1) The document analyzes the effect of multi-user MIMO (MU-MIMO) systems on channel capacity in crowded urban areas compared to single-user MIMO (SU-MIMO) systems. 2) It finds that MU-MIMO can achieve higher channel capacity than SU-MIMO in urban environments due to lower spatial correlation between users, unlike assumptions of independent channels in MIMO transmission. 3) The improvement of MU-MIMO capacity over SU-MIMO increases with path visibility, with MU-MIMO showing better performance when path visibility is over 13%.

1. www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 41
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Effect on Channel Capacity of Multi-User
MIMO System in Crowded Area
Vinay Thakur1
, Surinder Kumar Rana2
, Abhishek Thakur3
1,2
Electronics & Communication Department, Sri Sai University, India
3
Electronics & Communication Department, Indo Global College of Engineering, Punjab, India
1
Vinay.rajput1@gmail.com, 2
Sindu.97@gmail.com
I. INTRODUCTION
Multiple-Input Multiple-Output (MIMO) and Multi-User
MIMO (MU-MIMO) systems have been expected to
improve the channel capacity over a limited bandwidth of
existing networks [1], [2]. The effects on channel capacity of
Single-User MIMO (SU-MIMO) systems in urban scenarios
have been previously studied [3]. It has been clarified that
the larger number of antennas cannot contribute the
improvement on the channel capacity in urban SU-MIMO
scenarios due to very high spatial correlation. MIMO is also
called by some people my moh and me moh by other people,
for the better communication we mostly use multiple
antennas at receiver and transmission end. In the latest
technology there are several forms of the antennas. In this
paper, we focus on the MU-MIMO transmission because it
can discriminate multiple users by the difference of Angle of
Arrival (AoA). We compare the Multi Access Channel
(MAC) capacity in uplink with the channel capacity in SU-
MIMO by setting the total numbers of transmitting and
receiving antennas of SU-MIMO and MU-MIMO to be the
same. Multiple input and multiple output technique has call
the notice in wireless communications, because it gives a
hike in data output and range without any need of any other
external power and any change in bandwidth. It attains this
target by giving the same total transmitting power over the
antennas to achieve the spectral efficiency and to attain a
gain that improves the reliability by reducing the fading
effect. When the same numbers of antenna elements are
used, the better performance is obtained with MU-MIMO in
urban scenarios, unlike identical independent distributed
(i.i.d.) channels which are generally assumed in MIMO
transmission. We also clarify an interesting relationship
between the channel capacity improvement of MU-MIMO
compared with SU-MIMO and a path visibility.
A. Antenna and User Models
The antennas and the user are simulated through fullwave
EM simulations that are performed with a three dimensional
(3D) solver, FEKO [12]. The MIMO handset has two classic
single-band PIFAs designed co-polarized to each other and
both resonate at 2.6 GHz. We consider three usage scenarios:
i) Head only (H), ii) voice scenario with the user head and
hand (HH); and iii) data scenario (D) with the user’s two
hands. The examined usage scenarios are
shown in Fig. 1(a)-(c) where the phantom head and the hand
models are used to simulate the user.
B. Antenna Efficiency
An important factor in characterizing antennas is the
radiation pattern and hence, gain and efficiency of the
antenna. The antenna patterns and efficiency definitions are
not obvious and cannot be directly derived from
conventional pattern descriptions when the antenna is placed
in the vicinity of or on a lossy medium. This is due to losses
in the medium that cause waves in the far-field to attenuate
more quickly and finally to zero. The antenna efficiency is
proportional to its gain [11] (,) (,) GDθφ =η⋅ θφ. (2) In (2)
ηis the total efficiency factor and D(,) θφ is the antenna
directivity, which is obtained from the antenna normalized
power pattern that is observed in the far-field. An antenna
within a handset, for example, and/or in the vicinity of a user
would have different efficiency from an antenna in free
space due to changes in the far-field radiation pattern. Fig. 2
shows the total far-field pattern of the antenna in the
different usage scenarios described in Fig. 1. The difference
in the patterns among the different scenarios is obvious.
These differences arise from the change in the electric field
distributions at varying distances from the body or any other
obstacles in the communications channel.
II. ANALYSIS MODEL
The urban propagation model employed in this paper is
represented in Fig. 1. This model is composed of 64 blocks
of 50m×50m. Each block is composed of 4 buildings. The
road width is 20m. The buildings are assumed to be
constructed of concrete and the relative
dielectric constant and conductivity are set to 5 and 0.01S/m,
respectively. The uplink scenario of (M1+M2)×NMU-
MIMO systems (from MT to BS) are considered. The
characters M1, M2, and N respectively represent the
numbers of antenna elements of the first MT, the second

2. MT, and the BS. It is noted that M1+M2is supposed to be
not greater than N. A linear-array BS is located at the top of
a building on one side of the model as shown in Fig. 1. Since
an accurate reflection or diffraction cannot be obtained at the
edge of analysis model, the MTs are assumed to move
independently on the road in an area of 280m×280m around
the center of the model along the broken lines in Fig. 1 at the
height of 1.5m. The MT antennas are set in a symmetrical
array at half-wavelength (λ/2) spacing. The propagation
characteristics between MT and BS are then calculated by
using the ray-tracing method. The distribution of the height
of buildings is assumed following chi-squared
distribution:χ2(k), with kdegrees of freedom (DoF) which is
herein set to 5. The minimum height of these buildings is set
to 4m. The height of building:h, can be expressed as [4]
() 4, =+ hkχ
The carrier frequency is 3GHz. The numbers of reflection
and diffraction are 30 and 2, respectively. The channel
response matrices are obtained from the complex received
voltage matrices which are calculated at intervals of 14m in
length along the broken lines in Fig. 1. The uplink scenario
is considered. It is assumed that the Channel State
Information (CSI) between the transmitter and receiver is not
known by the MT. Whenthe transmitter does not know the
CSI, the channel capacity of SU-MIMO can be obtained in
the units of bps/Hz as [1]
In cases of MU-MIMO, the analysis of channel is commonly
referred to the MAC [5]. The MAC capacity (CMAC) is
considered as the total channel capacity which the BS
antenna can receive from the MTs moving in the propagation
area. In MAC channel, the BS can estimate all the CSI from
the MTs. In cases of 2-user MIMO systems, this CMACcan
be obtained by a substitution of the combined CSI (HMAC)
shown in Fig. 2 into (3).
III. FUNCTION OF MIMO
Three main categories of MIMO, Precoding ,Spatial
multiplexing and Diversity coding. Precoding is multistream
beam forming and considered to be all spatial processing. In
single stream beam signal is transmitted with appropriate
gain, phase and maximized power at receiver. Its advantages
are to increase received signal gain with all signals get add
up from different antennas & reduce multipath fading. In
Line of sight, beam formed is directional but conventional
beam are not good analogy in cellular network ,with multiple
antenna, the transmitting beam formed cannot maximized
signal level at receiving antenna. So precoding is used and
requires channel state information (CSI) at transmitter and
receiver. In spatial multiplexing, splits high rate signal
stream into multiple lower rate signals ,each signal stream is
transmitted from different transmitting antennas at same
frequency channel and required MIMO antenna
configuration. If these signals arrive at the receiver antenna
array with sufficiently different spatial signatures and the
receiver has accurate CSI, it can separate these streams into
(almost) parallel channels. It increase channel capacity at
higher signal-to-noise ratios (SNR) and maximum number of
spatial streams is limited by less number of antennas at the
transmitter or receiver. It can be used without CSI at the
transmitter, but can be combined with precoding if CSI is
available. It can also be used for simultaneous transmission
to multiple receivers, known as space-division multiple
access or multi-user MIMO, in which case CSI is required at
the transmitter.
Channel Capacity Characteristics of Urban MU-MIMO
Systems
The channel capacity of urban SU-MIMO has been
evaluated [3]. It has been clarified that the channel capacity
of SU-MIMO is deteriorated compared with the i.i.d. cases
due to a very high spatial correlation in urban propagation
environment. Hence, to reduce the effect of the spatial
correlation, the MU-MIMO transmission is introduced.
Figure 3 shows the effects of model configurations on the
channel capacity of (2+2)×4 MU-MIMO compared with 4×4
SU-MIMO. The results present significance, since there are
situations that CMAC> CSU, i.e. the MU-MIMO
transmission presents effectiveness. These results confirm
that the channel capacity characteristics of MU-MIMO are
greatly different from those in neither indoor nor i.i.d.
scenarios [6]. These are supported by Fig. 4. The average
spatial correlation between users of (2+2)×4 MU-MIMO
which two MTs moving independently in the propagation
area is much lower than the average spatial correlation
between each antenna element of 4×4 SU-MIMO which all
MT antenna elements always stay closely. Since the spatial
correlation becomes low, its effect on the channel capacity is
also deteriorated.

3. www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 43
From the view of receiving antenna (BS), the AoA-diff. is
definedas the difference of angle which the signal from each
MT arrives at BS.Its effect on the channel capacity is
indicated. Figure 5 shows the CMACand CSUat each
AoAdiff. In cases of MU-MIMO, when the AoA-diff is
increased or the MTs stay farther from each other, the
channel capacity is relatively increased. Even if the BS is
low mounted (50m) which MU-MIMO transmission is not
much more effective than SU-MIMO (see Fig. 3), the
channel capacity is also increased when two MTs are far
apart which the correlation becomes low. Moreover, when
the MTs stay at very near locations, or the AoA-diff is small,
(2+2)×4-MU-MIMO channels can be approximately
regarded as 4×4 SU-MIMO, and the channel capacity
becomes low due to high correlation.
Figure 6 shows the channel capacity improvement of MU-
MIMO over SU-MIMO. The curves show the ratio between
CMAC and CSU. The intersections between these curves
and the horizontal dashed line indicate the turning points
which CMAC becomes greater than CSU(CMAC/CSU> 1).
As the average building height is higher, the turning points
relatively present at a higher BS antenna height. For a clear
discussion, the path visibility defined as the probability that
the direct wave can be received at the receiving antenna or
Line-o Sight (LoS) exists [3], is considered. Figure 7 shows
the effect of the path visibility on the characteristics of
CMAC/CSU. As the results, along the increment of the path
visibility, the ratio between CMAC/CSUis relatively
increased, because in urban propagation scenario which the
spatial correlation is very high, the independent movements
of users in MUMIMO can reduce the spatial correlation.
That is the reason why the MU-MIMO transmission can
present the effectiveness while the SU-MIMO cannot.
Furthermore, considering the fitting curve in Fig. 7, it is
clarified that CMAC becomes greater than CSU, when the
path visibility is about 13 percent. That is to say, to obtain an
effectiveness of urban wireless communication, not only the
MU-MIMO transmission is supposed to be employed, but
also the BS antenna should be mounted at the height so as
the path visibility is greater than 13 percent. This result will
be useful when considering the installation of the BS in
urban SU/MU-MIMO systems.
IV. CONCLUSION
Throughout this paper, the channel capacity characteristics
of urban SU-MIMO and MUMIMO considering the uplink
scenario were studied. The MU-MIMO transmission was
introduced to reduce the spatial correlation. The MAC
capacity in 2-user 2×4 ((2+2)×4) MU-MIMO was compared
with the channel capacity in 4×4 SU-MIMO. It was clarified
that the spatial correlation between users of MU-MIMO
which two MTs moving independently in the propagation
area was much lower than that of SU-MIMO which all MT
antenna elements stayed closely all the times. Its effect on
the channel capacity was consequently deteriorated. By the
definition of AoA-diff, it was shown that when the MTs
stayed farther from each other which the spatial correlation
became low, the channel capacity was increased. Moreover,
when the AoA-diff was small or the MTs stayed at very near
locations, (2+2)×4 MU-MIMO channels could be
approximately
regarded as 4×4 SU-MIMO. Finally, it was shown that the
channel capacity improvement of MU-MIMO over SU-
MIMO was relatively increased along with the increment of
the path visibility.

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