Antenna selection and rf processing for mimo systems
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Antenna selection and RF processing for MIMO systems. July 2004 Andreas F. Molisch, Neelesh B. Mehta, Jianxuan Du, and Jin Zhang MERL (Mitsubishi Electric Research Labs), Cambridge, MA, USA (contact: [email protected]). Contents. System model Performance analysis

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Antenna selection and rf processing for mimo systems

Antenna selection and RF processing for MIMO systems

July 2004

Andreas F. Molisch, Neelesh B. Mehta, Jianxuan Du, and Jin Zhang

MERL (Mitsubishi Electric Research Labs), Cambridge, MA, USA

(contact: [email protected])

A. F. Molisch et al, MERL


Contents

Contents

  • System model

  • Performance analysis

  • Antenna selection algorithms

  • Effect of nonidealities

  • RF preprocessing

  • Summary and conclusions

A. F. Molisch et al, MERL


Antenna selection

Antenna selection

  • Additional costs for MIMO

    • more antenna elements (cheap)

    • more signal processing (Moore’s law)

    • one RF chain for each antenna element

  • Basic idea of antenna selection:

    • have many antenna elements, but select only best for downconversion and processing

    • only at one link end: cost reductions might be more important at one link end (MS) than the other

  • Hybrid antenna selection: select best L out of available N antenna elements, use those for processing

A. F. Molisch et al, MERL


System model

System model

A. F. Molisch et al, MERL


Contents1

Contents

  • System model

  • Performance analysis

  • Antenna selection algorithms

  • Effect of nonidealities

  • RF preprocessing

  • Summary and conclusions

A. F. Molisch et al, MERL


Antenna and weight selection for diversity

Antenna and weight selection for diversity

  • Weight selection if all antenna elements are used

  • Write channel matrix as

  • Excite channel with Vi, receive with UiH

  • Received power is i2

  • Antenna and weight selection for H-S/MRT

  • Create submatrices by striking rows

  • Compute maximum singular value for this submatrix

  • Search submatrix that gives largest max. singular val.

  • Use singular values associated with selected submatrix as antenna weights

A. F. Molisch et al, MERL


Hs mrt vs mrt diversity case

HS-MRT vs. MRT – diversity case

A. F. Molisch et al, MERL


Antenna selection for space time codes

Antenna selection for space-time codes

  • knowledge at transmitter only about statistics of fading

  • performance:

    • full diversity order

    • loss of coding gain

  • in correlated systems:

    • select antennas so that determinants of correlation matrices at TX and RX are maximized

A. F. Molisch et al, MERL


Antenna selection for spatial multiplexing

Antenna selection for spatial multiplexing

Capacity for full-complexity system

Antenna selection for H-S/MRT

  • Create submatrices by striking rows

  • Compute capacity according to Foschini equation

  • Search the submatrix that gives largest capacity

A. F. Molisch et al, MERL


Capacity spatial multiplexing

Capacity – spatial multiplexing

3 transmit antennas, 20dB SNR

A. F. Molisch et al, MERL


Contents2

Contents

  • System model

  • Performance analysis

  • Antenna selection algorithms

  • Effect of nonidealities

  • RF preprocessing

  • Summary and conclusions

A. F. Molisch et al, MERL


Antenna selection algorithms

Antenna selection algorithms

  • truly optimum selection:

    • exhaustive search

    • effort proportional to

  • approximate methods:

    • power-based selection: works well for diversity antennas, but not for spatial multiplexing

    • selection by genetic algorithms

    • maximize mutual information between antenna elements

    • Gorokov’s method

A. F. Molisch et al, MERL


Maximization of mutual information

Maximization of mutual information

A. F. Molisch et al, MERL


Contents3

Contents

  • System model

  • Performance analysis

  • Antenna selection algorithms

  • Effect of nonidealities

    • channel correlation

    • channel estimation errors

    • hardware

  • RF preprocessing

  • Summary and conclusions

A. F. Molisch et al, MERL


Channel correlation diversity

Channel correlation - diversity

A. F. Molisch et al, MERL


Channel estimation error diversity

Channel estimation error - diversity

A. F. Molisch et al, MERL


Hardware aspects

Hardware aspects

  • attenuation of switches

    • either decrease the effective SNR,

    • or LNA has to be before switchrequires more LNAs

  • switching time: has to be much smaller than duration of training sequence

  • accuracy of switch: transfer function has to be same from each input to each output port

A. F. Molisch et al, MERL


Contents4

Contents

  • System model

  • Performance analysis

  • Antenna selection algorithms

  • Effect of nonidealities

  • RF preprocessing

  • Summary and conclusions

A. F. Molisch et al, MERL


Rf pre processing followed by selection

Demodulator

Baseband

RF

S

W

I

T

C

H

1

1

Sig

Proc

A/D

LNA

2

Pre-

Proc

(M)

.

.

.

.

Down

Conv

Down

Conv

L

A/D

LNA

Nr

RF Pre-Processing Followed by Selection

  • Diversity gain AND Beamforming gain maintained

  • Beam selection Vs Antenna Selection

  • Can be implemented using variable phase-shifters

A. F. Molisch et al, MERL


What is the optimal rf pre processing

1

2

What is the Optimal RF Pre-Processing??

  • Instantaneous Channel Information

    • Orient the beams with the angle of arrival of the incoming rays

    • Require continuous updating of entries of pre-processor

  • No Channel Information

    • FFT based Pre-Processing

    • Simple

    • Beam pattern cannot adapt to the angle of arrival

A. F. Molisch et al, MERL


Channel statistics based pre processing

Channel Statistics-Based Pre-Processing

  • Pre-processor depends on channel statistics,

  • Orients the beam with the mean angle of arrival

  • Optimal Solution performs principal component decomposition on columns of H

  • Advantages

    • Continuous updating of entries of M not required

A. F. Molisch et al, MERL


Fft based pre processing

. . . .

. . . .

. . . .

FFT-Based Pre-Processing

  • Good gains if AoA is 0,60, 90, 300, 270

  • Static beam pattern: Cannot adapt to the mean AoA

. . . .

Beam Pattern as function of

Azimuth angle

A. F. Molisch et al, MERL


Channel statistics based pre processing1

Channel Statistics-Based Pre-Processing

Beam Pattern as function of Azimuth angle for mean AoA of 60 degrees

Beam Pattern as function of Azimuth angle for mean AoA of 45 degrees

A. F. Molisch et al, MERL


Summary and conclusions

Summary and conclusions

  • antenna selection retains the diversity degree, but SNR penalty

  • for spatial multiplexing, comparable capacity if LrNt

  • optimum selection algorithms have complexity N!/(N-L)!; however, fast, good selection algorithms exist

  • for low-rank channels, transmit antenna selection can increase capacity

  • channel estimation errors do not decrease capacity significantly

  • frequency selectivity reduces effectiveness of antenna selection

  • RF preprocessing greatly improves performance, especially in correlated channels

  • Covariance-based preprocessing especially suitable for frequency-selective channels

  • switches with low attenuation required both for TX and RX

    antenna selection is attractive for reducing hardware complexity in MIMO systems

A. F. Molisch et al, MERL


References

References

A. F. Molisch et al, MERL


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