interference mitigation & massive mimo for 5g - a summary of cpqds results

II International Workshop on Challenges& Trends on Broadband Wireless MobileAccess Networks – Beyond LTE-A

Interference Mitigation& Massive MIMO for 5G:Summary of CPqD’s Results

Joao Paulo Miranda, Ph.DSenior Research SpecialistWireless Communications Division

− November 6, 2014 −

Problem Statement

A few words about CPqD

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• Market is reached via partners to whom product technology is licensed

• Compact eNodeB certified by Anatel for operation in the 450 MHz band

c© Joao 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 Procedure1) 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 SetupNBI Sources & Signals • Case Study: 3GPP LTE Band 31

• Suppression Requirements

System Model • Signal Modeling• NBI Suppression Process

NBI Suppression: • Wavelet TransformsOverview 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 & MethodologyMIMO 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 R&S FSH8 spectrum analyzer

w Laptop running LabView

wv

u

==== Site ====

Lat: 22º 54' S

Long: 47º 02' W

Alt: 667 meters

fc = 460 MHz

• Recently standardized3GPP LTE Band 31

• Uplink: 451–458 MHz

• Downlink: 461–468 MHz

• Voice Services (NBI):Resolution for 12.5 kHzchannels set to 0.3 kHz

• LTE Service (SOI):Resolution for 5 MHzchannels 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 grantedto PTT systems (highwaycontrol and oil & gas)

• Talk time ≈ 20s for 90% ofthe cases and occupation≈ 30% on the average

• x1 @ f1 = 463.5500 MHzmainly affects PDSCH

• x2 @ f2 = 464.0000 MHzaffects PSCH,SSCH,PBCH

f [MHz]463 464 465 466 467

PRB 10

Subcarrier

120

131

x2x1

PRB 4

Subcarrier

48

59

r

...

...

... SOI NBI

5 MHz channel mask

SSCH

PSCH

PBCH Reference

Unused

PHICH

PDSCH

PDCCH

f1 f2

xi

fi

• This explains the different behaviors observed in the lab, namely poor BERperformance 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 timein the absence of NBI, so near-perfect signal reconstruction is crucial tomaintain the system BER

• Prior knowledge of NBI sources & signals: For the sake of flexibility andpractical feasibility, the amount of information of narrowband signalsshould be kept as low as it can possibly be

• Low computational complexity: Narrowband systems currently found inLTE bands may not be refarmed nor undergo changes of any kind soon,but the interference from them originated can be suppressed at thereceive 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) =

dNS/2e∑e=−bNS/2c

Cf (e) exp (j2π∆fe(t − TCPTS))

e : Subcarrier indexf : Symbol indexNS : Number of subcarriersCf (e) : Constellation conveyed by eth subcarrier during f th symbol∆f : Subcarrier spacingTCP : Length of the cyclic prefixTS : 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[

2πfi t + 2πfdev

∫ t

0ai (u)du + θi

]

i : Signal index, i = 0, 1, . . . , IAi : Magnitude of the ith carrierfi : Center frequency of the ith carrierfdev : Frequency deviation of the ith carrierai (t) : Audio signal modulated by the ith carrierθi : Random phase uniformly distributed in the interval (0, 2π)


System Model

Signal Modeling

After passing through a multipath fading channel with impulse responseh[l ] and L taps, the signal picked up by the LTE terminal corresponds to

Received (Sum) Signal

z [n] = r [n] +I∑

i=0yi [n] + w [n]

σl : Channel delay spread associated with the lth channel tapr [n] : Filtered version of s[n], i .e. r [n] =

∑L−1l=0 h[l ]s[n − σl ]

yi [n] : Filtered version of xi [n], i .e. yi [n] =∑L−1

l=0 h′[l ]xi [n − σl ]

w [n] : AWGN statistically independent from tap to tap


System Model

Block Diagram of an NBI Suppressor

Signal

Decomposition

Analysis Block

Σr[n] z[n]

w[n]

yi[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 Process1) z [n] is decomposed into a set of channels Zk [m], k = 0, 1, . . . ,K − 12) yi [n] in z [n] are cancelled out to yield Zk [m], k = 0, 1, . . . ,K − 13) 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 frequenciesthat change over time and freq.selective fading

• Spectral leakage (the higher theNBI power the larger the numberof corrupted subcarriers)

Time Domain

• Cancellation filters applied beforethe DFT block (no leakage)

• Less prior knowledge of NBI isrequired, e.g . center frequenciesand/or power per subcarrier

• Poor suppression performance, ISI(tradeoff CP length vs. impulseresponse 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)

Analysis Block

z[n]Cancellation

of Coefficients

due to NBI

z[n]

Suppression Block Synthesis Block

^

WTh[m]

WTlh[m]

WTlll[m]

HA,h(z)

HA,lh(z)

HA,lll(z)

HA,llh(z)WTllh[m]

2

4

8

8

WTh[m]

WTlh[m]

WTlll[m]

WTllh[m]

8

HS,h(z)

HS,lh(z)

HS,lll(z)

HS,llh(z) 8

2

4

+

^

^

^

^

+

+

+

• 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 HJ

A(z) =∏J−1

j=0 HA(z2j)

• Coefficients associated with NBI zeroed out using γ = σ2s√

2 erf−1(Pfa)



Multirate Digital Filter Banks (Polyphase Network)

Analysis Block

Zk [m] =M−1∑ρ=0

∞∑r=−∞

pρ[r ]zρ[m−r ]W−kρM

Synthesis Block

zρ[r ] =1M

M−1∑k=0

∞∑m=−∞

qρ[r−m]Zk [m]W kρM

M : Decimation and interpolation ratioK : Number of parallel channelshA[n] : Lowpass analysis filterhS[n] : Lowpass synthesis filterpρ[m] : ρth polyphase branch of hA[n], pρ[m] = hA[mM − ρ]

qρ[m] : ρth polyphase branch of hS[n], qρ[m] = hS[mM + ρ]

r = [n + ρ]/M and WM = exp(j2π)/M



Bilinear Signal Distributions (Discrete-time Wigner-Ville Distribution)

Analysis Block

Zk [n] =N∑

m=−Nz [n + m]z∗[n−m]w [m]w∗[−m]W km

4

Synthesis Block• Different procedures• Very hard to parameterize• Cumbersome in practice

M : Decimation and interpolation ratioK : Number of parallel channelshA[n] : Lowpass analysis filterhS[n] : Lowpass synthesis filterpρ[m] : ρth polyphase branch of hA[n], pρ[m] = hA[mM − ρ]

qρ[m] : ρth polyphase branch of hS[n], qρ[m] = hS[mM + ρ]

r = [n + ρ]/M and WM = exp(j2π)/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 ParametersNS ∆f TCP 1/TS fc BW512 15 kHz 16.67 µs 30.72 MS/s 465 MHz 5 MHz

NBI ParametersAi fi fdev I BW

NBI/SOI = 15 dB f1, f2 5 kHz {0, 1} 12.5 kHzParameters/Technique MDFBs Wavelets Bilinear

Type of implementation Polyphase DWT DWVDNo. of parallel channels, K 16 2 per level 512Decim./interpol. ratio, M 16 2 −No. of resolution levels, J 1 8 1Filter/window length, N 256 taps 16 taps 512 bins


Simulation Work

Channel Model

Parameter Band 31 IEEE 802.22Transmitter-receiver separation ≈ 30 Km 10-100 KmRadio frequency 450-470 MHz 30-3000 MHzChannel bandwdith 5 MHz 5/6/7 MHzPropagation conditions LOS/NLOS LOS/NLOSEnvironment type Rural Rural/Suburban/UrbanTransmit antenna height 40 m 30-1000 mReceive antenna height 5-10 m 10 mMultipath profiles N/A See belowSeasons of operation All All

Multipath Profile

Profile “A” Path 1 Path 2 Path 3 Path 4 Path 5 Path 6αl [dB] 0 −7 −15 −22 −24 −19σl [µs] 0 3 8 11 13 21fl [Hz] 0 0.10 2.5 0.13 0.17 0.37


Simulation Work

Preliminary Results for PDSCH @ f1 = 463.5500 MHz

0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100

Signal−to−Noise Ratio [dB]

Bit

Err

or R

ate

NBI OffNBI OnWaveletsPolyNetsDWVD

• Best results obtained byWavelets regardless thetype of physical channel

• 8-level DWT’s resolution isabout 20 times finer thanthat of 256-tap PolyNets

• NBI is cancelled out in ahighly localized fashion,in contrast to other TFDse.g . PolyNets and DWVD


Simulation Work

Preliminary Results for PSCH @ f2 = 464.0000 MHz

0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate






Simulation Work

Preliminary Results for SSCH @ f2 = 464.0000 MHz

0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate






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 ParametersWavelet Type Short Name N Lsup WBiorthogonal Bior9.3 9.3 19.7 20 tapsCoiflets Coif-5 5 29 30 tapsDaubechies Daub-8 8 15 16 tapsHaar Haar 1 1 2 taps


Simulation Work

Extended Results for AWGN Channels

PDSCH @f1 = 463.55MHz

0 1 2 3 4 5 610

−5

10−4

10−3

10−2

10−1

100


Bit

Err

or R

ate

HaarNBI OnDaub−8Bior9.3NBI OffCoif−5

PSCH @f2 = 464.60MHz

0 1 2 3 410

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnCoif−5HaarDaub−8Bior9.3NBI Off

Performances derived byDaub−8 and Bior9.3 werethe same as in the NBI Offcase (no error observed)

SSCH @f2 = 464.60MHz

0 1 2 3 4 5 610

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnHaarBior9.3Daub−8Coif−5NBI Off

No error measured for the NBI Off case

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


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Bit

Err

or R

ate

NBI OnBior9.3HaarDaub−8Coif−5NBI Off


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnCoif−5HaarBior9.3Daub−8NBI Off


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnHaarBior9.3Coif−5Daub−8NBI 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


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Bit

Err

or R

ate

NBI OnBior9.3Daub−8Coif−5HaarNBI Off


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnCoif−5HaarBior9.3Daub−8NBI Off


0 5 10 15 2010

−5

10−4

10−3

10−2

10−1

100


Err

or R

ate

NBI OnHaarBior9.3Daub−8Coif−5NBI 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 SSCHAWGN Channels Coif-5 Bior9.3, Daub-8 Coif-5Flat Fading Channels Coif-5 Daub-8 Coif-5, Daub-8Frequency-selective Channels Coif-5 Daub-8 Coif-5, Daub-8

0 0.2 0.4 0.6 0.8 1

−80

−60

−40

−20

0

Norm. Frequency (×π rad/sample)

Mag

nitu

de [d

B]

0 0.2 0.4 0.6 0.8 1

−80

−60

−40

−20

0

Norm. Frequency (×πrad/sample)

Mag

nitu

de [d

B] • Complementarity of QMF

pairs in analysis/synthesis

• Ability to reject frequenciesout of the band of interest

0 0.2 0.4 0.6 0.8 1

−80

−60

−40

−20

0


Mag

nitu

de [d

B]

0 0.2 0.4 0.6 0.8 1

−80

−60

−40

−20

0


Mag

nitu

de [d

B]

Lowpass AnalysisHighpass AnalysisLowpass SynthesisHighpass Synthesis

• Bior9.3: Least complementar

• Haar: Weakest attenuation

• Coif-5 and Daub-8: Bestoptions from both aspects


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

Question1) 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 questions2) 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

Questions4) 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 question6) 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)

Questions7) 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 question9) Can we still say that most papers do not model spatial correlation?


Trends in Massive MIMO

Subject Share

Other: 17%

Hardware: 8%

XCVR Design: 66%

Antennas: 6%

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


Trends 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


Concluding Remarks

Summary of Findings

• Multilevel DWT has been shown the best TFD for NBI suppression inLTE physical channels due to its low complexity, low signal distortion,high resolution, and ease of implementation

• Optimisation of the proposed wavelet-based NBI suppression processacross 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, andprovided 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

Joao Paulo Miranda, Ph.DSenior Research SpecialistWireless Communications Division+55 19 3705 6712+55 19 98176 [email protected]


interference mitigation & massive mimo for 5g - a summary of cpqds results

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