Interference Mitigation & Massive MIMO for 5G - A Summary of CPqDs Results

II International Workshop on Challenges
& Trends on Broadband Wireless Mobile
Access Networks – Beyond LTE-A
Interference Mitigation
& Massive MIMO for 5G:
Summary of CPqD’s Results
Jo~ao Paulo Miranda, Ph.D
Senior Research Specialist
Wireless Communications Division
− November 6, 2014 −

Problem Statement
A few words about CPqD
DL / UL: 13 / 4 Mbps
25 km
DL / UL: 26 / 7 Mbps
SLP, SLE
SLMP
SMP, STFC and SCM
SARC
SLP, SLE
SDR BWA
SLMP
7 MHz (uplink) 7 MHz (downlink)
451 458 461 468
450 MHz451 458 459 460 461 468 469 470
1 MHz 7 MHz 1 MHz 1 MHz 1 MHz 7 MHz 1 MHz 1 MHz
SMP, STFC and SCM
SLP Airports SLP Airports
SARC
RF
CR
WSN
Largest ICT RD Center in Brazil (founded 1976, ca. 1.300 employees)
Ops span from algorithm development to pre-industrial prototypes
Market is reached via partners to whom product technology is licensed
Compact eNodeB certified by Anatel for operation in the 450 MHz band

c Jo˜ao Paulo Miranda | CPqD | 2/40

Problem Statement
Developing LTE Base Stations
=) Intermittent difficulty on the part of UE to register with the cell (=
Cell Search Registration Procedure
1) PSCH: Zadoff-Chu sequences (symbol timing and frequency offsets)
2) SSCH: PN sequences (frame timing and cell identity information)
3) PBCH: Basic parameters (BW, CP length, antenna mode, etc.)


Agenda
Characterization of Measurement Setup
NBI Sources Signals Case Study: 3GPP LTE Band 31
Suppression Requirements
System Model Signal Modeling
NBI Suppression Process
NBI Suppression: Wavelet Transforms
Overview and Candidates Multirate Digital Filter Banks
Bilinear Signal Distributions
Simulation Work Input Parameters Channel Model
Preliminary Results @IEEE PIMRC’14
Extended Results @IEEE WCNC’15
On Trustworthy Massive Survey Motivation Methodology
MIMO Simulation Findings from the Survey Data
Trends in Massive MIMO


Agenda


Characterization of NBI Sources Signals
Measurement Setup
========= Setup =========
u
Custom-built J-pole antenna
v
RS FSH8 spectrum analyzer
w
Laptop running LabView
v w
u
==== Site ====
Lat: 22º 54' S
Long: 47º 02' W
Alt: 667 meters
fc = 460 MHz
Recently standardized
3GPP LTE Band 31
Uplink: 451–458 MHz
Downlink: 461–468 MHz
Voice Services (NBI):
Resolution for 12.5 kHz
channels set to 0.3 kHz
LTE Service (SOI):
Resolution for 5 MHz
channels set to 3 kHz
Our measurements confirmed the presence of multiple high-power NBI
(-65 dBm and above) sitting at both uplink and downlink frequencies


Exemplary Scenario of NBI in the LTE Downlink
Most frequencies granted
to PTT systems (highway
control and oil gas)
Talk time 20s for 90% of
the cases and occupation
30% on the average
x1 @ f1 = 463.5500 MHz
mainly affects PDSCH
x2 @ f2 = 464.0000 MHz
affects PSCH,SSCH,PBCH
463 464 465 466 467 f [MHz]
... PRB 10
SOI NBI
Subcarrier
120
131
x1 x2
PRB 4
Subcarrier
48
59
r
...
...
5 MHz channel mask
PSCH
SSCH
PHICH
PDCCH
PBCH Reference
Unused
PDSCH
f1 f2
xi
fi
This explains the different behaviors observed in the lab, namely poor BER
performance of the UE and/or its difficulty to register with the cell


Requirements for NBI Suppression
Low signal distortion: The LTE system operates up to 70% of the time
in the absence of NBI, so near-perfect signal reconstruction is crucial to
maintain the system BER
Prior knowledge of NBI sources signals: For the sake of flexibility and
practical feasibility, the amount of information of narrowband signals
should be kept as low as it can possibly be
Low computational complexity: Narrowband systems currently found in
LTE bands may not be refarmed nor undergo changes of any kind soon,
but the interference from them originated can be suppressed at the
receive side where low-complex approaches are preferred
What else have we learned from the field measurements?
NBI deemed statistically relevant in the Band 31 is from PTT radios
Narrowband signals may vary in number, power, and position


Agenda


System Model
Signal Modeling
The SOI is the OFDM-based LTE signal transmitted in the downlink
Signal of Interest
s(t) =
dNXS/2e
e=−bNS/2c
Cf (e) exp (j2fe(t − TCPTS))
8
:
e : Subcarrier index
f : Symbol index
NS : Number of subcarriers
Cf (e) : Constellation conveyed by eth subcarrier during f th symbol
f : Subcarrier spacing
TCP : Length of the cyclic prefix
TS : Sampling period


System Model
Signal Modeling
PTT signals based on FM can be assumed without any loss of generality
Narrowband Signal
xi (t) = Ai cos

2fi t + 2fdev
Z t
0
ai (u)du + i

8
:
i : Signal index, i = 0, 1, . . . , I
Ai : Magnitude of the ith carrier
fi : Center frequency of the ith carrier
fdev : Frequency deviation of the ith carrier
ai (t) : Audio signal modulated by the ith carrier
i : Random phase uniformly distributed in the interval (0, 2)


System Model
Signal Modeling
After passing through a multipath fading channel with impulse response
h[l] and L taps, the signal picked up by the LTE terminal corresponds to
Received (Sum) Signal
z[n] = r[n] +
XI
i=0
yi [n] + w[n]
8
:
l : Channel delay spread associated with the lth channel tap
r[n] : Filtered version of s[n], i.e. rnPL−1
[] =
l=0 h[l]s[n − l ]
yi [n] : Filtered version of xi [n], i.e. yi [n] =
PL−1
l=0 h0[l]xi [n − l ]
w[n] : AWGN statistically independent from tap to tap


System Model
Block Diagram of an NBI Suppressor
Signal
Decomposition
Analysis Block
yi[n]
r[n] z[n]
Σ
w[n]
NBI
Identification
Removal
^
Z0[m]
^
Z1[m]
...
^
ZK-1[m]
Signal
Reconstruction
z[n]
Suppression Block Synthesis Block
^
Z0[m]
Z1[m]
...
ZK-1[m]
Generic NBI Supression Process
1) z[n] is decomposed into a set of channels Zk [m], k = 0, 1, . . . ,K − 1
2) yi [n] in z[n] are cancelled out to yield ^Zk [m], k = 0, 1, . . . ,K − 1
3) ^z[n] is a good approximation of z[n] for I = 0, i.e. the no NBI case


Agenda


NBI Suppression: A Very Brief Overview
Frequency Domain
High-power NBI can be distingui-shed
from the lower-power SOI
Robust against center frequencies
that change over time and freq.
selective fading
Spectral leakage (the higher the
NBI power the larger the number
of corrupted subcarriers)
Time Domain
Cancellation filters applied before
the DFT block (no leakage)
Less prior knowledge of NBI is
required, e.g. center frequencies
and/or power per subcarrier
Poor suppression performance, ISI
(tradeoff CP length vs. impulse
response of the filter)
Frequency and Time
Flexibility and resolution superior to those obtained in single domain
Time-frequency distributions (TFDs) are of relatively lower complexity
Near-perfect signal reconstruction at cost of very few knowledge of NBI


NBI Suppression: Candidate Techniques
Wavelet Transforms (Multilevel Discrete Wavelet Transform)
HA,llh(z)
Analysis Block
z[n]
Cancellation
of Coefficients
due to NBI
z[n]
WTllh[m]
8 HS,llh(z)
Suppression Block Synthesis Block
^
WTh[m]
WTlh[m]
WTlll[m]
HA,h(z)
HA,lh(z)
HA,lll(z)
WTllh[m]
2
4
8
8
WTh[m]
WTlh[m]
WTlll[m]
8
HS,h(z)
HS,lh(z)
HS,lll(z)
2
4
+
^
^
^
^
+
+
+
JA
Pair of low- and highpass filters whose outputs are downsampled by 2
Finer resolution achieved by repetitive application of such filter banks
Lowpass filters and decimators replaced by H(z) =
QJ−1
j=0 HA(z2j
)
Coefficients associated with NBI zeroed out using
= 2
s
p2 erf−1(Pfa)


Multirate Digital Filter Banks (Polyphase Network)
Analysis Block
MX−1
Zk [m] =
=0
1X
r=−1
p[r]z[m−r]W−k
M
Synthesis Block
^z[r] =
1M
MX−1
k=0
1X
m=−1
q[r−m]^Zk [m]Wk
M
8
:
M : Decimation and interpolation ratio
K : Number of parallel channels
hA[n] : Lowpass analysis filter
hS[n] : Lowpass synthesis filter
p[m] : th polyphase branch of hA[n], p[m] = hA[mM − ]
q[m] : th polyphase branch of hS[n], q[m] = hS[mM + ]
r = [n + ]/M and WM = exp(j2)/M


Bilinear Signal Distributions (Discrete-time Wigner-Ville Distribution)
Analysis Block
XN
Zk [n] =
m=−N
z[n + m]z[n − m]w[m]w[−m]Wkm
4
Synthesis Block
Different procedures
Very hard to parameterize
Cumbersome in practice
8
:
M : Decimation and interpolation ratio
K : Number of parallel channels
hA[n] : Lowpass analysis filter
hS[n] : Lowpass synthesis filter
p[m] : th polyphase branch of hA[n], p[m] = hA[mM − ]
q[m] : th polyphase branch of hS[n], q[m] = hS[mM + ]
r = [n + ]/M and WM = exp(j2)/M


Agenda


Simulation Work
Simulator Simulation Method
Custom-built simulator implementing the PHY in accordance to LTE
5 × 107 Monte Carlo trials are conducted for each SNR point
Input Parameters
SOI Parameters
NS f TCP 1/TS fc BW
512 15 kHz 16.67 μs 30.72 MS/s 465 MHz 5 MHz
NBI Parameters
Ai fi fdev I BW
NBI/SOI = 15 dB f1, f2 5 kHz {0, 1} 12.5 kHz
Parameters/Technique MDFBs Wavelets Bilinear
Type of implementation Polyphase DWT DWVD
No. of parallel channels, K 16 2 per level 512
Decim./interpol. ratio, M 16 2 −
No. of resolution levels, J 1 8 1
Filter/window length, N 256 taps 16 taps 512 bins


Simulation Work
Channel Model
Parameter Band 31 IEEE 802.22
Transmitter-receiver separation 30 Km 10-100 Km
Radio frequency 450-470 MHz 30-3000 MHz
Channel bandwdith 5 MHz 5/6/7 MHz
Propagation conditions LOS/NLOS LOS/NLOS
Environment type Rural Rural/Suburban/Urban
Transmit antenna height 40 m 30-1000 m
Receive antenna height 5-10 m 10 m
Multipath profiles N/A See below
Seasons of operation All All
Multipath Profile
Profile “A” Path 1 Path 2 Path 3 Path 4 Path 5 Path 6
l [dB] 0 −7 −15 −22 −24 −19
l [μs] 0 3 8 11 13 21
fl [Hz] 0 0.10 2.5 0.13 0.17 0.37


Simulation Work
Preliminary Results for PDSCH @ f1 = 463.5500 MHz
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Signal−to−Noise Ratio [dB]
Bit Error Rate
NBI Off
NBI On
Wavelets
PolyNets
DWVD
Best results obtained by
Wavelets regardless the
type of physical channel
8-level DWT’s resolution is
about 20 times finer than
that of 256-tap PolyNets
NBI is cancelled out in a
highly localized fashion,
in contrast to other TFDs
e.g. PolyNets and DWVD


Simulation Work
Preliminary Results for PSCH @ f2 = 464.0000 MHz
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI Off
NBI On
Wavelets
PolyNets
DWVD


Simulation Work
Preliminary Results for SSCH @ f2 = 464.0000 MHz
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI Off
NBI On
Wavelets
PolyNets
DWVD


Simulation Work
Can wavelets other than Daubechies further improve performance?
The wavelet choice is typically dictated by the SOI characteristics
Signals conveyed through LTE physical channels have distinct structure
Is there a wavelet type that best suits each LTE physical channel?
The wavelet used in our implementation should be compactly supported
It should also possess the perfect reconstruction property
Candidate Set Input Parameters
Wavelet-specific Parameters
Wavelet Type Short Name N Lsup W
Biorthogonal Bior9.3 9.3 19.7 20 taps
Coiflets Coif-5 5 29 30 taps
Daubechies Daub-8 8 15 16 taps
Haar Haar 1 1 2 taps


Simulation Work
Extended Results for AWGN Channels
PDSCH @f1 = 463.55MHz
10−1
10−2
10−3
10−4
10−5
0 1 2 3 4 5 6
100
Bit Error Rate
Haar
NBI On
Daub−8
Bior9.3
NBI Off
Coif−5
PSCH @f2 = 464.60MHz
10−1
10−2
10−3
10−4
10−5
0 1 2 3 4
100
Error Rate
NBI On
Coif−5
Haar
Daub−8
Bior9.3
NBI Off
Performances derived by
Daub−8 and Bior9.3 were
the same as in the NBI Off
case (no error observed)
SSCH @f2 = 464.60MHz
10−1
10−2
10−3
10−4
10−5
No error measured for the NBI Off case
0 1 2 3 4 5 6
100
Error Rate
NBI On
Haar
Bior9.3
Daub−8
Coif−5
NBI Off
What have we learned from our AWGN analysis?
Coiflets wavelets are clearly the best option for both PDSCH and SSCH
Biorthogonal or Daubechies can be used for NBI suppression in PSCH
100% efficient if noise and NBI are the sole mechanisms at work


Simulation Work
Extended Results for Flat Fading Channels
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Bit Error Rate
NBI On
Bior9.3
Haar
Daub−8
Coif−5
NBI Off
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI On
Coif−5
Haar
Bior9.3
Daub−8
NBI Off
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI On
Haar
Bior9.3
Coif−5
Daub−8
NBI Off
What have we learned from our flat fading analysis?
Coiflets and Daubechies wavelets offer similar performance for PDSCH
All wavelets but Daubechies (20 dB gain) perform similarly for PSCH
Similar behavior observed also for SSCH with Coiflets as alternative
Perfect suppression no longer possible no matter the wavelet type


Simulation Work
Extended Results for Frequency-selective Fading Channels
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Bit Error Rate
NBI On
Bior9.3
Daub−8
Coif−5
Haar
NBI Off
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI On
Coif−5
Haar
Bior9.3
Daub−8
NBI Off
10−1
10−2
10−3
10−4
10−5
0 5 10 15 20
100
Error Rate
NBI On
Haar
Bior9.3
Daub−8
Coif−5
NBI Off
What have we learned from our freq. selective fading analysis?
Any wavelet outperforms ‘NBI On’ case in at least 5 dB for PDSCH
Wavelets of type Daubechies are confirmed as best option for PSCH
Either Daubechies or Coiflets can be used for NBI supression in SSCH
Perfect suppression no longer possible regardless wavelet type


Simulation Work
Summary of Simulation Results and Discussion
Operation Environment PDSCH PSCH SSCH
AWGN Channels Coif-5 Bior9.3, Daub-8 Coif-5
Flat Fading Channels Coif-5 Daub-8 Coif-5, Daub-8
Frequency-selective Channels Coif-5 Daub-8 Coif-5, Daub-8
0
−20
−40
−60
−80
0 0.2 0.4 0.6 0.8 1
Norm. Frequency (×p rad/sample)
Magnitude [dB]
0
−20
−40
−60
−80
0 0.2 0.4 0.6 0.8 1
Norm. Frequency (×prad/sample)
Magnitude [dB]
Complementarity of QMF
pairs in analysis/synthesis
Ability to reject frequencies
out of the band of interest
0
0
dB] −20
−20
[Magnitude −40
−40
−60
−60
−80
−80
0 0.2 0.4 0.6 0.8 1
0 0.2 0.4 0.6 0.8 1
Norm. Frequency (×prad/sample) Norm. Frequency (×prad/sample)
Magnitude [dB]
Lowpass Analysis
Highpass Analysis
Lowpass Synthesis
Highpass Synthesis
Bior9.3: Least complementar
Haar: Weakest attenuation
Coif-5 and Daub-8: Best
options from both aspects


Agenda


Survey Motivation Methodology
Motivation
Determine the current state of Massive MIMO simulation studies
Learn more about the subject’s specificities and most popular settings
Construct a trustworthy simulator on the basis of the survey data
By-product
Get a better (quantitative) view of trends in Massive MIMO research
Methodology
Search the IEEEXplore database for “acronym” and “massive mimo”
Limit results solely to proceedings published from 2010 to 2014
Set of 99 papers from nine IEEE conferences (5×ComSoc + 4×SPS)
Same person reviews all papers and asks only appropriate questions


Findings from the Survey Data
Summary


How trustworthy are the simulation works out there?
94 out of 99 papers (94.9%) use simulation to demonstrate their results
! 4 papers (4.3%) identify the simulator used to that end
! 3 papers (3.2%) address initialization bias and multiple scenarios
! No paper (0%) mentions availability to 3rd party use, version, OS
Question
1) Can such results be repeated for benchmarking/further development?
26 out of 94 papers (27.7%) state the number of iterations used
! In 20 papers (76.9%) this varies in type (runs,symbols,frames,time)
and amount (e.g. 10 to 100000 channel realizations)
More questions
2) How fair/hard it is to establish comparisons among results in this set?
3) What can be said about the statistical soundness of these papers?


Specificities of Massive MIMO Simulation
91 out of 99 papers (91.9%) state the number of antennas M and K
! 50 papers (54.9%) have 100 M 1000 and M K
! 41 papers (45.1%) consider arrays of up to 64 or as large as 1010
Questions
4) Is M = 64 large enough to fully exercise the technology under test?
5) Can any of you envision a practical array with 1010 antenna elements?!
52 out of 94 papers (55.3%) state the cellular layout adopted
! 37 papers (71.2%) use multicellular layouts in their environments
36 out of 94 papers (38.3%) state the user dropping strategy
! 19 papers (52.8%) assume uniformly distributed terminals
One more question
6) Are multicellular settings with uniformly distributed users preferred?


Specificities of Massive MIMO Simulation (continued)
87 out of 94 papers (92.6%) state the channel model used
! 38 papers (43.7%) consider a number of different channel models
! 41 papers (31.0%) consider large-scale effects (PL,shadowing,both)
Questions
7) Does this reflect the lack of standardized or widely accepted models?
8) What can be inferred from the prevalence of distance-based models?
56 out of 94 papers (59.6%) state the correlation matrix model used
! 23 papers (41.1%) rely on models hard to determine from the text
! 18 papers (32.1%) explicitly indicate the use of exponential models
! 15 papers (26.8%) assume uncorrelated antenna elements
One more question
9) Can we still say that most papers do not model spatial correlation?


Subject Share
Hardware: 8%
Other: 17%
Antennas: 6%
XCVR Design: 66%
Propagation: 3%
Channel characterization and modeling are currently under development
Mutual coupling and front-back ambiguity are also being investigated
Solutions to circumvent the imperfections of low-cost HW are needed
Transceiver design encompasses key problems in Massive MIMO


Transceiver Design: Detailed View
CSI Feedback: 19%
CSI Acquisition: 31%
Detection: 14%
Precoding: 37%
CSI ! FDD: #PRB for pilots and #channel responses scale with M
! TDD: Reciprocity calibration and pilot contamination
Precoding: MF vs. ZF vs. MMSE vs. BD vs. VP vs. THP vs. DPC
Detection: MF vs. ZF vs. MMSE vs. BI-GDFE vs. TS vs. LAS vs. ML


Agenda


Concluding Remarks
Summary of Findings
Multilevel DWT has been shown the best TFD for NBI suppression in
LTE physical channels due to its low complexity, low signal distortion,
high resolution, and ease of implementation
Optimisation of the proposed wavelet-based NBI suppression process
across LTE physical channels calls for different types of wavelets
Suppression performance drops as more realistic operation conditions,
such as shadowing and multipath fading, are taken into consideration
Determined the current state of Massive MIMO simulation studies, and
provided a quantitative assessment of trends in that research area
Coming up next...
Create IP in the form of patent for our wavelet-based NBI suppressor
Put together a PoC showcasing proposed solution implemented in DSP
Complete the construction of our trustworthy Massive MIMO simulator


www.cpqd.com.br
Jo~ao Paulo Miranda, Ph.D
Senior Research Specialist
Wireless Communications Division
+55 19 3705 6712
+55 19 98176 0250
jmiranda@cpqd.com.br


Interference Mitigation & Massive MIMO for 5G - A Summary of CPqDs Results

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Interference Mitigation & Massive MIMO for 5G - A Summary of CPqDs Results

Similar to Interference Mitigation & Massive MIMO for 5G - A Summary of CPqDs Results (20)

More from CPqD

More from CPqD (20)

Recently uploaded

Recently uploaded (20)

Interference Mitigation & Massive MIMO for 5G - A Summary of CPqDs Results