1
MIMO Mesh Systems
Kristian Cini and Saviour Zammit, Member, IEEE
Abstract— This paper addresses the limited capacity inherent in
WMNs due to the wireless nature of the backbone which is often
not enough to support accumulated traffic to or from mesh
clients in dense application scenarios. A possible key solution is
the utilization of MIMO communication links where spectral
efficiency enhancement at no additional cost of power or
spectrum is possible. MIMO-based WMNs were analyzed using
extensive ns-2 simulations where the significant limitations of
the ns-2 tool were addressed to setup a framework over which
simulations of MIMO WMNs based on IEEE 802.11a/g WLAN
technology are supported. Using extensive simulations, MIMObased WMNs were shown to improve the end-to-end
throughput, delay and jitter performance using SM and STBC
techniques for short and longer link distances respectively.
Finally, the additional degrees of freedom introduced by MIMO
in the context of network scalability were also addressed using a
real-case scenario. It was shown that using STBC, coverage is
maximized while servicing a limited number of users using a
small number of mesh routers. On the other hand when the
number of users requesting service increases, it is shown that
scaling the network using SM techniques maximizes the number
of users serviced with a particular network topology.
Index Terms— MIMO, Network Capacity, WMNs, Ns-2.
I. INTRODUCTION
M
ultiple-input multiple-output (MIMO) technology has
emerged as the most significant breakthrough in modern
wireless communications. This technology is capable of
providing spectral efficiency enhancement in multi-path
fading environments at no additional cost of power or
spectrum. A more reliable communication can be achieved
where bit error rate is reduced by diversity gain using well
known techniques such as Space Time Block Coding (STBC).
A different line of thought is to exploit multipath to achieve
multiplexing gain where it was shown that if the path gains
between each antenna element fade independently, then
multiple spatial ‘data pipes’ can be utilized to transmit data
in parallel thus increasing the data rate linearly with the
number of antennas [1]. Due to these attractive features this
technology is highly considered to be a viable solution for
resolving the bottleneck of traffic capacity in present and
K. Cini and S. Zammit are with the Communications and Computer
Engineering Department, University of Malta, Imsida, Malta. (e-mail:
kcin0001@um.edu.mt, saviour.zammit@um.edu.mt).
future Internet intensive wireless networks. The growth of
WLANs in the wireless technology market was
surprisingly high stimulating researchers to study the
possibility of extending the coverage of WLANs from hot
spots to hot zones. In particular, wireless mesh networks
(WMNs) have emerged in recent years as a major candidate
to satisfy such requests. This type of network is a promising
technology for numerous applications due to its selforganization and configuration with nodes establishing and
maintaining mesh connectivity amongst them offering rich
connectivity and reliable coverage.
Despite recent advancement in WMNs, many research
issues in all layers of the protocol stack are still to be resolved
before this type of technology reaches its full potential. In
particular, the limited network capacity is often not enough to
support accumulated traffic to or from mesh clients in dense
application scenarios. A possible key solution for such an
issue is boosting capacity by utilizing MIMO communication
links. This work addresses these considerations while
concentrating on the following aspects:
(1) The setting up of a framework over which realistic
simulations of WMNs based on IEEE 802.11
technology can be run and provide the capability of
supporting distinct MIMO techniques.
(2) Perform a comprehensive performance study that
shows throughput comparisons of MIMO WMNs
with respect to multi-channel multi-radio WMNs.
(3) Investigate the advantages of MIMO WMNs in the
context of network scalability.
The rest of the paper is organized as follows. Some related
work is presented in section 2 while the simulation
framework is described in Section 3. The simulation results
comparing multi-channel WMNs with MIMO-based WMNs
are presented in Section 4 followed by the respective analysis
in Section 5. Finally in Section 6 we conclude our study.
II. RELATED WORK
The work presented in [2] proposes a novel MIMO mesh
network based on SM techniques combined with interference
cancellation. Numerical simulations were conducted to
2
compute the channel capacity and validate the proposed
MIMO mesh network with a maximum of three antennas per
node. It was found that the performance of the link-by-link
MIMO network is almost triple of SISO due whilst the
proposed MIMO mesh network nearly improved the
performance by a factor of four which originates from the
additional interference cancellation gain. A theoretical
analysis on the per-node capacity of link-by-link MIMO mesh
networks was presented in [3] for both chain and grid
topologies. Through simulations conducted using the derived
analytical work, as expected the network capacity increases
with number of antennas per node while the transmission
configuration affects the rate of increasing capacity with
SNR. Research on applying MIMO on WMNs is still in its
infancy and results are still limited. The work presented in [4]
resembles mostly to the work conducted in this paper where
Wang et al. present a prototype of a planned WMN using a
(4x4) grid mesh topology based on typical IEEE 802.11 b/g
technology. The results obtained indicate significant
throughput improvement where the average throughput
improved by more than 80 per cent and nearly 100 per cent
for two and four hop transmissions respectively. Furthermore,
the delay performance is reduced by approximately 50 to 70
per cent while it is shown that the jitter performance is also
improved. The work proposed in this paper outstands from
the previous since it utilizes real channel error performance
instead of using the typical i.i.d. Rayleigh fading channel.
III. NS-2 SIMULATION FRAMEWORK
A. Multi-Channel Multi-Radio WMNs
Traditional WMNs were based on single-channel singleradio interface nodes which face severe limitations regarding
network capacity. One approach to improve the network
capacity of a WMN would intuitively be to use multiple radio
interfaces. Recently the development of multi-radio WMNs
has accelerated due to inexpensive and off-the-shelf IEEE
802.11-based wireless interfaces. Therefore due to these
considerations, ns-2 was extended with the aid of the
Hyacinth project [6] to support multiple-interfaces tuned to
different non-overlapping channels. Moreover, since the
typical ad hoc routing protocols supported by ns-2 were
designed for single-channel single-interface nodes, the
simulator was extended using a manual routing protocol
avoiding inadequate routing protocol operation and excessive
overhead.
B. Channel Modeling
According to the current implementation, ns-2 uses the
WirelessPhy class primarily to calculate the receiving power
Pr, through typical path loss models such as the two-ray
channel model. It then compares the received power with the
carrier sense threshold (CSTresh_) and if it is less than the
latter, it discards the packet since it cannot be sensed. On the
other hand, a packet is sensed and possibly received without
error if Pr is higher than the receiver threshold sensitivity
(RXThresh_). This channel modeling approach attempts to
approximate the statistical behavior of the PHY layer at a low
run-time computational cost. However such an approach does
not faithfully capture the characteristics of a specific
transmission or reception at the PHY layer. Therefore, there
has been a need to extend ns-2 to simulate more faithfully the
packet error probabilities for specific channels and
technologies.
For the purpose of this study it was opted to extend the
Mac-802_11 class in order to take into consideration the
actual packet error rate of the channel. In this manner, when
the WirelessPhy class acknowledges reception of a packet
with the received power larger than RXThresh_, the Mac802_11 class takes into consideration the error probability of
the channel. Thus a ChannelModel descendant class was
introduced which simulates packet error events. The Signal to
Interference plus Noise Ratio (SINR) parameter together with
the rate at which the packet was received, are passed as
arguments to a function of this class for every packet
received. Since the IEEE 802.11a standard uses eight modes
in total, ranging from 6 Mbps to 54 Mbps regarding the
nominal physical data rate, then the datarate_ parameter,
which controls the transmission rate of packets, is used to
check in which one of the eight modes the packet was sent.
Obviously the higher the mode, the higher the modulation
used and thus the higher the probability of error. A table is
used to hold the packet error rate values at each SINR for
each of the eight modes. Thus, the SINR and the transmission
mode calculated in the function are used as vertical and
horizontal reference points on the table to derive the packet
error rate of the channel. As explained in the next section, a
separate error probability table was obtained from the already
published work to describe a typical SISO WLAN link and a
WLAN link using MIMO technology.
TABLE I
MIMO MODES WITH THE RESPECTIVE PHY LAYER DATA RATES
C. Performance Plots
The most promising WMNs application scenarios are
neighborhood and community networking together with
metropolitan area networks. Therefore, research on the
typical channel conditions present for such popular urban
scenarios was conducted. For instance in [7] the authors
obtain statistical measurements for the r.m.s. delay spread in
Austin urban environment where the resulting r.m.s. delay
spread was 173ns with the maximum delay spread reached
approximately 550ns. Therefore the error performance plots
in similar channel conditions for IEEE 802.11a WLAN and
MIMO over the same WLAN technology were found from the
available published work. These error performance plots are
3
shown below using Figures 1 and 2 and were used to build the
error performance tables aforementioned.
Fig. 1. PER for IEEE 802.11a in NLOS propagation environment.
to represent the majority of channel scenarios, the ergodic
capacity increases by a factor of 3.75 in the high SNR regime
with respect to the SISO case. Thus the resulting average data
rates at the physical layer for modes 5 and 7 are taken to be
45 Mbps and 90 Mbps respectively.
D. Rate Adaptation
All PHY layer standards of IEEE 802.11 WLAN provide
multiple transmission modes. To achieve a high performance,
these devices need to be capable of dynamically selecting the
best mode according to determined channel conditions. Due
to lack in implementation complexity and suitability to the
demands of this study, the RBAR protocol [13] was chosen as
the rate adaptation mechanism. In particular, the RBAR
protocol allows easily the manual setting of SNR thresholds
by which the MAC dynamically chooses the ideal mode. The
operation of RBAR is based upon the receiver choosing the
best transmission mode for the transmitter during the
RTS/CTS packet exchange. The receiver of the RTS packet
selects which transmission mode to be used based on the SNR
of the upcoming RTS packet and on a set of SNR thresholds
which are calculated on a priori wireless channel model.
Therefore, ns-2 was extended to support RBAR by simply
modifying some functions of the mac802_11 class.
IV. SIMULATION RESULTS
A. Multiple – Flow Simulation
Fig. 2. MIMO over IEEE 802.11a in NLOS propagation environments.
The Packet Error Rate (PER) with SNR for all eight
transmission modes of the IEEE 802.11a WLAN were
obtained from the work in [8] and are shown in Figure 1.
Furthermore, the PER performance for different MIMO
transmission modes were obtained from the work reported in
[9] and [10] and are shown in Figure 2 and summarized in
Table 1. Both these set of data account for a propagation
environment having an r.m.s. delay spread of around 250ns
while also characterized by Rayleigh fading. The actual data
rate achieved by modes 3 and 4 in Table 1 which use MIMO
SM technique needs further elaboration. One should note that
the rates reported in Table 1 are nominal and are achieved in
unrealistic channel conditions where a rich scattering
environment is present having negligible spatial correlation.
In reality, as well reported in [11] and [12], the ergodic
capacity enhancement achieved when using a (4x4) MIMO
system is less than 4. Thus using the work reported in [11]
and assuming an average correlation of 0.5 being appropriate
In this part of the paper, the performance of a typical (3x3)
grid WMN will be used to support multiple flows and
investigate MIMO performance. Since interference is the
major cause for performance degradation in the WMN
backbone, a simple fixed channel assignment will be first
presented. The twelve orthogonal channels provided by the
IEEE 802.11a standard were used to reduce the interference
present and create more ‘parallel’ flows. This channel
assignment was then used to investigate the performance
impact of MIMO technology on multi-radio WMNs using
multiple flows.
Fig. 3. Static Channel Assignment
A simple static channel assignment was utilized to study
how MIMO impacts the performance of WMNs when subject
4
to multiple flows interacting in a network characterized by
limited interference and channel contention. The static
channel assignment utilized is shown in the Figure 3. The
assignment was done in such a way that clients connected to
mesh routers 0, 2, 6 and 8 and wishing to communicate with
the gateway (node 4), need to have packets relayed through
mesh routers 1, 5, 3 and 7 respectively using a full duplex
communication. Since the bottleneck of network capacity is
very likely to consist of links nearer to the gateway, then
nodes making up these links are provided more bandwidth
using multiple interfaces. Obviously having a dynamic
channel assignment algorithm together with adequate routing
and load balancing protocols would be ideal for such
scenarios but however this is not the scope of this study.
TABLE 2
PERCENTAGE GAINS PER FLOW
The network configuration shown was used to support ten
traffic UDP flows to simulate the performance of the grid
WMN using both the typical SISO system and the MIMO
system. The data rate for each flow was limited to 4 Mbps
whereas simulation time was set to 10s to get better accuracy
on results. Since the majority of the traffic is expected to be
skewed around the gateway, then nine of the ten UDP flows
were set to involve the gateway. The end-to-end average
throughput, end-to-end average delay and average jitter were
measured and are presented in Table 2. These measurements
were taken for two different mesh router separation distances
which were set at 100m and 200m. Note that besides SISO
and MIMO system using RBAR protocol, the best two MIMO
performing modes were also included. Most importantly,
Table 2 shows the percentage gain improvement per flow.
Utilizing the best two performing MIMO modes, the 100m
case SISO was compared with MIMO 45 Mbps whereas the
200m case SISO was compared with MIMO 24 Mbps to
obtain the percentage gain improvement in both cases.
Fig. 4. WMN Topologies with the respective channel assignments.
B. Realistic-Case Scenario
The results of a realistic-case scenario are presented in this
section to outline the high efficiency introduced when using
MIMO technology with WMNs. In particular, it is shown that
the scalability of the network is aided by the STBC and SM
techniques available when using MIMO transmission modes
in comparison with the SISO modes. To prove this point, a
simple realistic scenario was assumed by considering an
urban area, 300m x 300m large, providing broadband support
to homogenously spaced users. The area was first assumed to
contain a low number of clients requesting service, which is
often the case when a new operating service is offered. For
this purpose the number of users was initially set to six and
later increased to 15 users. For both scenarios, the advantage
of using MIMO with respect to SISO was established by
measuring the end-to-end throughput provided for each user.
The service offered to each user was assumed to be a UDP
application streaming CBR traffic at 4 Mbps in the downlink,
from the gateway to each of the serviced users. To keep the
study simple while providing equal support to users in any
location of the area, grid WMNs were considered which are
ideal for providing support to square-like areas. Therefore,
the topologies used are shown in Figure 4 which consist of
two and four mesh routers respectively. In both cases node 0
is the gateway while mesh connectivity is provided using the
available non-overlapping channels.
Fig. 5. Performance for SISO using the two different topologies
Fig. 6. Performance for MIMO using the (2x1) topology
5
For a lightly loaded WMN i.e. having only 6 users
requesting service, it was found that the (2x2) topology was
needed to provide adequate support for the SISO WMN. This
is shown in the Figure 5 where the performance for SISO
with the (2x1) topology was compared with the (2x2) grid
topology by means of end-to-end throughput measurements
for each of the six user flows. The same scenario was then
investigated for a WMN using MIMO technology and as
shown in Figure 6, all six users are provided with full 4 Mbps
support using only the (2x1) topology in comparison with the
(2x2) topology necessitated by the SISO WMN. Furthermore,
the behavior of three MIMO modes using this topology was
investigated when supporting six users spaced in a larger
area. The six users were spaced in a similar area having sides
400m long. Thus each router had to support larger link
distances in the backbone and possibly even with clients. The
resulting behavior for MIMO 18 Mbps and 24 Mbps modes
using STBC, and 45 Mbps mode using SM is shown in
Figure 7. The STBC modes with the 18 Mbps mode in
particular, were found to perform better in larger coverage
areas supporting fully the six users.
Fig. 7. Performance for MIMO modes in larger coverage
area
Fig. 8. MIMO performance with the (2x2) topology
The same aforementioned area was again considered but
increasing the number of clients requesting service to 15. The
(2x2) topology was found to be sufficient to support to all 15
users in the MIMO case. The throughput measurements
obtained using such a topology are shown in Figure 8 for
MIMO using RBAR and the lowest three fixed rate modes. In
particular it was found that the MIMO 45 Mbps mode which
uses SM is capable of providing support to all 15 users at 4
Mbps. For the SISO case, scaling the network using a (3x3)
grid WMN similar to Figure 3 proved to be still insufficient to
support this traffic load. However unlike the topologies in
Figure 4, the (3x3) WMN had some links contending the
same channel and thus the RTS/CTS is typically disabled to
appropriately measure the performance in such a topology.
Since the RBAR protocol operates using this mechanism,
disabling it would also disable this protocol and thus
obtaining the performance for SISO would be inappropriate
since link adaptation is always used. However intuitively, to
provide support for the 15 clients and covering the area
uniformly, a (3x3) grid WMN is at least required which
would imply higher network planning.
V. ANALYSIS OF RESULTS
A. MIMO with Multiple Flows
Table 2 presents the percentage gain improvement of the
best MIMO mode in comparison to SISO. For both scenarios,
the performance was clearly enhanced when using MIMO
technology. In particular it was noted that MIMO 45 Mbps
achieves the best performance at a distance of 100m whereas
MIMO 24 Mbps achieves the highest throughputs in the
200m case. Intuitively, this occurs since SM modes obtain the
best performance when SNR is relatively high like in the
100m case. On the other hand when channel conditions
deteriorate, STBC modes such as MIMO 24 Mbps achieve the
best performance since they are capable of providing
relatively high throughputs in non-ideal conditions.
Using Table 2 again, one can note that the highest gain in
throughput was achieved on flows 1 and 3. For the SISO case,
flows 1 and 3 are starved by other flows contending the same
channels due to the known unfairness issues of the IEEE
802.11 MAC which often favors the shortest paths in multihop networks. By means of MIMO, these flows are supplied
with higher support achieving very large gains. Finally note
that for an AP-AP distance of 100m, the network was capable
of providing 4 Mbps support to all of the 10 flows using
MIMO whereas for the 200m case, few flows were provided
with this support. This occurs since at these distances, STBC
modes are only the only practically but do not have enough
capacity to support this much traffic. The only flow which
was not enhanced is flow 7 which is also the only flow fully
supported by 4 Mbps with SISO WMN. Again from Table 2,
a consistent gain is noted ranging approximately between 50
and 90 per cent for the 100m case and between 20 and 65 per
cent for the 200m case. It is evident that when the channel
conditions permit such as in the 100m case, the MIMO 45
Mbps mode obtains better delay gain performance than the
MIMO 24 Mbps mode owing to a significantly higher data
rate available. Similarly to the gain in delay, a consistent
improvement in jitter was achieved between 40 and 75
6
percent for the 100m case and between 20 and 65 per cent for
the 200m case owing to the same aforementioned reasons.
B. Scalability of WMNs
It was found that for the 6 user scenario, the SISO network
needed 4 mesh router arranged in a (2x2) topology. A simple
WMN using 2 MIMO mesh routers was found to be enough
to support all users at 4 Mbps. For the SISO network, high
degradation in throughput was measured with only 2 routers
since each router needs to provide a maximum of 150m of
coverage, causing the SNR to lower with an inherent increase
in channel errors. With MIMO 18 Mbps and 24 Mbps STBC
modes, full support is provided to the 6 users whereas a slight
degradation in throughput was measured with the MIMO 45
Mbps mode. An important consideration is that even a range
longer than 150m can be supported using these STBC modes
which make use of diversity gain to combat fading in the low
SNR regime.
The possibility that broadband service requests increase was
then investigated by assuming an increase from 6 to 15 users
in the same area. It was also found that severe performance
degradation is present when using the same topologies
servicing 15 users. It is of particular interest to note that if
one uses the same topologies, 12 out of 15 users are serviced
with a higher throughput using SISO than using the MIMO
WMN since the SISO WMN uses 4 mesh routers
communicating with multiple channels in full-duplex and
thus dividing the load approximately by the four whilst
servicing a shorter coverage range. On the other hand for the
MIMO WMN with 2 routers, the load on each router is too
high resulting in severe performance degradation. To provide
adequate support, the network has to be scaled up by dividing
the load amongst a higher number of mesh routers. In fact it
was found that using the (2x2) topology is enough to service
15 users using MIMO. Most importantly it was found that the
MIMO 45 Mbps mode using SM, was the only mode which is
capable of supporting all 15 users. This is because the STBC
modes do not offer enough capacity to support the total
requested load. In fact, one can easily note that the 18 Mbps
and 24 Mbps STBC modes are capable of supporting a
maximum of 12 users due to the limited data rate. Therefore,
this highlights another fundamental point regarding network
scalability using MIMO. The network can be scaled gradually
by increasing the number of mesh routers causing the
coverage range per mesh router to reduce. This reduction in
coverage brings about an improvement in SNR which allows
the system to utilize higher MIMO SM modes providing
higher data rates and maximizing the number of users
serviced.
This model consisted in using published error performance
plots to simulate MIMO characteristic features efficiently and
realistically in selected outdoor macrocell environments.
By means of extensive simulations, considerable end-to-end
throughput enhancement, and a reduction in average delay
and jitter was measured when using MIMO WMNs. Using
such a technology appropriately, higher service support can
be provided to clients with increased throughput while
meeting more efficiently Qos requirements demanded by
today’s applications. Furthermore, it was noted that for
moderate mesh router link distances, the SM MIMO modes
perform best whilst for longer distance links, STBC MIMO
modes achieve the best performance due to the availability of
diversity gain. Finally a simple realistic scenario was
presented which highlighted how MIMO SM and STBC
techniques aid in the scalability of WMNs. It was highlighted
that when WMNs are lightly loaded, the STBC modes can be
utilized to maximize coverage using a minimal number of
mesh routers. On the other hand given a number of mesh
routers, the SM modes can be utilized to maximize the
number of users serviced.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
VI. CONCLUSION
In the reported work, a framework was implemented which
allowed a comprehensive analysis on the potential impact of
MIMO applied to WMNs. The widely used network simulator
ns-2 was extended to accommodate MIMO characteristic
features by the introduction of a simple channel error model.
[13]
G. J. Foschini and M. J. Gans, “On limits of wireless communications in a
fading environment when using multiple antennas”, Wireless Personal
Communications, vol. 6, no. 3, pp. 311-335, Mar. 1998.
W. Jaafar, W. Ajib, S. Tabbane, “The Capacity of MIMO-based Wireless
Mesh Networks”, in Proceedings of the 15th IEEE International Conference
on Networks, 2007, pp. 259 – 264.
F. Ono, K. Sakaguchi, “Bi-directional link multiplexing for MIMO mesh
network”, in Proceedings of the 6th International Conference on Information,
Communications & Signal Processing, 2007, pp. 10 -13.
D. Wang, S. Yang, Y. Hu, D. Teng, “9Square: A New Architecture for MIMO
WMN”, in Proceedings of the International Conference on Wireless
Communications, Networking and Mobile Computing, 2006, pp. 22 - 24.
NS-2 Official WebSite, http://www.isi.edu/nsnam/ns, last accessed on 25-0408.
A. Raniwala and T. cker Chiueh, “Architecture and algorithms for an IEEE
802.11-based multi-channel wireless mesh network”, in Proceedings of the
24th Annual Joint Conference of IEEE Computer and Communications
Societies, 2005, pp. 2223 - 2234.
C. Liang, K. R. Dandekar, “Modeling MIMO-OFDM ad-hoc communication
systems with computational electromagnetics”, in Proceedings of the IEEE
60th Vehicular Technology Conference, 2004, pp. 4340 – 4344.
A. Doufexi, S. Armour, M. Butler, A. Nix, D. Bull, J. McGeehan, P. Karlsson,
“A Comparison of the HIPERLAN/2 and IEEE 802.11a Wireless LAN
Standards”, IEEE Communications Magazine, vol. 40, issue 5, pp. 172 - 180,
May 2002.
J. Coon, J. Siew, M. Beach, A. Nix, S. Armour, J. McGeehan, “A Comparison
of MIMO-OFDM and MIMO-SCFDE in WLAN Environments”, IEEE
Global Telecommunications Conference, vol. 3, pp. 3296 – 3301, Dec. 2003.
K. H. Ng, E. K. Tameh, A. Doufexi, M. Hunukumbure, A. R. Nix, “Efficient
Multielement Ray Tracing With Site-Specific Comparisons Using Measured
MIMO Channel Data”, IEEE Transactions on Vehicular Technology, vol. 56,
Issue 3, pp. 1019 – 1032, May 2007.
M. Kiessling, “Unifying Analysis of Ergodic MIMO Capacity in Correlated
Rayleigh Fading Environments”, European Wireless (EW) Conference Num
5, vol. 16, num. 1, pp. 17 – 35, Feb. 2004.
X. Chengshan, Y.R. Zheng, “Ergodic Capacity, Capacity Distribution and
Outage Capacity of MIMO Time-Varying and Frequency-Selective Rayleigh
Fading Channels”, IEEE Global Telecommunications Conference, vol. 5, pp.
3133 – 3137, Dec. 2004.
G. Holland, N. Vaidya, P. Bahl, “A Rate-Adaptive MAC Protocol for MultiHop Wireless Networks”, in Proceedings of the 7th annual international
conference on Mobile computing and networking, 2001, pp. 236 - 251.