![rule. At the same time, the effects of multipath and
shadowing can be weakened, and the channel capacity will
be increased.
There are several diversity techniques based on multi-
antenna, including time diversity, frequency diversity, space
diversity, polarization diversity, angle diversity, etc.
Polarization diversity and angle diversity are two special
examples of space diversity [1]
. If there is one antenna at
transmitter (i=1) and multiple antennas at receiver (j>1), then
it is a SIMO system and the diversity is called receive
antenna diversity. If there are multiple antennas at
transmitter (i>1) and one antenna at receiver (j=1), then it is
a MISO system and the diversity is called transmit antenna
diversity.
A. Receive antenna diversity
Because the channel state information at the receiver
(CSIR) is available easily than at the transmitter, receive
antenna diversity is usually adopted in traditional space
diversity. The optimal combination of independent signals at
receiver can improve the SNR at receiver, channel capacity
and spectral efficiency. The combination rules adopted at
receiver usually include selective combining, equal-gain
combining and maximal ratio combining. The maximal ratio
combining is the optimum combining rule because the SNR
at receiver can reach the maximum which is equal to the sum
of instantaneous SNR of all single received signals[2]
.
B. Transmit antenna diversity
In transmit antenna diversity, several duplication of
transmitting signal with spatial redundancy are transmitted to
receivers by multiple independent antennas or antenna array.
According to whether the channel state information (CSI) is
available at transmitter or not, transmit antenna diversity can
be divided into closed-loop transmit diversity and open-loop
transmit diversity.
In closed-loop transmit diversity, the CSI is needed at
transmitter and acquired through feedback channel from
receivers. The coefficient of transmit antennas is adaptive to
CSI and power of received signal or channel capacity can
acquire the maximum. It is very difficult to acquire CSI at
transmitter usually. Uncertain estimate to CSI will influence
the performance of closed-loop transmit diversity system. If
the estimate of CSI isn’t match with the current CSI will also
lead to the same result. When the CSI of uplink and
downlink are similar, the closed-loop transmit diversity is
feasible even without the feedback information of CSI from
receivers.
To a fast fading channel, it is usually very difficult to
acquire the effective CSI at transmitter in channel coherence
time. So the open-loop transmit diversity is applied more
widely than closed-loop transmit diversity. In an open-loop
transmit diversity, data must be processed (like space-time
coding) before transmission to get diversity gain.
SIMO and MISO are very fit to present cellular
networks. If we configure multiple antennas at BS and single
antenna at MS, receive antenna diversity can be realized in
uplink and transmit antenna diversity can be realized in
downlink. The lower power level of signal from MS can be
compensated to improve signal quality and enlarge the
effective communication distance in uplink. At the same
time, the complexity, size and cost of MS can be reduced for
the transmit antenna diversity in downlink.
III. MIMO
To make use of transmit diversity and receive diversity
synthetically, multiple-antenna can be configured to
transmitter and receiver synchronously in a MIMO system.
MIMO can be divided into centralizing MIMO and
distributing MIMO depending on that multiple-antenna is
belonged to one terminal or geographically separated
terminals.
A. Centralizing MIMO
If the signal from multiple-antenna is irrelevant,
independent parallel channels can be brought by space-time
coding and multiple-antenna in the transmitter of a
centralizing MIMO system. Transmit diversity can bring
spatial multiplexing gain, enhance information transmitting
rate and spectral efficiency. Receive diversity can increase
channel capacity by improving SNR at receiver.
In a MIMO system with i transmit antennas and j receive
antennas, if CSI is certain but unavailable to the transmitter
and transmitting power is allocated to every antenna
averagely in a deterministic channel, channel capacity of the
MIMO system is[3]
:
Q
i
IC jiMIMO ),min(2 detlog
Where det( ) denotes the determinant of ( ), Imin(i, j) is
a identity matrix of min(i, j) min(i, j), H denotes the
channel coefficient matrix from transmit antenna to receive
antenna, and
jiwhenHH
jiwhenHH
Q H
H
,
,
where the
superscript H denotes conjugate transpose.
When multiple transmit antennas make of orthogonal
sub-channels, the channel capacity is:
)1(log),min( 2jiCMIMO
It is obvious from (3) that the channel capacity of a
MIMO system is increased linearly with min(i, j) without
extra power and bandwidth. This is especially important to
cellular networks with increasing user and operation.
The realization of MIMO channel capacity is determined
by if the CSI is available to transmitters. If the transmit
power is fixed and the CSI is available to transmitters, the
transmit power can be made the best of by Water-filling
methods and the capacity can be achieved in different
channel conditions[4]
.
SIMO and MISO can be regarded as two special
examples of MIMO, so the capacity of SIMO and MISO
channel in the same condition can be deduced from (2):
2010 2nd International Conference on Signal Processing Systems (ICSPS)
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![)1(log2 jCSIMO
)1(log2MISOC
Equation (4) shows that the diversity gain of a SIMO is
equal to the number of receive antennas compared with
SISO. Equation (5) shows that the MISO channel capacity is
equal to SISO when CSI is unavailable to transmitters. But
when CSI is available to transmitters, the most diversity gain
of MISO is equal to the number of transmit antennas
compared with SISO without extra transmitting power and
bandwidth [5]
.
B. Distributing MIMO
The spatial diversity gain from multi-antenna in MIMO
systems can confront fading channel, and the data
transmission rate and spectral efficiency are increased
significantly. But the application of centralizing MIMO is
limited because MS in cellular networks can’t configure
multi-antenna for consideration of size, power consumption
and cost.
The structure of a distributing MIMO system is shown in
Fig. 2 in which there are i-1 MS around T1 and j-1 MS
around R1. The antennas of several geographically closed
MS can construct a virtual antenna array (VAA) or a
distributing MIMO system. When T1 communicates with R1,
the MS geographically closed to T1 receive and transmit the
information from T1 after some kind of processing. Of course,
they can transmit their own information in this process.
Similarly, the R1 receive the information directly from the T
and from some other MS geographically closed to R1.
Figure 3 is a simple application of distributing MIMO in
cellular mobile communications in which there is a source
MS (MS1) and two relay MS (MS2 and MS3). In the uplink of
MS1, when MS2 and MS3 receive the information from MS1,
they process the information according to the cooperative
protocol and transmit the processed information to the BS
with their own information at the same time. The process of
the downlink is similar with the uplink. The research shows
that the system capacity is enhanced and/or the reliability of
the link from the source to the destination is improved [6]
.
Figure 2. Illustration of a distributed MIMO
Figure 3. A simple distributed MIMO
The cooperation in VAA makes a single-antenna MS to
have the spatial diversity gain, so the advantages of a
centralizing MIMO is also achieved in a distributing MIMO,
including higher data transmission rate [7]
, lower outage
probability and bit error rate, more coverage of cells, etc.
Usually, a MS of cellular networks have only single
antenna, so the distributing MIMO is much applicable than
centralizing MIMO.
When space-time code is introduced, the transmission
performance is improved obviously even in worse uplink
channel condition. At the same time, the transmission
performance is also improved slightly in better uplink
channel condition[8]
.
IV. ADAPTIVE ANTENNA ARRAY
The signal arrived to receive antenna array must be
strongly relevant in Adaptive Antenna Array (AAA), and
this is different from MIMO. AAA track and extract the
spatial information of MS, and transmit/receive signal in the
same channel using orientation difference of MS without
interference. The structure of AAA at receiver is shown in
Fig. 4. The radio wave arriving at multiple receive antenna is
sent to the beamformer after analog-to-digital conversion.
The beamformer adjusts the coefficient of every array
element to optimum reception according to some rule.
Based on reciprocity theorem, if the coefficient keep
invariable, the transmit antenna array will have the same
orientation gain.
AAA can enhance the SNR at receiver and channel
capacity obviously by improving expectation signal quality
and restraint interference from different orientation. At the
same time, AAA spreads the communication resources from
time domain, frequency domain and code domain to space
domain and increases the system capacity greatly.
V. CONCLUSIONS
At present, multi-antenna techniques are applied to
cellular mobile communications widely. AAA is introduced
to time division – synchronous code division multiple access
(TD-SCDMA) and MIMO (including SIMO and MISO) is
introduced to wideband code division multiple access
(WCDMA) and code division multiple access 2000
(CDMA2000) in the third generation (3G) standard of
cellular mobile communication.
The AAA used in cellular mobile communication is
called smart antenna. The system capacity of cellular
2010 2nd International Conference on Signal Processing Systems (ICSPS)
V1-840
Authorized licensed use limited to: IEEE Xplore. Downloaded on October 22,2011 at 06:40:34 UTC from IEEE Xplore. Restrictions apply.](https://image.slidesharecdn.com/applicationofmulti-antennatechnologiesincellularmobilecommunications-160922175111/85/Application-of-multi-antenna-technologies-in-cellular-mobile-communications-3-320.jpg)
![networks can be increased significantly for spatial gain of
smart antenna.
Compared with SISO, MIMO is very fit for the
development trend of cellular mobile communication for the
obvious advantage on communication distance, throughput
and reliability. Furthermore, MIMO can enhance system
capacity and spectral efficiency significantly without extra
bandwidth. So, the idea of MIMO is adopted by nearly all
schemes in beyond 3G (B3G) [9]
. The related standards of
MIMO have been published by International
Telecommunication Union (ITU) and the Third Generation
Partnership Project (3GPP).
There is no problem to portable computer in configuring
multiple antennas, and the antennas should have the
characteristics of high gain and small size [10]
. But it is
difficult to MS for the limitation of size, power consumption
and cost. This problem can be solved in a distributing MIMO
system by cooperation among MS.
For the different characteristics and their advantages over
single antenna technology, multi-antenna technologies will
be applied synthetically with single antenna technology in
the future cellular mobile communications for better
performance on channel capacity and spectral efficiency.
ACKNOWLEDGMENT
This work was supported in part by the National Science
Foundation of China (No. 60772119, 60972063), the
Program for New Century Excellent Talents in University
(No. NCET-08-0706), the Science Foundation of Liaoning
Province (No. 20072147), the Program for Liaoning
Excellent Talents in University (No. 2008RC56), the Open
Research Fund of Nation Mobile Communications Research
Laboratory of Southeast University (No. W200812), the
Scientific Research Development Fund of Dalian Navy
Academy (No. 2010003).
REFERENCES
[1] Guey J-C, Fitz M, Bell M and Kuo W-Y. “Signal design for
transmitter diversity wireless communication systems over Rayleigh
fading channels,” IEEE Trans. Commun., vol. 47, no. 4, pp. 527-537,
Apr. 1999.
[2] Rappaport T S. “Wireless Communication: Principles and Practice,”
New Jersey, USA: Prentice Hall, Englewood Cliffs, 1996.
[3] Telatar I E. “Capacity of multi-antenna Gaussian channels,” Eur.
Trans. Telecomm., vol. 10, no. 6, pp. 585-595, Jul. 1999.
[4] Smith P J and shafi M. “Water-filling methods for MIMO systems,”
3rd Australian Communication Theory Workshop, Canberra,
Australia, 2002.
[5] Winters J H. “The diversity gain of transmit diversity in wireless
systems with Rayleigh fading,” Proc. IEEE Int. Conf.
Communications, vol. 2, New Orleans, LA, MAY 1994, pp. 1121-
1125.
[6] Nosratinia A, Hunter T E and Hedayat A. “Cooperative
communication in wireless networks,” IEEE Commun. Mag., vol. 42,
no. 10, pp. 74-80, Oct. 2004.
[7] Sendonaris A, Erkip E and Aazhang B. “User cooperation diversity
part II: Implementation aspects and performance analysis,” IEEE
Trans. Commun., vol. 51, no. 11, pp. 1939-1948, Nov. 2003.
[8] Janani M, Hedayat A, Hunter T E and Nosratinia A. “Goded
cooperation in wireless communications: Space-time transmission
and iterative decoding,” IEEE Trans. Signal Process., vol. 52, no. 2,
pp. 362-371, Feb. 2004.
[9] Dahlman E, Ekstrom H, Furuskar A and et al. “The 3G long-term
evolution-radio interface concepts and performance evaluation,” Proc.
IEEE 63rd Veh. Technol. Conf., vol. 1, MAY. 2006, pp: 137-141.
[10] Daniele Piazza, Michele D’Amico and Kapil R. Dandekar.
“Performance Improvement of a Wideband MIMO System by Using
Two-Port RLWA,” IEEE Antennas and Wireless Propagation Letters,
vol. 8, pp. 830-834, 2009.
Figure 4. Structure of adaptive antenna array systems
2010 2nd International Conference on Signal Processing Systems (ICSPS)
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Application of multi antenna technologies in cellular mobile communications
- 1. Application of Multi-antenna Technologies in Cellular Mobile Communications Qing-hua Wu Dalian Maritime University Dalian Naval Academy Dalian, P. R. China wqhaaa76@163.com Zi-wei Zheng Dalian Maritime University Dalian, P. R. China Southeast University Nanjing, P. R. China Ningbo University Ningbo, P. R. China ziwei_zheng@yahoo.com.cn Shao-hua Chen Dalian Maritime University Dalian, P. R. China chengshineng@163.com Abstract—In cellular mobile communications, multipath fading, shadowing, near-far problem and the Doppler effect have led to a complex radio wave propagation environment. Furthermore, with the continuously increasing of users and operations, not only new bandwidth is required, new technologies and measures are also expected to improve the system capacity and channel capacity. This paper introduces the general structure of multi-antenna communication systems firstly, then analyzes the characteristics of various multi- antenna technologies and their advantages over single antenna system when applied in cellular mobile communications. Finally, combined with the trends and requirements of cellular mobile communications, the further application of multi- antenna technologies in future cellular mobile communications is prospected. Keywords-cellular network, multiple input multiple output, adaptive antenna array, virtual antenna array, channel capacity, diversity. I. INTRODUCTION In traditional cellular networks, there is only one antenna in base stations (BS) and mobile stations (MS) usually. This is called single input single output (SISO) system. In a continuous SISO channel, when there is only additive white Gaussian noise (AWGN), the capacity of this channel is: )1(log)1(log 0 22 Bn S C Where C is channel capacity, is signal-to-noise ratio (SNR), S is power of signal, B is channel bandwidth, and n0 is side power spectral density. Equation (1) gives the upper limit of reliably rate of communications in a AWGN channel. The channel capacity can be enhanced by adding channel bandwidth or increasing SNR at receiver. But the increase of channel bandwidth will lead to the increase of noise power, so channel capacity can’t be enhanced infinitely with the increase of bandwidth. When channel bandwidth tends to infinite, channel capacity will tend to 1.44S/n0, a constant proportion to SNR at receiver. At the same time, when SNR at receiver is enhanced because of the increase of transmit power, the electromagnetic pollution to environment and interference between equipments will also be enhanced. The channel condition and motion of MS result in the awful environment of radio wave propagation, and Doppler shift, multipath fading, shadowing loss, near-far problem are austere challenge in a cellular network. At the same time, because of the increasing user and operation, new techniques and measures must be introduced to enhance channel capacity and spectral efficiency except for extra bandwidth. How to enhance channel capacity and spectral efficiency in limited bandwidth becomes a hotspot in cellular network research. At present, multi-antenna techniques have been widely applied in cellular networks because they have geminate channel capacity. The structure of radio communication systems is shown in Fig. 1, in which there are i antennas at transmitter and j antennas at receiver. Based on the number of transmit/receive antenna and correlation of different array elements, radio communication systems can be classed into SISO, single input multiple output (SIMO), multiple input single output (MISO), multiple input multiple output (MIMO) and adaptive antenna array (AAA). SIMO and MISO can be regarded as two special example of MIMO. II. DIVERSITY TECHNOLOGIES Diversity is a technology in which signals from multiple independent channels is utilized to restore the information at receiver. Figure 1. Figure 1 Structure of radio communication systems Because the probability that independent multipath signals are in deep fading simultaneously is seldom impossible, the SNR at receiver of expect signal can be enhanced by combining the multipath signal based on some 2010 2nd International Conference on Signal Processing Systems (ICSPS) V1-838978-1-4244-6893-5/$26.00 2010 IEEEC Authorized licensed use limited to: IEEE Xplore. Downloaded on October 22,2011 at 06:40:34 UTC from IEEE Xplore. Restrictions apply.
- 2. rule. At the same time, the effects of multipath and shadowing can be weakened, and the channel capacity will be increased. There are several diversity techniques based on multi- antenna, including time diversity, frequency diversity, space diversity, polarization diversity, angle diversity, etc. Polarization diversity and angle diversity are two special examples of space diversity [1] . If there is one antenna at transmitter (i=1) and multiple antennas at receiver (j>1), then it is a SIMO system and the diversity is called receive antenna diversity. If there are multiple antennas at transmitter (i>1) and one antenna at receiver (j=1), then it is a MISO system and the diversity is called transmit antenna diversity. A. Receive antenna diversity Because the channel state information at the receiver (CSIR) is available easily than at the transmitter, receive antenna diversity is usually adopted in traditional space diversity. The optimal combination of independent signals at receiver can improve the SNR at receiver, channel capacity and spectral efficiency. The combination rules adopted at receiver usually include selective combining, equal-gain combining and maximal ratio combining. The maximal ratio combining is the optimum combining rule because the SNR at receiver can reach the maximum which is equal to the sum of instantaneous SNR of all single received signals[2] . B. Transmit antenna diversity In transmit antenna diversity, several duplication of transmitting signal with spatial redundancy are transmitted to receivers by multiple independent antennas or antenna array. According to whether the channel state information (CSI) is available at transmitter or not, transmit antenna diversity can be divided into closed-loop transmit diversity and open-loop transmit diversity. In closed-loop transmit diversity, the CSI is needed at transmitter and acquired through feedback channel from receivers. The coefficient of transmit antennas is adaptive to CSI and power of received signal or channel capacity can acquire the maximum. It is very difficult to acquire CSI at transmitter usually. Uncertain estimate to CSI will influence the performance of closed-loop transmit diversity system. If the estimate of CSI isn’t match with the current CSI will also lead to the same result. When the CSI of uplink and downlink are similar, the closed-loop transmit diversity is feasible even without the feedback information of CSI from receivers. To a fast fading channel, it is usually very difficult to acquire the effective CSI at transmitter in channel coherence time. So the open-loop transmit diversity is applied more widely than closed-loop transmit diversity. In an open-loop transmit diversity, data must be processed (like space-time coding) before transmission to get diversity gain. SIMO and MISO are very fit to present cellular networks. If we configure multiple antennas at BS and single antenna at MS, receive antenna diversity can be realized in uplink and transmit antenna diversity can be realized in downlink. The lower power level of signal from MS can be compensated to improve signal quality and enlarge the effective communication distance in uplink. At the same time, the complexity, size and cost of MS can be reduced for the transmit antenna diversity in downlink. III. MIMO To make use of transmit diversity and receive diversity synthetically, multiple-antenna can be configured to transmitter and receiver synchronously in a MIMO system. MIMO can be divided into centralizing MIMO and distributing MIMO depending on that multiple-antenna is belonged to one terminal or geographically separated terminals. A. Centralizing MIMO If the signal from multiple-antenna is irrelevant, independent parallel channels can be brought by space-time coding and multiple-antenna in the transmitter of a centralizing MIMO system. Transmit diversity can bring spatial multiplexing gain, enhance information transmitting rate and spectral efficiency. Receive diversity can increase channel capacity by improving SNR at receiver. In a MIMO system with i transmit antennas and j receive antennas, if CSI is certain but unavailable to the transmitter and transmitting power is allocated to every antenna averagely in a deterministic channel, channel capacity of the MIMO system is[3] : Q i IC jiMIMO ),min(2 detlog Where det( ) denotes the determinant of ( ), Imin(i, j) is a identity matrix of min(i, j) min(i, j), H denotes the channel coefficient matrix from transmit antenna to receive antenna, and jiwhenHH jiwhenHH Q H H , , where the superscript H denotes conjugate transpose. When multiple transmit antennas make of orthogonal sub-channels, the channel capacity is: )1(log),min( 2jiCMIMO It is obvious from (3) that the channel capacity of a MIMO system is increased linearly with min(i, j) without extra power and bandwidth. This is especially important to cellular networks with increasing user and operation. The realization of MIMO channel capacity is determined by if the CSI is available to transmitters. If the transmit power is fixed and the CSI is available to transmitters, the transmit power can be made the best of by Water-filling methods and the capacity can be achieved in different channel conditions[4] . SIMO and MISO can be regarded as two special examples of MIMO, so the capacity of SIMO and MISO channel in the same condition can be deduced from (2): 2010 2nd International Conference on Signal Processing Systems (ICSPS) V1-839 Authorized licensed use limited to: IEEE Xplore. Downloaded on October 22,2011 at 06:40:34 UTC from IEEE Xplore. Restrictions apply.
- 3. )1(log2 jCSIMO )1(log2MISOC Equation (4) shows that the diversity gain of a SIMO is equal to the number of receive antennas compared with SISO. Equation (5) shows that the MISO channel capacity is equal to SISO when CSI is unavailable to transmitters. But when CSI is available to transmitters, the most diversity gain of MISO is equal to the number of transmit antennas compared with SISO without extra transmitting power and bandwidth [5] . B. Distributing MIMO The spatial diversity gain from multi-antenna in MIMO systems can confront fading channel, and the data transmission rate and spectral efficiency are increased significantly. But the application of centralizing MIMO is limited because MS in cellular networks can’t configure multi-antenna for consideration of size, power consumption and cost. The structure of a distributing MIMO system is shown in Fig. 2 in which there are i-1 MS around T1 and j-1 MS around R1. The antennas of several geographically closed MS can construct a virtual antenna array (VAA) or a distributing MIMO system. When T1 communicates with R1, the MS geographically closed to T1 receive and transmit the information from T1 after some kind of processing. Of course, they can transmit their own information in this process. Similarly, the R1 receive the information directly from the T and from some other MS geographically closed to R1. Figure 3 is a simple application of distributing MIMO in cellular mobile communications in which there is a source MS (MS1) and two relay MS (MS2 and MS3). In the uplink of MS1, when MS2 and MS3 receive the information from MS1, they process the information according to the cooperative protocol and transmit the processed information to the BS with their own information at the same time. The process of the downlink is similar with the uplink. The research shows that the system capacity is enhanced and/or the reliability of the link from the source to the destination is improved [6] . Figure 2. Illustration of a distributed MIMO Figure 3. A simple distributed MIMO The cooperation in VAA makes a single-antenna MS to have the spatial diversity gain, so the advantages of a centralizing MIMO is also achieved in a distributing MIMO, including higher data transmission rate [7] , lower outage probability and bit error rate, more coverage of cells, etc. Usually, a MS of cellular networks have only single antenna, so the distributing MIMO is much applicable than centralizing MIMO. When space-time code is introduced, the transmission performance is improved obviously even in worse uplink channel condition. At the same time, the transmission performance is also improved slightly in better uplink channel condition[8] . IV. ADAPTIVE ANTENNA ARRAY The signal arrived to receive antenna array must be strongly relevant in Adaptive Antenna Array (AAA), and this is different from MIMO. AAA track and extract the spatial information of MS, and transmit/receive signal in the same channel using orientation difference of MS without interference. The structure of AAA at receiver is shown in Fig. 4. The radio wave arriving at multiple receive antenna is sent to the beamformer after analog-to-digital conversion. The beamformer adjusts the coefficient of every array element to optimum reception according to some rule. Based on reciprocity theorem, if the coefficient keep invariable, the transmit antenna array will have the same orientation gain. AAA can enhance the SNR at receiver and channel capacity obviously by improving expectation signal quality and restraint interference from different orientation. At the same time, AAA spreads the communication resources from time domain, frequency domain and code domain to space domain and increases the system capacity greatly. V. CONCLUSIONS At present, multi-antenna techniques are applied to cellular mobile communications widely. AAA is introduced to time division – synchronous code division multiple access (TD-SCDMA) and MIMO (including SIMO and MISO) is introduced to wideband code division multiple access (WCDMA) and code division multiple access 2000 (CDMA2000) in the third generation (3G) standard of cellular mobile communication. The AAA used in cellular mobile communication is called smart antenna. The system capacity of cellular 2010 2nd International Conference on Signal Processing Systems (ICSPS) V1-840 Authorized licensed use limited to: IEEE Xplore. Downloaded on October 22,2011 at 06:40:34 UTC from IEEE Xplore. Restrictions apply.
- 4. networks can be increased significantly for spatial gain of smart antenna. Compared with SISO, MIMO is very fit for the development trend of cellular mobile communication for the obvious advantage on communication distance, throughput and reliability. Furthermore, MIMO can enhance system capacity and spectral efficiency significantly without extra bandwidth. So, the idea of MIMO is adopted by nearly all schemes in beyond 3G (B3G) [9] . The related standards of MIMO have been published by International Telecommunication Union (ITU) and the Third Generation Partnership Project (3GPP). There is no problem to portable computer in configuring multiple antennas, and the antennas should have the characteristics of high gain and small size [10] . But it is difficult to MS for the limitation of size, power consumption and cost. This problem can be solved in a distributing MIMO system by cooperation among MS. For the different characteristics and their advantages over single antenna technology, multi-antenna technologies will be applied synthetically with single antenna technology in the future cellular mobile communications for better performance on channel capacity and spectral efficiency. ACKNOWLEDGMENT This work was supported in part by the National Science Foundation of China (No. 60772119, 60972063), the Program for New Century Excellent Talents in University (No. NCET-08-0706), the Science Foundation of Liaoning Province (No. 20072147), the Program for Liaoning Excellent Talents in University (No. 2008RC56), the Open Research Fund of Nation Mobile Communications Research Laboratory of Southeast University (No. W200812), the Scientific Research Development Fund of Dalian Navy Academy (No. 2010003). REFERENCES [1] Guey J-C, Fitz M, Bell M and Kuo W-Y. “Signal design for transmitter diversity wireless communication systems over Rayleigh fading channels,” IEEE Trans. Commun., vol. 47, no. 4, pp. 527-537, Apr. 1999. [2] Rappaport T S. “Wireless Communication: Principles and Practice,” New Jersey, USA: Prentice Hall, Englewood Cliffs, 1996. [3] Telatar I E. “Capacity of multi-antenna Gaussian channels,” Eur. Trans. Telecomm., vol. 10, no. 6, pp. 585-595, Jul. 1999. [4] Smith P J and shafi M. “Water-filling methods for MIMO systems,” 3rd Australian Communication Theory Workshop, Canberra, Australia, 2002. [5] Winters J H. “The diversity gain of transmit diversity in wireless systems with Rayleigh fading,” Proc. IEEE Int. Conf. Communications, vol. 2, New Orleans, LA, MAY 1994, pp. 1121- 1125. [6] Nosratinia A, Hunter T E and Hedayat A. “Cooperative communication in wireless networks,” IEEE Commun. Mag., vol. 42, no. 10, pp. 74-80, Oct. 2004. [7] Sendonaris A, Erkip E and Aazhang B. “User cooperation diversity part II: Implementation aspects and performance analysis,” IEEE Trans. Commun., vol. 51, no. 11, pp. 1939-1948, Nov. 2003. [8] Janani M, Hedayat A, Hunter T E and Nosratinia A. “Goded cooperation in wireless communications: Space-time transmission and iterative decoding,” IEEE Trans. Signal Process., vol. 52, no. 2, pp. 362-371, Feb. 2004. [9] Dahlman E, Ekstrom H, Furuskar A and et al. “The 3G long-term evolution-radio interface concepts and performance evaluation,” Proc. IEEE 63rd Veh. Technol. Conf., vol. 1, MAY. 2006, pp: 137-141. [10] Daniele Piazza, Michele D’Amico and Kapil R. Dandekar. “Performance Improvement of a Wideband MIMO System by Using Two-Port RLWA,” IEEE Antennas and Wireless Propagation Letters, vol. 8, pp. 830-834, 2009. Figure 4. Structure of adaptive antenna array systems 2010 2nd International Conference on Signal Processing Systems (ICSPS) V1-841 Authorized licensed use limited to: IEEE Xplore. Downloaded on October 22,2011 at 06:40:34 UTC from IEEE Xplore. Restrictions apply.