TIME SYNCHRONIZATION AND LOW COMPLEXITY DETECTION FOR HIGH SPEED WIRELESS LOCAL AREA NETWORK

1
TIME SYNCHRONIZATION AND LOW COMPLEXITY DETECTION
FOR HIGH SPEED WIRELESS LOCAL AREA NETWORK
V. Sathish, 2004438105 Supervisor Dr.S.Srikanth
AU-KBC Research centre, MIT Campus, Chennai,
India
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Timing synchronization in IEEE 802.11n systems
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Presentation Outline

• Abstract
• IEEE 802.11n standard, goals and its challenges
• Review of IEEE 802.11a preamble and its usage
• 802.11n operating modes and frame formats
• Timing synchronization
• Literature survey
• Proposed coarse timing estimation
• Proposed fine timing estimation
• Simulation setup and results discussion
• Conclusion

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Abstract

• A low complexity timing synchronization method
  for the systems leased on the MIMO-OFDM1 based
  802.11n standard is proposed
• Two high throughput operating modes in IEEE
  802.11n
• Mixed mode where 802.11a/g legacy systems and
  802.11n based MIMO-OFDM systems shall co-exist
• Greenfield mode where only 802.11n enabled
  MIMO-OFDM systems exists
• For timing synchronization purposes,
• Mixed mode short training field (STF) and long
  training field (LTF) in preamble
• Greenfield mode Only short training field in
  preamble
• Essentially, two time sync algorithms are needed
  for MIMO modes
• Proposed algorithm uses only STF for timing
  synchronization and achieves same performance as
  LTF based algorithm
• The STF structure is same on both the modes, so a
  single time sync algorithm can be implemented for
  all the high throughput modes.

1MIMO-OFDM Multiple input multiple output
Orthogonal frequency division multiplexing
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WLAN standards

• Wi-Fi standards
• - IEEE 802.11 standard, 1997 2 Mbps, 2.4GHz,
  CSMA/CA
• - IEEE 802.11b std, 1999 11 Mbps, 2.4GHz,
  CSMA/CA
• - IEEE 802.11a std, 1999 54 Mbps, 5GHz,
  CSMA/CA
• - IEEE 802.11g std, 2003 11 Mbps 54 Mbps,
  2.4 GHz, CSMA/CA
• - IEEE 802.11n draft, 2006 500 Mbps, 2.4 GHz,
  CSMA/CA

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802.11n standard Goals and its challenges

• Achieve higher data rates (around 500 Mbps)
• Use of MIMO-OFDM technology
• Supports 20MHz and 40 MHz bandwidth operation
• Interoperable with 802.11a/g legacy systems
• Increased complexity
• Multiple radio frequency (RF) and baseband (BB)
  chains required
• Spatial detection techniques
• Backward compatibility
• MIMO-OFDM system should be able to decode the
  legacy packets
• Legacy system should atleast know about the
  MIMO-OFDM transmission to avoid collision
• Design of preamble impacts on initial receiver
  tasks

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Review of IEEE802.11a frame
Short symbols 1. Start of packet (SOP)
detection 2. Automatic gain control (AGC) 3.
Coarse timing estimation 4. Coarse frequency
offset estimation
Receiver tasks
Long symbols 5. Fine timing estimation 6. Fine
frequency offset estimation 7. Channel estimation
8. Data detection
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Initial receiver tasks
AGC Synchro. Mode
Acquisition mode
Start of packet
Packet detected
End of packet
Time frequency Acquired
Data detection
Ch. Estimation Mode
Correction Tracking mode
Offset update
Channel estimated
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802.11n frame formats
Non-High Throughput frame format

• Used in the legacy network where only the
  802.11a/g enabled devices are present
• Content is identical to the frame defined in
  the IEEE 802.11a standard
• STF Short training field
• LTF Long training field

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802.11n frame formats contd.
High throughput mixed frame format
DATA

• High throughput stations and legacy stations
  shall co-exists
• MIMO stations should transmit and receive the
  legacy frames and HT frames
• For compatibility reasons, Initial preamble part
  is provided with the first three fields of non-HT
  preamble
• HT-SIG, HT-STF and HT-LTFs are used decoding the
  MIMO packets
• If the tranmission is intended for MIMO_OFDM
  system, then based on the number of TX antennas
  cyclic shift is applied as shown in table1

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802.11n frame formats contd.
High throughput Greenfield frame format

• Only HT MIMO-OFDM stations can exist
• All the training fields specific to MIMO-OFDM
  systems
• HT-STF is identical to the L-STF field of mixed
  mode and is used for timing acquisition, AGC and
  frequency acquisition
• For TH-SIG demodulation, channel estimates are
  obtained from first HT_LTF fields
• Remaining HT-LTFs are used for estimating the
  channels across multiple transmit and receive
  antennas
• Frames in different TX antennas are cyclically
  shifted based on table2 before transmission

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Cyclic shift for HT frame transmission
Table1. Cyclic shift for the non-HT portion of
the packet
Table2. Cyclic shift for the HT portion of the
packet
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For Backward compatibility
Mixed mode
Legacy mode

• Preamble should be
• compatible to legacy stations
• Should work better for
• MIMO-OFDM systems

Only frames in legacy format
Green field mode
Preambles that are specific to MIMO-OFDM systems
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Typical 802.11n network
Mixed mode
Legacy mode
Green field mode
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Typical MIMO-OFDM system model
NtxNr MIMO-OFDM system
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Received signal model
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Timing synchronization

• Timing synchronization
• To estimate the sampling time of the OFDM symbol
• The start of OFDM symbol varies based on the
  strongest path of the fading channel
• Non-optimal sampling causes ISI and ICI
• Done in two steps
• Coarse timing offset (CTO) estimation
• Fine timing offset (FTO) estimation
• Coarse timing offset estimation
• Rough estimate is obtained
• After start of packet detection and AGC, timing
  estimator is triggered
• Fine timing offset estimation
• Optimal starting of OFDM symbol is obtained

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Literature survey

• In 4, T. M. Schmidl and D.C. Cox had proposed a
  maximum likelihood (Ml) synchronization timing
  estimation method for a SISO-OFDM system.
• An extension of this method for MIMO-OFDM system
  was proposed in 5 by A. N. Mody and G.L.
  Stuber, and in 6 by A. Van Zelst and Tim C. W.
  Schenk.
• The drawback of these methods is that the
  preambles assumed in the papers are not the same
  as in the 802.11n standards.
• In 7, Jianhua Liu and Jian Li presented a
  timing synchronization technique for a preamble
  that is similar to the one in the 802.11n
  standard.
• However, the computational complexity of this
  method is high due to the cross correlation
  performed on the LTF for fine timing estimation.

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Coarse timing offset estimation

• The objective of the CTO estimator is to find the
  rough starting position of any of the short
  symbol
• Typically 5-6 blocks of SS is taken for AGC
  operation
• Coarse timing estimation can be performed only
  after AGC convergence.
• An easy way is to find the end of the STF by
  using the autocorrelation property of the
  received signal.

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Proposed Coarse timing offset estimation
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Proposed coarse timing estimation technique
Step1

• A metric is calculated from the instant k at
  which the AGC is converged
• This metric is similar to the one in 7 and is
  given as

where
and
is the value of the cross correlation between the
signal and noise terms
is the sum of noise energy and value of cross
correlation between the signal and noise terms
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Proposed CTO estimator contd..
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Plot of metric1
Threshold based detection
Reference for metric
The falling end of plateau is noisy and getting
a coarse timing estimates will be erroneous
Metric
Metric forms a Plateau - 2x2 system under the
channel D with SNR10dB
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Proposed CTO estimator contd.
Step2
The metric is given as
The total averaged power of the difference signal
will increase as n increases. This is because of
the contributions from LTF
A smoothing operation is done on the metric by
weighted averaging and is given as
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Plot of CTO metrics
Metric plotted for a 2x2 system under the channel
D without noise
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Proposed CTO estimator contd.
The intersection point between these two metrics
is estimated as the coarse time
At low SNR, both the metrics will be noisy and
fluctuating and this would result in wrong
estimate
There might more than one intersecting point due
to fluctuations

To avoid this a simple condition is proposed
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Plot of metrics
Reference for metric 1
Metric 1
2x2 MIMO-OFDM system Channel model D SNR10dB
Reference for metric 2
Metric 2
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Proposed Fine timing offset estimation
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Proposed fine timing estimator
The objective of the fine timing offset estimator
is to find the exact start of the OFDM symbol
In multipath channel conditions this might not be
possible because the strongest path could occur
at non-zero delays
In the proposed FTO estimator, we find an index
in the starting of the 9th SS where the sum of
channel impulse response energy is maximum
between the receive antenna and transmit
antenna This is achieved by using the
correlation property of the STF and the
advantages of the cyclic shift
Achieved in two steps
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Proposed FTO estimator contd.
Step1
A simple cross correlation is performed between
the received signal and the transmit signal
Since the received signal at each receive antenna
contains multiple versions of the transmit signal
in cyclically shifted manner, the cross
correlation between the received signal and the
transmit signal will result in multiple peaks
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Proposed FTO estimator contd.
Each peak corresponds to the total channel energy
between transmit and receive antennas
The position corresponding to the first peak of
the first receive antenna output sequence is the
fine timing estimate
For example
Let us assume the coarse timing estimate
and all the channel impulse responses
have the strongest path at zero delay
For the 4x4 mixed mode system
The cross correlation output between the
first transmit antenna signal and the first
receive antenna signal will have 4 peaks placed
consecutively from
Detecting the first peak is quite tricky due to
multiple peaks that corresponds to different
channel power between transmit and receive
antennas
To choose the first peak, we propose a simple
technique
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Cross correlated output - Example
For a 4x4 system
Antenna3
Antenna1
Antenna2
Antenna4
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Proposed FTO estimator contd.
Step2
The cyclic shift 50us, 100us, 150us and 200us
applied at the transmit antenna corresponds to
numerical shift 15, 14, 13 and 12 that is applied
at the correlated output obtained from different
transmit signals.
The index corresponding to the maximum of
absolute of the metric is determined as the fine
timing offset.
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Complexity analysis
In case of the conventional LTF based FTO
estimator, the complex cross correlation should
be performed between 64 samples length long
symbol and the received signal.
In the proposed FTO estimator, the cross
correlation is performed between 16 samples
length short symbol and the received signal
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Simulation and performance analysis
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Performance of coarse timing estimator

• Probability distribution of CTO estimate is
  plotted
• Compared to the performance of threshold based
  technique
• System model
• 2x2, 3x3 and 4x4 antenna configuration
• MIMO Channel model
• TGn channel models
• SNR 8dB

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Parameters of coarse timing estimator

• For threshold based technique as in 7
• Mixed mode and green field mode
• Threshold c20.6 and Q215 samples
• For proposed technique
• Mixed mode and green field mode
• Threshold 0.45 and Q8 samples
• Smoothing filter weight 0.5 for both the metrics

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Probability of coarse timing offset estimate of
conventional and the proposed technique.
Probability of getting zero CTO is high for the
algorithm proposed in threshold based technique
Significant probability of the CTO obtained using
this algorithm is going beyond the defined
estimation accuracy
In the proposed algorithm, estimates are more
stable and lie within the estimation range
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Comparison of probability of CTO estimates for
different antenna configurations
Probability of CTO estimates within the
estimation accuracy
As the number of antenna increases, the spatial
diversity is leveraged resulting in a better
performance for higher antenna configuration
Proposed algorithm performs better at the lower
SNR values as compared to the CTO estimation
algorithm in 7
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Impact of channel models
Probability of CTO estimates within the
estimation accuracy for proposed algorithm in
different channel models
The maximum probability is achieved at 10dB SNR
for a 2x2 system
Motivation to use only the STF for the fine
timing offset estimation
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Performance of fine timing estimator

• Probability distribution of fine timing estimate
  is plotted
• Compared to the performance of simple cross
  correlation based technique using LTF
• System model
• 2x2, 3x3 and 4x4 antenna configurations
• MIMO Channel model
• TGn channel models

42
Comparison of probability of FTO estimates with
LTF based FTO estimator
The estimation accuracy is defined with the range
0, 3.
Computationally complex LTF based FTO LTF will
have slightly better performance as compared to
proposed technique
Due to better noise averaging
The probabilities of the FTO estimates within the
estimation accuracy is plotted for the 3x3 and
4x4 systems of mixed mode.
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Conclusion

• A low complexity time synchronization algorithm
  is proposed
• The proposed techniques performs better even at
  lower SNRs.
• Using only STF, a single coarse and fine timing
  estimation technique will be used for both the
  high throughput modes
• Same performance is achieved as LTF based timing
  synchronization
• Thereby reducing total complexity of the system

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References

• 1. IEEE P802.11n/D2.00, Draft standard for
  Information Technology-Telecommunications and
  information exchange between systems-Local and
  metropolitan area networks-Specific
  requirements-, Feb 2007
• 2. IEEE 802.11a standard, ISO/IEC
  8802-111999/Amd 12000(E), http//standards.ieee.
  org/getieee802/download/802.11a-1999.pdf
• 3. IEEE 802.11g standard, Further Higher-Speed
  Physical Layer Extension inthe2.4GHzBand,
  http//standards.ieee.org/getieee802/download
  /802. 11g-2003.pdf
• 4 T. M. Schmidl and D.C. Cox, Robust
  Frequency and Timing Synchronization for OFDM,
  IEEE Trans. on Communications, vol. 45, no. 12,
  pp. 1613-1621, Dec. 1997.
• 5. A. N. Mody and G.L. Stuber,
  Synchronization for MIMO-OFDM systems, in Proc.
  IEEE Global Commun. Conf., vol. 1, pp.509-513,
  Nov.2001
• 6 A. Van Zelst and Tim C. W. Schenk,
  Implementation of MIMO-OFMD based Wireless LAN
  systems, IEEE Trans. On Signal Proc. Vol. 52,
  No.2, pp. 483-494, Feb 2004
• 7 Jianhua Liu and Jian Li, A MIMO system with
  backward compatibility for OFDM based WLANs,
  EURASIP journal on Applied signal processing. Pp.
  696-706, May 2004
• 8 IEEE P802.11 TGn channel models, May 10
  2004,http//www.ece. ariz ona.edu/yanli/files/11
  -03-0940-04-000n-tgn-channel-models.doc

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Low Complexity MIMO-OFDM System for High Speed
WLANs
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Presentation Outline

• Introduction
• System model and channel model
• MIMO-OFDM1 detection techniques
• Proposed Group ordered MMSE V-BLAST2 detection
• Simulation results
• Conclusion

1MIMO-OFDM Multiple input multiple output
Orthogonal frequency division multiplexing
2MMSE V-BLAST Minimum mean square error
Vertical bell labs layered space time system
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Introduction

• MIMO-OFDM is a promising technique to increase
  data transmission rate in wireless frequency
  selective fading channels1,2
• The key technique behind the MIMO-OFDM system is
  the spatial detection at the receiver

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802.11n MIMO-OFDM baseband transmitter
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802.11n MIMO-OFDM baseband receiver
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Signal model and MIMO channel
Received signal
After removing cyclic prefix and FFT operations,
the received signal vector corresponding to
subcarrier (bar over a variable represents vector)
(1)
where
Transmit signal vector
Additive white Gaussian Noise
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Signal model and MIMO channel
Channel matrix at the subcarrier

• MIMO detection is done in all the subcarriers in
  a similar fashion.
• For simplicity, we drop the index k and the
  received signal is given as
• The elements in are independent and
  identically distributed (iid) zero mean and
  circularly symmetric complex Gaussian random
  variables with variance

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MIMO Detection Techniques
MIMO Detection techniques
Non-linear (ML) Low BER High complexity
Embedded (V-BLAST) Low BER Moderate complexity
Linear (MMSE, ZF) High BER Low complexity
Modified
Group ordered MMSE V-BLAST Low BER Low
complexity
Proposed system
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MIMO Detection Techniques

• ML Detection

Complexity , M is the order of
the constellation

• Zero Forcing

• MMSE

where
where
Complexity
Complexity
Noise variance computation is an overhead
Noise enhancement
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MIMO Detection Techniques
Successive Interference cancellation (SIC)
With ordering Order of detection based on SINR,
stream with largest SINR is selected in each
iteration 4 (V-BLAST with MMSE/ZF solution)
Without ordering Order of detection is selected
randomly
MMSE V-BLAST

• Combined MMSE and iterative SIC
• Transmit signal from each antenna is detected
  at each iteration
• Interference due to the detected signal is
  cancelled form
• the received signal
• Repeat the iteration until all the signals are
  transmitted

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MMSE V-BLAST
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MMSE V-BLAST Algorithm

• Obtain MMSE solution
• Find the detection order using the criterion
  below 5
• Initial Nulling and detection
• Interference cancellation
• Recursion
• Obtain new by replacing the column of
  with zeros. Repeat from step 1 until all the
  streams are detected.

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Group Ordered MMSE V-BLAST

• Concept of proposed detector
• Group the streams that face similar channel
  conditions
• Use same MMSE solution to detect all the streams
  in that group
• SIC is applied inside and across the groups
• Since the MMSE solution is calculated for each
  group,
• there is a reduction in the complexity of
  detection.
• GO MMSE V-BLAST can be implemented in 2 ways
• 1. Fixed method
• 2. Adaptive method

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Group ordered MMSE- V-BLAST (fixed)
59
Group ordered MMSE- V-BLAST (fixed)
Algorithm
(2)

• Grouping
• Group1 Streams corresponding to
• Group2 Streams corresponding to

• Cancel the interference due to from

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Group ordered MMSE- V-BLAST (fixed)
61
Group ordered MMSE- V-BLAST (adaptive)
Algorithm

• Cancel the interference due to from

Repeat from step 3 until all the streams are
detected
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Simulation and Discussion

• Uncoded system
• Number of transmit antennas 4
• Number of receive antennas 4
• Modulation QPSK
• Number of subcarriers 64
• Cyclic prefix length 16 samples
• MIMO channel TGn channel model D
• Max Delay spread of channel D 390ns
• Spatial distance between antennas 0.5
• For adaptive scheme thres 1.75 and 2

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GO MMSE V-BLAST (fixed)

• In uncoded MIMO-OFDM
• system the fixed group
• ordering performs better than MMSE

• The computations required is
• slightly more than MMSE but
• less than MMSE V-BLAST

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GO MMSE V-BLAST (Adaptive)

• In uncoded MIMO-OFDM
• system the adaptive group
• ordering almost approaches the performance of
  original V-BLAST

• As thres value decreases, the performance
  approaches the MMSE V-BLAST

• When thres1, the performance of proposed scheme
  is similar to MMSE V-BLAST

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Performance under various channel models

• SNR at BER10-4 for fixed scheme and adaptive
  scheme under all the channel models

• In channel C and B, the system performs poorly
  due its high condition numbers

• Performance of the system in
• the most representative channel
• model D is good.

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Coded GO MMSE VBLAST

• 4x4 MIMO OFDM system from EWC proposal for
  802.11n standardization 7
• Convolutional encoder with coding rate ½
• Interleaving across the streams and across
  subcarriers
• QSPK Modulation
• Uses 56 subcarriers for useful data with 16
  samples as cyclic prefix length
• Channel model D with maximum delay spread of 390
  ns
• Spatial distance between antennas 0.5
• For adaptive scheme thres 1.75 and 2

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Coded GO MMSE VBLAST (fixed)

• In coded MIMO-OFDM system the fixed group
  ordering performs better than MMSE and is very
  close to MMSE V-BLAST

• Coding and interleaving exploits the frequency
  diversity and provides this performance

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Coded GO MMSE VBLAST (Adaptive)

• In coded MIMO-OFDM
• system the adaptive group
• ordering performs similar to original V-BLAST

• When thres1, the performance of proposed scheme
  is similar to the performance MMSE V-BLAST

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Performance under various channel models

• SNR at BER10-4 for fixed scheme and adaptive
  scheme under all the channel models

• In channel C and B, the system performs poorly
  due its high condition numbers

• Performance of the system in
• the most representative channel
• model D is good.

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Complexity Comparison
Proposed fixed scheme requires 3360 extra
computations compared to MMSE
For Adaptive schemes, they are computations are
variable for each subcarrier.
And as threshold decreases, the computations
required also increases.
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Conclusion

• A Group ordered MMSE V-BLAST with low complexity
  has been proposed.
• The complexity required for the proposed system
  is slightly larger than the MMSE but less when
  compared to MMSE VBLAST.
• The performance difference between group ordered
  and MMSE V-BLAST is slightly large in uncoded
  system whereas in coded system difference merges.
• The proposed technique can be potentially used as
  a detection technique for high speed WLANs

72
Acknowledgement

• The authors like to acknowledge AAU-CSys and
  FUNDP-INFO for providing the MATLAB
  implementation of the IEEE 802.11 HTSG channel
  model. They would also like to thank Professor
  Laurent Schumacher for guiding in channel model
  simulations.

73
References

• 1. G. J. Foschini and M. J. Gans, On the
  limits of wireless communications in a fading
  environment when using multiple antennas,
  Wireless Personal Communications, vol. 6, no. 3,
  pp. 311-335, 1998.
• 2. http//www.wwise.org/11-05-0149-01-000n-wwise
  - proposal- high-throughput-extension-to-802-11-
  standard.doc
• 2. LaurentSchumacher, Klaus I. Pedersen, Preben
  E. Mongensen, From antenna spacings to
  theoretical capacities guidelines for
  simulating MIMO systems, Proc. PIMRC 2002, pp.
  587- 592, vol.2,
• 3. P. W. Wolniansky, G. J. Foschini, G. D.
  Golden and R. A. Valenzuela, V-BLAST an
  architecture for realizing very high data rates
  over the rich-scattering wireless channel, in
  Proc. ISSSE, pp. 295-300, 1998
• 4. Babak Hassabi, A efficient square root
  algorithm for BLAST, Proc. International
  Conference on Acoustics, Speech and Signal
  Processing 2000, pages 737-740.
• 5. IEEE P802.11 TGn channel models, May 10
  2004,http//www.ece. ariz ona.edu/yanli/files/11
  -03-0940-04-000n-tgn-channel-models.doc
• 6. http//www.enhancedwirelessconsortium.org/hom
  e/EWC_PHY_spec_V113.pdf

74
Publications

• V.Sathish, S.Srikanth, Low complexity MIMO
  detection technique for high speed WLANs, pp.
  63-67, Proc. National Conference RF Baseband
  systems for wireless applications, TIFAC core,
  Madurai, India, Dec 11-12, 2005.
• Published a tutorial in www.wirelessnetdesignline.
  com with title Tutorial on IEEE 802.11n systems

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Thank you