Vehicle to Vehicle communication

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• Vehicle to Vehicle communication
• Marie Claire Naima Raynal
• Group No. 07gr1119
• August 2007
• Aalborg University

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Introduction I/2

• 60 roadway collisions could be avoided if
  drivers were warned ½ seconds prior to collision.
• Avoid car accidents and traffic congestions
• Anti-collision detection
• Online information about the actual traffic
  conditions
• Detailed information about the road conditions
  ahead

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Introduction 2/2

• MIMO channel Narrowband
• MIMO channel Wideband
• Narrowband vs. Wideband
• Singular Value Decomposition
• Maximum eigen beanforming
• Time Reversal
• Time Reversal vs. Singular Value Decomposition
  on spatial focusing

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MIMO technique

• Spatial multiplexing allows multiple distinct
  data streams to be transmitted at the same
  frequency but over different spatial channels.
• Getting the Most out of MIMO Boosting Wireless
  LAN Performance with Full Compatibility, Atheros
  communication

multiple receiving antennas can recover these
data streams
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Spatial multiplexing advantages

• Creates multiple parallel independent channels
• Recognizes the unique codes of these independent
  paths
• Achieve very high spectral efficiency Increase
  the channel capacity without increasing the
  bandwidth or transmitted power
• Spatial multiplexing will be a mandatory element
  in the 802.11n standard.

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MIMO channels wideband

• The signal is transmitted along
• different propagation paths which increases its
  chance of
• being received by the receiver

In wideband MIMO system, the channel is modeled
by a number matrices.
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MIMO channels wideband
x(t) signal sent from the transmitting antenna
is additive gaussian noise at the receiver
represents the convolution operator
elements of the composite MIMO channel response
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MIMO channels wideband

• Our wideband channel is coping with
• Delay Spread type of distortion that is caused
  when
• identical signal arrives at different times at
  its destination
• ISI overlap of individual pulses
• Scattering environment such as rough vehicle
  and
• roadside which causes a big shift in phase of the
  wave
• Frequency selective fading partial cancellation
  of the signal
• by itself

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MIMO channels wideband
Scattering area of the wideband channel
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MIMO channels wideband
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MIMO channels wideband
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MIMO channels narrowband
In narrowband MIMO system, the channel is modeled
by a single matrix.
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MIMO channels narrowband

• Frequency flat fading
• the same degree of fading takes place for all of
  the frequency
• That is, all the frequency components of the
  transmitted signal
• rise and fall in unison.

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MIMO channels narrowband
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Singular Value Decomposition

• H represents the noisy signal can be diagonalized
  using the SVD technique
• U and V are unitary matrices
• ? is the ltNt x Nrgt diagonal matrix containing
  non-negative singular values
• (.)H means complex conjugate transpose

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Singular Value Decomposition

• Singular values
• Eigen values of and

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Singular Value Decomposition
parallel channels can be realized
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Singular Value Decomposition
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Singular Value Decomposition

• By using the weight matrix UH at Tx and the
  weight matrix
• at the receive side VH the received symbol
  becomes
• After being weighted by V, the variance of the
  noise vector
• n is the same since V is an unitary matrix
• This equation implies that the power put into K
  parrallel
• channels are amplified by the eigenvalues power
  put into
• channels which have indices larger than K will be
  lost

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Singular Value Decomposition
At one frequency tone, the channel matrix of the
kth antenna k (1...Nr), is denoted as Hk and
the received signal of the jth intended user is
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Singular Value Decomposition

• The weight vectors and at the
  antenna ports form
• the transmitting and receiving eigenpatterns
• The singular vector will shape the
  eigenpatterns in an effort
• to maximize the channel gain

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Maximum eigen beamforming approach
For a single user MIMO system with Nt
transmitting antennas, Nr receiving antennas
the SIR can be estimated as The eigenvectors
act as the steering vectors which steer the beam
pattern toward the direction radiating maximum
energy. Therefore, the maximum eigen beamforming
creates some sort of spatial focusing with the
resolution and the signal to interference ratio.
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Maximum eigen beamforming 4x8
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Maximum eigen beamforming 4x2
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Maximum eigen beamforming 2x4
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Maximum eigen beamforming conclusion

• The power distributed to the rest Nt-Nr
• ports is lost
• All transmitted power is received in a
• MIMO NtltNr
• This gives rises to an increment in the
• channel capacity of the MIMO NtltNr
• system over that of MIMO NtgtNr

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Time Reversal advantages

• Temporal focusing reduce Delay Spread
• SISO TR
• MISO TR fully
  correlated
• MISO TR fully
  uncorrelated Rayleigh
• Spatial focusing the power peaks at the
  intended receiver and decays rapidly away from
  the receiver, results in very low co-channel
  interference and in a very efficient use of
  bandwidth in the overall system
• Channel hardening channel statically stable,
  results in high diversity gain

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Time Reversal
Phase 1 The transmitter learns the channel
impulse response I and j are the indices for
transmitting and receiving antenna
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Time Reversal
Phase 2 Each transmitter applies a filter and
sends the same data stream from all the elements
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Time Reversal
x(t) denotes the transmitted signal y(t)
indicates the received signal
represents the convolution operator denotes
the complex conjugate operator is the
noise component
is the autocorrelation of the CIR
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Time Reversal

• Time Reversal in Wireless Communications A
  Measurement-Based Investigation, Hung Tuan Nguyen

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Time Reversal
Received signal at an off-target point i.e at a
different point than jth which is defined as one
of the receiving antenna
denotes the IR of the channel from the
transmitting point to the off-target point
is the cross correlation of the CIR
to the target point and the IR
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Time Reversal
is the signal of interest
is the interfering signal
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Time Reversal
spatial focusing capability of TR can be defined
by how much the interference from other users or
antennas can be mitigated. The spatial focusing
potential is characterized by the SIR
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Time Reversal 8x8
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Time Reversal 8x4
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Time Reversal Conclusion

• The interference power increases according to the
  number
• of the receiving antennas
• However with resonably smaller number of Rx
  than Tx and
• a rich multipath environment the desired signals
  
• magnitude might become larger than that of the
  interference

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Conclusion Futur work

• TR outperforms the SVD technique in the spatial
  focusing
• perspective in the case of 8x4
• It would have been nice to compare a 8x1 MIMO TR
  with a
• 8x 1, 8x2,MIMO SVD technique in terms of
  complexity of
• the receiver
• But we have seen that a NtgtNr MIMO SVD does not
  bring a
• lot of capacity so it can be concluded that
• TR is a high technique to ISI without the need of
  high
• complexity receiver
• TR approach for multi-user UWB communications