Prediction of wireless communication systems in the context of modeling 2-3-4

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
INTERNATIONAL JOURNAL OF ELECTRONICS AND
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 1, January- February (2013), © IAEME
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 1, January- February (2013), pp. 11-17
IJECET
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2012): 3.5930 (Calculated by GISI)
©IAEME
www.jifactor.com

PREDICTION OF WIRELESS COMMUNICATION SYSTEMS IN THE
CONTEXT OF MODELING

T.Regua, Dr.G.Kalivarathanb
a
Research Scholar, CMJ University, Meghalaya, Shillong.
b
Principal/ PSN Institute of Technology and Science, Tirunelveli, Tamilnadu, Supervisor,
CMJ University, Shillong. Email:sakthi_eswar@yahoo.com

ABSTRACT

This paper is focused with the use of numerous antenna elements in wireless
communication over frequency non-selective radio channels. Both experimental results and
theoretical analysis are discussed with definite details. New transmit strategies are derived
and balanced to existing transmit strategies, such as beamforming and space time block
coding (STBC). It is seen that the best transmission algorithm is principally dependent on the
channel characteristics, such as the number of transmit and receive antennas and the
continuation of a line of sight component. Rayleigh desertion multiple input multiple output
(MIMO) channels are studied using an eigenvalue analysis and faithful expressions for the bit
error charge and outage capacities for beamforming and STBC is found. In general MIMO
fading channels are correlated and there exists a mutual coupling between antenna elements.
These investigations are supported by indoor MIMO measurements. It is seen that the mutual
coupling can, in some scenarios, increase the outage capacity. An adaptive antenna testbed is
used to obtain measurement results for the SIMO channel. The results are analyzed and
design oriented guidelines are obtained for how a beamformer executed in hardware shall be
constructed. The property of nonlinear transmit amplifiers in array antennas are also
analyzed, and it is seen that an array condenses the effective intermodulation distortion
(IMD) transmitted by the array antenna by a spatial filtering of the IMD. A novel frequency
allocation algorithm is proposed that reduces IMD even additional. The use of a low cost
antenna with switchable directional properties, the switched freeloading antenna, is studied in
a MIMO context and compared to array techniques. It is found that it has comparable
performance, at a fraction of the cost for an array antenna.

Keywords: antenna array, calibration, mutual coupling, MIMO system, flat fading, nonlinear
amplifier, switched parasitic antenna, analog beamformer, adaptive antenna testbed.

11


1.0 INTRODUCTION

Wireless systems are now popular worldwide to help people and machines to communicate
with each other irrespectively of their location. So far, using a cellular system is by far the most
common wireless method to access data or to perform voice dialing. But in a near future, we will be
surrounded by a numerous of options to set up an unwired connection over the radio interface. One of
the slogans for the fourth generation wireless communications system (4G) is “always best
connected”, meaning that your wireless equipment should connect to the network or system that at the
moment is the “best” for you. Various connections ranges from satellites that provides low bit rates
but global coverage and cellular systems with continental coverage to high bit rate local area networks
and personal area networks with a maximum range of a few to a hundred meters. If these systems
should co-exist, then we would obtain a crowded frequency spectrum, since there are many different
actors that want their share of the limited frequency resource. To use a signaling strategy that is
spectrally efficient is thus of utmost importance. The current trend to achieve high spectral efficiency
is by utilizing adaptivity in the ever changing radio environment and sources of interference.
Adaptivity on the physical layer can be used in all possible dimensions: Time, frequency, power and
space. Adaptivity can also be used on higher signaling layers to boost performance even further; an
example is multi-user scheduling. This thesis is devoted to the physical layer of wireless
communication systems and will focus mainly on the adaptive utilization of the space dimension.
Space utilization is possible through the use of multiple antenna elements arranged in an array, for the
transmission and/or reception of the signals. Or, in some cases a single antenna element that has
several polarizations or modes is used to obtain polarization or angle diversity. Due to the use of
multiple antennas, the antenna gain is increased and this leads to an increased range and coverage.
This is useful in remote areas with low population. A large area can thus be served with less base
stations. Alternatively, the transmit power of the mobile units can be reduced due to the increased
gain, or sensitivity, of the receiving base station antenna array. By using the spatial dimension
provided by multiple antenna elements, it is possible to suppress interfering signals in a way that is
not possible with a single antenna. Hence, the system can be tuned to be less susceptible to
interference and the distance between base stations using the same time/frequency channel can be
reduced, which is beneficial in densely populated areas. This leads to a system capacity improvement.
A receiver array antenna can be used to localize the transmitter, just as we can use our both ears to
localize the source of a sound in a room without using our eyes. This has application in positioning
services and emergency call localization. The maximum likelihood (ML) detector for linear space-
time codes used over a flat fading MIMO channel with spatially and temporally colored Gaussian
noise is now derived. The derivation gives insight to the problems associated with spatial multiplexing
transmission and the resulting detector is used in other sections of this thesis. Furthermore, the
pairwise error probability (PEP) for the linear space time code is also studied. The PEP characterizes
the performance of a system with coding over a finite number of blocks, M, and captures the diversity
advantage of a code.

2.0 MIMO SYSTEM PERFORMANCE

MIMO systems in flat fading channels are in this chapter analyzed and compared, with and
without partial Channel State Information (CSI) at the transmitter, under the assumption of different
fading statistics. It will be assumed that the signals from different receive and/or transmit antennas are
correlated. Mutual coupling is also introduced between the antenna elements. Measurement results
from a MIMO testbed are presented and it is verified that the Rayleigh fading assumption is valid in
some cases. Accurate and tractable channel modeling is critical to realize the full potential of antenna
arrays. Two subgroups of channel models for MIMO systems can be identified. One is based on the
physical properties of the channel, and is a parametric model where the parameters are; the number of
scatterers, angle of arrival of the signal from the scattered, time delay, and power decay profiles.
These parameters are often modelled as random variables from a given distribution. Such models can

12


adapt to the multipath environment and the array geometry and its flexibility makes them
attractive for computer simulations. For analytical derivations, however, a reductionistic
approach is taken, where random matrix theory is used to define the channel with fewer
parameters than in the physical model, although at the expense of lower accuracy in capacity
prediction. To generate signal correlation matrices CR and CT, a model that resembles the
physical scattering in the channel can be used. Many different approaches to model signal
correlation, or directly the channel matrix for MIMO systems have been presented.

3.0 STOCHASTIC CHANNEL MODELS

The propagation scenario in a wireless communication system is very complex and
the signal transmitted from an antenna will reach the receiving antenna after many path
reflections. If the scattering is rich enough, then a stochastic method is suitable to model the
channel. The well known stochastic models for the SISO channel are in this section extended
to the MIMO channel. The elements of the H matrix are assumed to be random variables
taken from a probability distribution function (PDF). The elements are in general correlated
but sometimes the correlation is neglected in an initial analysis to make it tractable. The
correlation depends on the scattering scenario, the antenna element radiation patterns, their
configuration and separation distance. It is modelled using a general correlation model, where
the covariance matrix of the channel matrix elements is defined. However, in the definition of
the Rayleigh fading MIMO channel, a special structure will be imposed on the correlation
matrix. This structure makes further analysis of the Rayleigh fading MIMO case possible,
since the joint pdf of the eigenvalues to HH* for this case is known.

4.0 SIGNAL CORRELATION

An important property of the MIMO channel that essentially determines the channel
capacity is the correlation between the channel coefficients. When multi antenna systems are
analyzed, it is commonly assumed that the fading between pairs of transmit and receive
antennas are independent and identically distributed random variables with a Rayleigh
distribution

5.0 MEASURED MIMO CHANNELS

To collect real MIMO channel data, measurements were performed in an indoor
environment at the Signals and Systems Group, Uppsala University. The aim of the
measurements was to verify the assumptions made in the theoretical analysis regarding fading
distribution, signal correlation and the flat fading assumption. The measurements were
performed in an indoor office environment using a 4 × 4 MIMO system at the frequency 1.8
GHz. A Vector Network Analyzer was used to measure the channel coefficients for the 16
channels using a switching method.
Each complete “MIMO snapshot” of all the 16 channels were measured in less than 3
seconds, which is fast enough in indoor environments to ensure that the channel remains
stationary during each measurement. The switching method was also used on an 8×8 MIMO
system. Between each measurement, the receiving array was moved one eight of a
wavelength in the broadside direction. The antenna elements were microstrip patch antennas
placed in a linear array with an inter-element spacing (δ) of half a wavelength (δ = λc/2). The
patch antennas had a half power beam width of 80◦ and a half power bandwidth of 170 MHz

13


and the measurement SNR was set to 20 dB in all measurements. Two scenarios were
investigated, one line of sight (LOS) and one non-LOS (NLOS) setup. In the LOS scenario,
the two arrays were placed facing each other in an 8×6 meter laboratory room containing
various instruments, tables and cabinets and ND = 146 measurements were conducted. In the
NLOS scenario, ND = 220 measurements were conducted and the receive array was placed
outside the laboratory room, centered in a long corridor with the array broadside parallel with
the corridor. The transmit array was kept in the adjacent laboratory. To make comparisons
with the theoretical models, each element of the measured H matrix was normalized as

6.0 COHERENCE BANDWIDTH

The initial measurements aimed to verify the flat Rayleigh fading assumption. The
measured power spectrum in the NLOS case from one transmit antenna to the four receive
antennas. The coherence bandwidth (at correlation coefficient 0.9) is estimated to Bc = 2.8
MHz so the flat fading assumption is valid if the signalling bandwidth is less than Bc. If a
system with higher bitrate is required, then transmission over many subchannels can be used,
where the bandwidth of each subchannel is less than Bc

Fig.1. Power spectrum for NLOS channel. Each curve represents the received power in
one out of four half-a-wavelength spaced antennas from one transmit antenna.

14


Figure 2 shows the normalized channel amplitudes for all 16 channels in the NLOS case and
it is immediately apparent that the channels fading patterns are different. The correlation
between these subchannels is further explored below. In Figure 3, the estimated probability
density functions of the normalized amplitudes in the LOS and NLOS cases are shown. The
curves are fitted to a Nakagami-m distribution using a moment based method [100]. The
Nakagami-m distribution .The mf -parameter was estimated to mf = 1.17 in the NLOS case
and mf = 6.41 in the LOS case. The measured data was also used

FIG.2. Channel amplitudes in a 4 × 4 MIMO NLOS channel as a function of
measurement.

An important property of the MIMO channel that essentially determines the channel capacity
is the correlation between the channel coefficients. When multi antenna systems are analyzed,
it is commonly assumed that the fading between pairs of transmit and receive antennas are
independent and identically distributed random variables with a Rayleigh distribution. On the
other hand, a small antenna element spacing is often necessary to fit multiple antennas on a
portable device. Small element spacing also introduces mutual coupling between the antenna
elements which affects the achievable capacity of the system, although not necessarily in a
negative way the distances from the different transmitting array antennas via the scattered to
the receiver array are approximately equal, so the correlation between two paths from
different transmit elements to one receive elements is expected to be high as compared to the
large

15


FIG.3. CUMULATIVE DISTRIBUTION OF PHASE IN NLOS CHANNEL.

7.0 CONCLUSIONS

The aim of this work was to investigate the impact of using MCPA’s at the base station
for the downlink in a wireless system. The derivations was simplified by assuming a switched
beam configuration, often implemented using a beamforming network, such as the Butler
matrix. This technique was shown to have the interesting property that the IMD follows the
same radiation patterns as the original desired signals patterns. This allowed for the concept
of a beam-frequency scheme and a frequency channel allocation algorithm for reducing the
harmful IMD in that particular cell was developed. A base station with more antenna
elements gave a reduction of intermodulation distortion which could be of the order of
several dB.

REFERENCES

[1] P.K. Bondyopadhyay, “The first application of array antenna,” in Proceedings of IEEE
International Conference on Phased Array Systems and Technology., Dana Point , USA,
2000, pp. 29–32.
[2] S. Andersson, B. Carlqvist, B. Hagerman, and R. Lagerholm, “Enhancing cellular
network capacity with adaptive antennas,” Ericsson Review, vol. 76, pp. 138–141, 1999.
[3] H. Dam, M. Berg, S. Andersson, R. Bormann, M. Frerich, and T. Henβ, “Performance
evaluation of adaptive antenna base stations in a commercial GSM network,” in Proceedings
of Vehicular Technology Conference (VTC), Piscataway, USA, 1999, pp. 47–51.

16


[4] K. Sheikh, D. Gesbert, D. Gore, and A.J. Paulraj, “Smart antennas for broadband wireless
access networks,” IEEE Signal Processing Magazine, vol. 37, no. 11, pp. 100–105, Nov.
1999.
[5] R.T. Derryberry, S.D. Gray, D.M. Ionescu, G.Mandyam, and B. Raghothaman, “Transmit
diversity in 3G CDMA systems,” IEEE Communications Magazine, vol. 40, no. 4, pp. 68–75,
April 2002.
[6] S. Andersson and B. Hagerman, “Adaptive antennas in wireless systems basic
background and field-trial results,” in Proceedings of Radiovetenskaplig Konferens,
Karlskrona, Sweden, 1999, pp. 249–253.
[7] S. Andersson, U.Forss´en, J.Karlsson, T.Witzschel, P. Fischer, and A. Krug,
“Ericsson/Mannesmann GSM field trials with adaptive antennas,” in Proceedings
of Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp. 1587–1591.
[8] J. Strandell, M. Wennstro¨m, A. Rydberg, T. O¨ berg, O. Gladh, L. Rexberg, E. Sandberg,
B.V. Andersson, and M. Appelgren, “Experimental evaluation of an adaptive antenna for a
TDMA telephony system,” in IEEE Personal Indoor and Mobile Radio Communications
Conference (PIMRC), Helsinki, Finland, Sep. 1997, pp. 79–84.
[9] J. Strandell, M. Wennstro¨m, T. O¨ berg, and A. Rydberg, “Design and evaluation of a
fully adaptive antenna for telecommunication systems,” in Proceedings Antenn97 conference,
Gothenburg, Sweden, 1997, pp. 357–366.
[10] S.M. Simmonds and M. Beach, “Downlink calibration requirements for the
TSUNAMI(II) adaptive antenna testbed,” in Proceedings of the Ninth International
Symposium on Personal, Indoor and Mobile Radio Communications, Boston, USA, 8-11
September 1998.
[11] P.E. Mogensen, K.I. Pedersen, P. Leth-Espensen, B. Fleury, F. Fredriksen, and
K.Olesen, “Preliminary results from an adaptive antenna array testbed for GSM/UMTS,” in
Proceedings of Vehicular Technology Conference (VTC), Phoenix,USA, May 1997, pp.
1592–1596.
[12] G. Tsoulos, M. Beach, and J. McGeehan, “Space division multiple acess (SDMA) field
trials. Part 2: Calibration and linearity issues,” IEE Proceedings - Radar, Sonar and
Navigation, vol. 145, no. 1, pp. 79–84, Feb. 1998.
[13] S.M. Alamouti, “A simple transmit diversity technique for wireless communications,”
IEEE Journal on selected areas in communications, vol. 16, no. 8, pp. 1451–1458, Oct. 1998.
[14] T. O¨ berg, Modulation, Detection and Coding. Chichester: John Wiley and Sons, 2001.
[15] J.G. Proakis, Digital Communications. Singapore: McGraw-Hill, 1989.
[16] Dr. V. Murali Krishna, Karimella Vikram and Prof. Narasimha, “Broadband Wireless
Communication” International journal of Electronics and Communication Engineering
&Technology (IJECET), Volume3, Issue2, 2012, pp. 217 - 226, Published by IAEME.
[17] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi,
“Rectangular Microstrip Array Antennas For Wide Triple Band Operation” International
journal of Electronics and Communication Engineering &Technology (IJECET), Volume1,
Issue1, 2010, pp. 53 - 61, Published by IAEME.
[18] Gangadhar P Maddani, Sameena N Mahagavin and Shivasharanappa N Mulgi, “Design
And Development Of Microstrip Array Antenna For Wide Dual Band Operation”
International journal of Electronics and Communication Engineering &Technology
(IJECET), Volume1, Issue1, 2010, pp. 107 - 116, Published by IAEME.

17

Prediction of wireless communication systems in the context of modeling 2-3-4

More Related Content

What's hot (20)

Viewers also liked (6)

Similar to Prediction of wireless communication systems in the context of modeling 2-3-4 (20)

More from IAEME Publication (20)

Prediction of wireless communication systems in the context of modeling 2-3-4