II International Workshop on Challenges and Trends on Broadband Wireless Mobile Access Networks – Beyond LTE-A

1. CSI Acquisition for FDD-based Massive MIMO
Systems: Exploiting Sparsity and
Multidimensionality of the Wireless Channel
Andre L. F. de Almeida
Group of Wireless Communication Research-GTEL
Federal University of Ceara - UFC
November 18, 2014
Andre L. F. de Almeida CPqD 2014 1 / 39

2. Acknowledgements
Daniel Costa Araujo (PhD student)
Samuel Tumelero Valduga (PhD student)
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3. Scaling up MIMO systems
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4. Testebed for Massive MIMO
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5. Outline
1 Overview on Massive MIMO
2 Massive MIMO:TDD vs FDD
3 CSI Aquisition in FDD Massive MIMO
4 Multidimensional Channel Estimation
5 Conclusion
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6. Overview on Massive MIMO
Scaling up the number of antennas
Massive MIMO concept
Massive MIMO is an emerging technology that scales up the number of antennas by
orders of magnitude and achieving larger arrays than the current state-of-the-art
[Marzetta, 2010] [Rusek et al., 2013]. This means:
to deploy hundreds, or even thousands, of antennas at the BS;
cheap power ampli

7. er can be employed in the BS;
the BS is capable to create extremely narrow beamformers;
Motivation: Why should we scale up the number of antennas?
the mobile data trac will be 13 more than 2012.
2/3 of the total trac will be video streaming and communications.
The most modern standard, LTE-Advanced, allows for up to 8 antenna ports at the
base station and equipment being built today has much fewer antennas than that.
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8. Overview on Massive MIMO
MM-wave and Massive MIMO: Potential Application
Some interesting points
The power of the wireless signal in millimeter-wave attenuates quickly. This restricts
the range of the area to be covered.
There is a considerable amount of free spectrum around 60 GHz.
Very-large arrays are shrunk when using MM-waves transmission.
North America
Europe
Australia
Korea
Japan
56 57 58 59 60 61 62 63 64 65 66
Figure : Unlicensed Frequency Spectrum - GHz
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9. Massive MIMO:TDD vs FDD
Limiting Factor in TDD
BS1
h1;1
BS2
h h2;2 1;2
h2;1
Figure : TDD scenario
Issues
Pilot Contamination
Channel Reciprocity
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10. Massive MIMO:TDD vs FDD
Limiting Factor in TDD: Solutions
Channel Reciprocity: Solution
This is still an open issue.
Pilot Contamination: Some proposed Solutions
The allocation of pilot waveforms
Blind channel estimation techniques
Cooperative transmission
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11. Massive MIMO:TDD vs FDD
Limiting Factor in FDD scenario
Channel
Feedback Channel
Issues
Pilot overhead
Feedback overhead
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12. Massive MIMO:TDD vs FDD
Limiting Factor in FDD scenario: Solutions
Pilot Overhead
Compressive Sensing
Adaptive channel estimation (Low complexity)
Multidimensionality of the Channel
Feedback Overhead
Compressive Sensing
Matrix Completion
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13. CSI Aquisition in FDD Massive MIMO
A Very Brief Overview in Compressive Sensing
De

14. nition
Compressive sensing theory asserts that one can recover certain signals and images from
far fewer samples or measurements than traditional methods [Donoho, 2006] [Candes,
2006].
y = 2 CMN 2 CNN b 2 CN1
M N
Figure : Compressive sensing idea.
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15. CSI Aquisition in FDD Massive MIMO
Fundamental Result
m C2(; )K log(N) (1)
(; ) is the mutual coherence between the matrices and .
K is the number of non-zero entries in N 1 vector b.
C is a positive constant.
The smaller the mutual coherence is, the fewer samples are needed.
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16. CSI Aquisition in FDD Massive MIMO
mm-Wave and Massive MIMO
Channel Characterization
Short range communication.
Sparsity in the delay domain.
Applications typically do not involve high-velocity users (indoor scenarios).
Channel Estimation
LOS channel environment could allow for channel estimation based on direction-of-arrival
(DOA) estimation. Compressed sensing can be a very useful tool to apply such idea.
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17. CSI Aquisition in FDD Massive MIMO
mm-Wave and Massive MIMO: Some results
Scenario Description
60 GHz indoor channel.
OFDM modulation.
Multiple antennas at the UE.
The problem
We address the problem of estimating beamforming directions on the downlink in a 60
GHZ indoor channel.
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18. CSI Aquisition in FDD Massive MIMO
FDD Scenario
Scatter
Scatter
BS UE
Figure : Downlink transmission
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19. CSI Aquisition in FDD Massive MIMO
Related Work
Compressive Sensing Based Methods in Communication Systems
W.U. Bajwa, J. Haupt, A.M. Sayeed, and R. Nowak, Compressed channel sensing: A
new approach to estimating sparse multipath channels, Proc. of the IEEE, vol. 98, no.
6, pp. 1058{1076, 2010
Estimation techniques in MM-waves Channel
D. Ramasamy, S. Venkateswaran, and U. Madhow, Compressive tracking with
1000-element arrays:A framework for multi-gbps mm wave cellular downlinks, Proc.
Allerton, pp. 690{697, 2012.
D. Ramasamy, S. Venkateswaran, and U. Madhow, Compressive adaptation of large
steerable arrays, Proc. ITA, pp. 234{239, 2012.
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20. CSI Aquisition in FDD Massive MIMO
Our Proposal
Coarse
Estimation
Refinement
Figure : Block Diagram of Channel Estimation Method
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21. CSI Aquisition in FDD Massive MIMO
System Model
Received Signal
yr(k; l) =
XNp
n=1

22. nvR(R;n; R;n)vH
T (T;n; T;n)s(k; l)e|2nlf + z(k; l)
Steering Vector Model
[v
(
;n;
;n)]i = e|(!xxi+!yyi);
2 fR; Tg (2)
Variables Description
!x = 2d
cos (
;n) cos (
;n);
!y = 2d
cos (
;n) sin (
;n);
xi and yi de

23. ning the spatial position of the i-th antenna element on the plane xy;
d is the inter-element antenna spacing;
is the wavelength.
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24. CSI Aquisition in FDD Massive MIMO
Channel Estimation: Coarse Estimation
Coarse
Estimation
Refinement
Figure : Coarse Estimation stage
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25. CSI Aquisition in FDD Massive MIMO
Coarse Estimation
Probing directions
^p = max
p
X
k
X
l
kwH
p yr(k; l)k; p = 1; : : : ; P; (3)
r^p(k; l) = sT (k; l)V
TF^pb(l) + ~z^p(k; l); (4)
where
VT = [vT (T;1; T;1); : : : ; vT (T;Np ; T;Np )] ;
F^p is a diagonal matrix whose n-th diagonal element is given by
[F^p]n;n = wH ^p vR(R;n; R;n);
b(l) = [

26. 1e|21lf ; : : : ;

27. Npe|2Np lf ]T ;
~zp(k; l) = wH ^p z(k; l).
Stacking spatial samples into a vector
rp(l) = STl
V
TFpb(l) + ~zp(l); (5)
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28. CSI Aquisition in FDD Massive MIMO
Coarse Estimation: Angle of Departure
Compressive Sensing Estimation
min kb

29. lt(l)k1 s:t kr^p(l) STl
UTb

30. lt(l)k22
2: (6)
where UT is a Fourier matrix and b

31. lt(l) = Fpb(l).
BS UE
Figure : Coarse Estimation
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32. CSI Aquisition in FDD Massive MIMO
Re

33. nement of the Angles
Coarse
Estimation
Refinement
Figure : Re

34. nement stage
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35. CSI Aquisition in FDD Massive MIMO
Re

36. nement of the Estimates: Angle of Arrival
Re

37. nement Problem
[!ref
R;x(l); !ref
R;y(l)] = arg max
(!R;x;!R;y) 2 R2
J(!R;x; !R;y; l)
where J(!R;x; !R;y; l)
:=
XK
k=1
jwH(!R;x; !R;y)y(k; l)j2; (7)
Comments
w(!R;x; !R;y) is the steering vector associated with the pair (!R;x; !R;y).
The

38. nal estimates are given by averaging over the L subcarriers, i.e.
!ref
R;x = (1=L)
LP
l=1
!ref
R;x(l), and !ref
R;y = (1=L)
LP
l=1
!ref
R;y(l).
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39. CSI Aquisition in FDD Massive MIMO
Re

40. nement of the Estimates: Angle of Departure
Re

41. nement Problem
[!ref
T;x(l); !ref
T;y(l)] = arg max
!T;x;!T;y
jvH(!T;x; !T;y)S
l ybeam(l)j2; (8)
where ybeam(l) =
2
64
yT (0; l)
... yT (K 1; l)
3
75
w(!ref
R;x; !ref
R;y)
Comments
As for the receive spatial frequencies, the

42. nal estimates of !ref
T;x and !ref
T;y are obtained
by averaging over the L subcarriers.
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43. CSI Aquisition in FDD Massive MIMO
Re

44. nement of the Estimates:Figure
BS UE
Figure : Estimation after the re

45. nement
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46. CSI Aquisition in FDD Massive MIMO
Summarizing
Estimate coarsely angles of arrival
Estimate coarsely angles of departure
Re

47. ne angles of arrival
Re

48. ne angles of departure
Fed back the angles of departure to the BS
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49. CSI Aquisition in FDD Massive MIMO
Simulation Parameters: part I
Table : Simulation Parameters
Environment Indoor (LOS)
Carrier Frequency 60 GHz
Multiplexing Scheme OFDM
Subcarrier Bandwidth 360 kHz
Number of Subcarriers 512
System Bandwidth 0:18 GHz
Number of pilots Subcarriers 32
FFT size 1024
Payload period 1:389 s
Cyclic pre

50. x 347:22 ns
OFDM symbol period 3:1252 s
Maximum Tx Power per AN 2 mW
Thermal Noise Level 174 dBm/Hz
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51. CSI Aquisition in FDD Massive MIMO
Simulation Parameters: part II
Table : Simulation Parameters
Noise Figure 6 dB
Number of Tx Antennas 64
Number of Rx Antennas 16
Distance Between the Antennas =2
UE speed 1 m/s
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52. CSI Aquisition in FDD Massive MIMO
Throughput
Throughput
The proposed algorithms are evaluated using Shannon's capacity formula by considering
three beamforming schemes as follows:
SVD-based beamforming: derived from the right singular vector of the
frequency-dependent channel matrix (i.e. each subcarrier has dierent beamforming
weights). Perfect knowledge of the full instantaneous channel matrix is assumed;
Steering vector-based beamforming: designed from the only knowledge of the
estimated spatial frequencies, i.e. [v(!T;x; !T;y)]i = e|(!T;xxi+!T;yyi);
Round-phase beamforming: The design of the steering vector is constrained to four
dierent prede

53. ned phases only. Speci

54. cally, the phases associated with each entry of
the steering vector are rounded to the closest phase among the four ones.
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55. CSI Aquisition in FDD Massive MIMO
Figure : Oce Environment
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56. CSI Aquisition in FDD Massive MIMO
2.2
2
1.8
1.6
1.4
1.2
1
9
x 10
0 5 10 15
Time [s]
throughput
nOFDMsym=20; Time Interval=0.1s,LOS
SVD per subcarrier
Steering Vector
Round−phase
Figure : System throughput (bps) for three types of beamforming. The time
interval between two consecutive blocks is 0.1s and the number of OFDM
symbols is 20.
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57. CSI Aquisition in FDD Massive MIMO
2.2
2
1.8
1.6
1.4
1.2
1
9
x 10
0 5 10 15
Time [s]
throughput
nOFDMsym=5; Time Interval=0.1s,LOS
Steering Vector
Round−phase
SVD per subcarrier
Figure : System throughput (bps) for three types of beamforming.
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58. CSI Aquisition in FDD Massive MIMO
Overhead
Number of OFDM Symbols/subcarrier Overhead
20 0.0039 %
5 9:74 104 %
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59. CSI Aquisition in FDD Massive MIMO
Conclusion
Our Considerations
Low-complexity channel estimator for massive MIMO systems.
Two-stage solution that combines coarse estimation of Tx/Rx spatial directions
followed by a re

60. nement stage that exploits channel sparsity.
The proposed method achieves a quite low pilot overhead while ensuring very
accurate channel estimates.
The steering vector based precoder has a similar throughput performance compared
to the SVD-based one, being a good solution from a hardware implementation
viewpoint.
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61. Multidimensional Channel Estimation
Sparsity in a Multidimensional Space
Motivation
So far the sparsity has been taken into account only in the spatial dimension. However,
the concept can be extended for other dimensions: delay and Doppler.
Compressive Sensing in Multidimensional Problems
The channel is jointly estimated based on the sparsity of angular, delay and Doppler
domains.
More freedom to reduce the number of pilots in the time-frequency grid.
Tensor Algebra can be a very useful theory to develop new methods of estimation
techniques based on sparsity, and with reduced complexity.
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62. Multidimensional Channel Estimation
Compressed sensing in a tensor representation
3
3
2 1 2 1
Tensor Compressed Sensing
Tensor A Tensor B
Formulation
A = B 1 11 2 22 3 33 (9)
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63. Conclusion
Conclusion
Final Considerations
Massive MIMO: key enabling technology for beyond LTE cellular systems.
Sparsity is a key solution to channel estimation in FDD massive MIMO systems
Exploiting channel multidimensionality can further reduce the pilot overhead in TF
selective propagation.
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64. Conclusion
THANK YOU !!!
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