adopted the backhaul distance (or coverage size) maximization algorithm on the 5-MAR branch (branch 3) and the result is presented in Fig. 8 and 9.
Figure 7. Backhaul distance vs. coverage size vs. data rate for 5-MAR branch using 1 spatial stream at backhaul link (refer branch 3 in Fig.1) Fig. 8 shows that the total (aggregated) coverage size achievable against various data rate using the greedy algorithm. It can be observed that the 5-MAR branch network can support more than 2sqkm of aggregated coverage size with 1Mbps per user. Fig. 9 shows that the maximum multihop backhaul distance (aggregated) remains constant at ~4km for all data rates and number of spatial streams considered. In this case, the total coverage size is being traded off with backhaul distance. It is also clearly shown that higher number of spatial streams enable bigger coverage sizes.
Figure 8. Total coverage size of 5-MAR branch with maximised BH distance Fig. 10 shows coverage size and backhaul distance with coverage size being optimized by the greedy algorithm. Maximum achievable aggregated coverage size for 5-MAR branch is 2.2 square km. For high capacity 4 spatial-stream backhaul, the coverage size can be maintained by increasing the user data rate with no more than one fifth of loss in coverage when providing 10Mbps data rate per user. Capacity limited 1 spatial-stream backhaul performs the worst in maintaining coverage size when user data rate increases. The aggregated
0 1 2 3 1 2 5 10 T ota l Co vera ge f or 5 M A R s (s qkm ) Data Rate (Mbps)
coverage size of 10Mbps is around 70% smaller than coverage size of 1Mbps. As shown in Fig. 11 the total backhaul distance drops gradually when user data rate increases. It is reduced by almost 30% with 10Mbps as compared to 1Mbps user data rate.
Figure 9. Total backhaul distance for 5-MAR branch with maximised BH distance
Figure 10. Total coverage size of 5-MAR branch with maximised access coverage size
Figure 11. Total backhaul distance for 5-MAR branch with maximised access coverage size
Since the backhaul distance optimization algorithm tends to stretch for longer distance by trading off coverage size, less number of users can therefore be supported. This algorithm is more suitable for rural deployment where the number of user is low and distributed. The additional distance provided on backhaul enables a great degree of freedom in terms of MAR placing. On the other hand, the coverage maximization algorithm which ultimately converges towards blanket–type coverage is believed to be more suitable for urban or suburban deployments.
VI. CONCLUSIONS
This paper presents a capacity and coverage analysis of a multiradio multihop network deployment using IEEE802.11n radios for a typical rural area. Insights on the relationships between various key design parameters particularly backhaul link rate, backhaul link distance/multihop distance, coverage size per MAR/total coverage size, number of MIMO spatial multiplex (SM) stream, number of MAR per branch and effective capacity per access point or per user have been acquired. In general, user data rate ranging from 1 to 10Mbps can be supported with a 5-hop multiradio network using IEEE802.11n radios with some tradeoffs in terms of maximum coverage size. We have also introduced two approaches that maximize the coverage size or backhaul distance respectively. As expected, the backhaul distance maximization algorithm is more suited for rural case and coverage maximization on the other hand is more appropriate for urban or suburban deployments.
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http://www.tranzeo.com/allowed/Tranzeo_Link_Budget_Whitepaper.pdf 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 1 2 5 10 Mu lt ih op B ack ha ul D is tan ce (k m ) Data Rate (Mbps)
1 Stream 2 Stream 3 Stream 4 Stream
0 1 2 3 1 2 5 10 To ta l Cov er age fo r 5 M A Rs (s q k m ) Data Rate (Mbps)
1 Stream 2 Stream 3 Stream 4 Stream
0 1 2 3 4 5 1 2 5 10 M ult ih op Ba ckh aul Dist ance (k m ) Data Rate (Mbps)