WPMC'08 Tutorial slides

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Flexible Transceivers Based on

Time-Frequency Representation Theory

On the behalf of URANUS EU FP6 project

Harri Saarnisaari, Univ. of Oulu / CWC, Finland

Alexsander Vießmann, Univ. Duisburg-Essen, Germany

WPMC 2008 Tutorial, Saariselkä, Finland, September 8, 2008 Page 1

Flexible Transceivers Based on Time-Frequency Representation Theory

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Outline of Tutorial

 A very brief introduction to URANUS project that is behind this tutorial

 Motivation

– Why we want flexible ratios with time frequency processing

 A brief introduction to Gabor analysis

– Mathematical definitions

– Computation

 Time-frequency processing

– Transmitters and receivers in time-frequency domain

– Doubly-dispersive channel modelling

– Simulation results

• Time frequency receiver vs conventional UMTS receiver

– Time frequency symbol synchronization techniques

– Other items of interest, future concepts

 Platform demonstration



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 U niversal RA dio-link platform for efficie N t U ser-centric acces S

 The aim of the project

– Is to develop flexible baseband transceiver architectures based on time-frequency signal processing

• More precisely, the Gabor transform

– Find a parameterized architecture

• The modulation (mode) can be changed just changing the parameters, not transceiver chains

– Demonstrate the main elements

URANUS Project



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 Partners

– LETI (F) : coordinator

– STM (F)

– TID (E)

– CWC (FI)

– UNIK (G)

– IASA (GR)

– PUT (PL)

 Duration : 36 months

– 2006-2008

 EC contribution : 2,6 M€

 Deliverables

– www.ist-uranus.org

URANUS Project

UniK



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Motivation

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Motivation

 We’d like to transmit and receive existing multitude of modulation methods by one efficient transreceiver

 As well we want to be future proof such that our flexible transceiver could handle also future signals that increases the life cycle of our device

– Or if not our device’s then at least our design’s

 We’d like to do this without sacrificing too much on performance, and achieve some benefits if possible

– Since we know that flexible solutions are not necessarily as optimal as pointoptimal solutions

 It would be a benefit if the same receiver structure could be used also for synchronization and channel estimation

– No need to define separate chains for these



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Restrictions

 We consider waveforms in the physical layer (PHY)

– No coding or decoding, no MAC, etc, …

– Just modulation and demodulation, and synchronization and channel estimation

 We consider the base band part

– No RF part at all although that has a major impact on actual multimode transceivers



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Conventional Approaches to Flexible Radio

 Current approaches to the baseband of flexible radios rely either on (in increasing order of complexity):

– Multiple parallel chains (Fig 1)

• Easy/rapid implementation (follow a standard)

• Improved performance if switching between them is optimized

• However: Limited scope (to existing air interfaces)

– Common reusable modules or specialized instruction sets (Fig 2)

• Increased processing speed

• More efficient power consumption

• However: Limited scope (must be developed on a case-by-case basis)

– Software defined radio (SDR) (Fig 3)

• Full reprogrammable flexibility

• Best performance possible (since optimal algorithms can be implemented)

• However: Large power consumption / Limited reconfiguration speed



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1 – multiple HW

1

2

SW

SW

HW

HW

1

2

1

2

Different Architectures

2 – single HW

HW

SW HW

1

2

HW

4 – Uranus 3 – generic SDR

SW HW

1

2

CPD

1

2

SW

HW

1

2

1

2



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URANUS Approach

 We selected a solution (Fig 4) that includes a flexible, reconfigurable hardware into which most part of the transceiver operation can be fitted

– The building blocks of the transceiver chain are parameterized

• Canonical parametric description (CPD) of the transceiver

– Modulation method (the mode of the transceiver) is specified by these parameters

 We see the following benefits of this solution

– Less die area

– Less power consumption

– Fast switching time from one mode to another

• Beneficial, e.g., in vertical hand over (VHO)

– Fast time to market



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Pros and Cons at a Glance

Area

Power

RT Reconfig.

NRT Reconfig.

IPs Reusability

Upgradeability

Time to Market

Testability

Mult. HW Single HW Generic

SDR

High

High

Low

Low

Low

Low

Medium

Medium

URANUS

Low

Low

Medium High

Low Low High High

Low

Low

Low

Low

Medium Low

Predef.

Predef.

High

High

Low

High

High

High

Low

High



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Background and Further Motivation

 Single carrier (SC) signals are traditionally received using time domain receivers

– Complexity and power consumption of the time domain filters and equalizers are a big issue, especially with high sampling rates, i.e., data rates or bandwidths

 A way to reduce the complexity is to use frequency domain equalizers (FDEs)

– As proposed, e.g., in 3GPP SC-FDMA

– As well as in UMTS for chip level equalization

M-point FFT

Subcarrier de-mapping

/ Frequency domain equalizer

N-point IFFT detection



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 Multi carrier (MC) signals are naturally transmitted and received in frequency domain using (I)FFT

– Adoption of empty subcarriers at band edges even eliminates the need for digital pulse shaping filters in the transmitter and receiver

 Therefore, frequency domain transceiver may be seen as a candidate flexible baseband architecture

 But, can we do something else, even better?



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 MC signals (OFDM) have a benefit in frequency selective channels

– The radio channel is a constant single tap channel over some subcarriers

• Simple single tap channel estimators and equalizers can be used

– No filtering frequency subcarriers

 However, in double selective channels where the channel is time varying even within a symbol we can do something else

 This else is time frequency (TF) processing: the topic of this tutorial symbol symbol time

Frequency selective

Double spread



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 In TF processing we divide the signal into TF grid

– In OFDM division is only in F direction

 There are many possibilities for TF processing but we selected the

Gabor transform as our basis

– Discrete Gabor transform (DGT) and its inverse (IDGT)

 A reason is that it is a generalized

MC signal (GMC signal), a possibility in the future

 Furthermore, it can be efficiently implemented through filter banks or short time Fourier transform (STFT) frequency

Pulse shape time

TF-grid



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Time Frequency Processing



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 The main working horse is the discrete Gabor transform (DGT) and its inverse (IDGT)

– See right

TF Signal Representation

c lm



L



1 M



1   l



0 m



0



N n





1



0 lm

*

[ ] [



]e j 2



/

 j 2



/

.

 Function g[n] is called the synthesis window since the signal

s[n] is reproduced from its Gabor coefficients c lm by it

 Function w[n] is called the analysis window since the Gabor coefficients are calculated by it

 Also name Gabor atom is used

• T is the atom separation in time (grid size in time)

• F = 1/M is the separation in frequency

• L is the number of time slots

• M is the number of frequency slots

• N is the number of signal samples in time domain

• Note similarity with FFT - IFFT pair



u frequency atom shape


TF-grid

0

• Atoms can overlap

• Their shape can be selected to fit for the signal and channel

F

T time

T s

= N/LT

Signal duration



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 We usually require that the lattice constants T and F satisfy

TF ≤ 1

– Means that atoms cover all the TF-grid

– Means that a signal can be fully represented by its Gabor transform

 TF = 1 is called the critical case

– Used in the IFFT phase of the OFDM

 TF < 1 is the overcritical case

 TF > 1 is the undercritical case

 In terms of M, L and N the condition TF ≤ 1 becomes LM ≥ N

– The number of grid points is equal or larger than the number of samples



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 The selection of analysis and synthesis windows is not arbitrary

 If we select the synthesis window to be a dual of the analysis window, then the synthesis is possible

– E.g., rectangular windows

 If the synthesis window is a tight dual of the analysis window, it is the analysis window multiplied by a constant

 There exits tools to calculate windows and their duals

– See, e.g., books and web pages concerning Gabor analysis or STFT



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Ways to Apply: I

 Take a signal with bandwidth B and duration T by its Gabor coefficients s and represent it

– Like UMTS, GSM, OFDM, …

– If the signal has N samples in time domain, remember that L and M satisfy the design constraint LM ≥ N

– Select suitable atoms, e.g. based on radio channel characteristics

 Use synthesis to regenerate the signal and analysis to compute the Gabor coefficients

 Results transmitter and receiver structures for any signal

– Will be discussed later



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Ways to Apply: II

 It is easy to see that DGT is a generalization of FFT

– Atoms (windows) added

– Temporal domain added to coefficients c lm





L



1 M



1   l



0 m



0

N n





1



0 lm

*

[ ] [



]e j 2



/

 j 2



/

 1.

Think the Gabor coefficients c lm as symbols to be transmitted in the TFgrid and obtain a generalized multicarrier (GMC) signal

– Future communications?

• 3.

See that usual linearly modulated SC signals are a special case with M = 1 and the atom as a pulse shaping function

 2.

See that usual MC signals (OFDM and MC-CDMA) are a special case with rectangular atom and L = 1

• As a consequence, a GMC system can be used to transmit GMC, MC and SC signals


.


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Computation

 The formulation of DGT is equal to the short time Fourier transform (STFT)

– compute using (I)FFT and window functions

 Another way is to apply filter banks and especially uniform DFT filter bank that uses FFT

 We have

– Analysis filter bank (AFB) to calculate the Gabor coefficients

– Synthesis filter bank (SFB) to reproduce the signal from the Gabor

(or STFT) coefficients



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Uniform DFT FB

z

1 z

1 analysis filter bank

E z

0

M ( )

E z

1

M ( )

Ї

N

Ї

N

FFT c

0

[ ]

[ ]

IFFT synthesis filter bank

 N

 N

R

0

( z

M )

R

1

( z

M ) z 1 z 1 z 1 z

1

E

M 1

( z

M ) Ї N c n

M 1

[ ]

 N R

M 1

( z

M )

• Size of (I)FFT: M

• Filters E i

(z) and R i

(z) are polyphase components of the analysis and synthesis windows

• Up- and downsampling units

(Note: in this figure N = grid timeT in samples)



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Transmitter



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 GMC

– synthesize the signal using the synthesis formula

 MC

– OFDM is simply seen as special case and yields a simple implementation by IFFT

Transmitter

 SC

– Instead of usual pulse shape filtering it is possible to use filter banks for pulse shaping

– Can be seen as TF based filtering, generalization of well known frequency domain filtering



x k u

 g z

1 z

1

Coefficient generator

%

0

( )

%

1

( )

Ї N

Ї

N

SC Transmitter

FFT c l ,0 c l ,1 c

, 1

IFFT

DFT synthesis filterbank

N

N

N

R

R

0

( z

M )

1

( z

M )

R

M 1

( z

M ) z

1 z 1 z

1 z

1

%

M 1

( ) Ї N

 Polyphase components P i

(z) include both the analysis window and pulse shaping filter

–  is the upsampling factor for pulse shaping

 Also here N = grid timeT in samples



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SC Transmitter

 In this structure windows can be used for sidelobe reduction

 The number of computations can be reduced by nulling frequency components that have insignificant components not affecting the quality of the signal

• In this figure the complexity of the FB transmitter is 20x higher in terms of complex multiplications

• However, if the sidelobe level of the conventional TX is reduced its complexity is increased



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Receiver – Channel Modeling and Demodulation



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 We concentrate on receivers based on TF processing

– Not special cases

Receiver

 Demodulation

– Correlation approach

– Equalization

 Channel modeling approach

 Symbol synchronization



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Receiver – Channel Modeling

 In the radio channel the transmitted signal s(t) is modified

– Modification defined by operation Hs(t)

 In a single tap (only LOS) channel a single time or frequency domain (FD) tap is sufficient to define the whole channel

– Traditional model

 In frequency selective channels several FD taps are needed

– Channel assumed to be constant over a frequency slot, but several bands are needed to cover the whole signal bandwidth

– A FIR filter model in time domain

 In double spread channels frequency selective channels are time varying

– Problems if within a symbol

– Channel assumed to be a constant within a time-frequency cell, several cells needed to cover the whole used TF plane

• TF processing



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Receiver - Channel Modeling

frequency

F

T

H(0,1)

H(0,0)

H(0,-1) atom shape

H(1,1)

H(1,0)

H(1,-1)

T s

= N/LT

Signal duration

Channel coefficients in

2D model

H(2,1)

H(2,0)

H(2,-1)

TF-grid

• All these approaches yield to a single tap per F slot or TF cell channel models

-- No filtering

-- Are approximations of real radio channels time

0

• In DGT based processing a research topic is to select atoms that best fit for the used radio channel

-- a degree of freedom here

-- robust windows: good both for easy and bad channels



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Receiver - Channel Modeling

 If coherent modulations (like PSK, QAM) are applied pilots are needed to find the channel coefficients

 The number of pilots depends on the expected operational environment

– Channel, relative velocity, etc.

 There should be a pilot per constant frequency slot (OFDM) or per constant TF cell (TF processing)

– Some short of interpolation between pilots might also be used

 If TF processing is applied to existing (legacy) signals their pilot structure has to be utilized



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 Assume linear modulation

– Received signal y (t ) = b(k) s(t ) + n (t )

– b(k) kth data symbol

– s(t ) pulse shape

– n (t ) additive noise

Receiver - Demodulation

 Sufficient statistics in white Gaussian noise are

v (k)=  t

y (t )s *(t ) (discrete correlation)

 Assume that channel H affects the signal. Then sufficient statistics are

v (k) =  t

y (t ) ( H s(t ) )*



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 In TF processing we do correlation in TF domain

 We need

– TF form of y (t )

• Analysis of y (t ) results Gabor coefficients c(l,m)

– TF form of H

• Single tap 2D channel model results taps H (l,m)

– TF form of s(t )

• Analysis of pulse shape s(t ) results coefficients u(l,m)

 As a consequence, we have

v(k) =  l,m

 l,m

c(l,m) ( H (l,m) u(l,m) )* =

( c(l,m)H*(l,m) ) u*(l,m)

– Note that the sum is over l and m in TF domain



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 The sufficient statistics can be used (as usual) for

– ML receiver

– Decorrelation receiver

– MMSE receiver

– Equalizer receiver

 Observe close connection to frequency domain receivers

– TF processing is replaced by Fourier transform (or FFT in practice)

 Since channel taps H (l,m) are unknown pilots are used to estimate them

– 2D channel estimation considered in deliverables



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Signal in

Analysis

FB

Demodulator decoding

Reference signal

Channel estimation

 Reference signal is TF form of s(t ) or pilots depending on needs

 Needs

– AFB

– Reference/pilot generator or both +AFB for those

– Channel estimation unit

– Demodulator (desired receiver type)



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c(l,m)


u(l,m)

H (l,m)

 More detailed look of the receiver

– The correlator block denotes the receiver type (ML, MMSE, equalizer, …)

 The blocks are parameterized by variables 

– Mode (modulation) is changed by changing the parameters within a block



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 Vector model of sufficient statistics


v = Ab + n, A(i,j) = correlations of channel affected signals Hs(t )

 ML: arg max 2Re{b H v} - b H Ab

 Decorrelation: b = diag( A -1 v )

 MMSE: b = diag( (A+N

0

I) -1 v )

 Equalizer: b(k) =

b(k) =  l,m

 l,m

c(l,m) (H (l,m) u(l,m) )* / |H (l,m)|

c(l,m) u*(l,m) / H (l,m)

2 or even



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 We applied the idea for UMTS

Receiver – Simulation Results

 It is a challenging thing since the reference in UMTS is, due to scrambling, time varying

– TF representation of the reference has to be calculated again for each input block

 The simulation chain includes a common transmitter

 However, there exist a variety of ways to do the receiver

– Traditional UMTS receivers

• Time domain correlator

• Frequency domain chip level equalizer

– TF based

• Matched filter

• TF based equalization



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WCDMA Simulation Chain

Binary source #1

Binary source #U

BER #1

BER #U

Channel coding #1

Mapping

Channelization

(OVSF) scrambling

Pulse shaping

(SRRC)

Channel coding #U

Mapping

SISO channel

Channel de-coding

#1

Demapping

User demultiplexing

Waveform processing

(GMC or MF or FDE)

Channel de-coding

#U

Demapping



URANUS and usual approach differ only in this block

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TF Architecture I: Correlator

y

C l,m

AFB1 w(n) correlator signal waveform generator p k

Channel IR

C l,m

AFB2 g(n)



(k) l,m



(k) l,m h l,m

Channel estimation u k

LLR

Channel decoding



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TF Architecture II: Combiner

y

AFB1 w(n)

C l,m

Channel combiner

H l,m b l,m correlator



(k,i) l,m

AFB2 g(n) u i

(k) signal waveform generator

P k,i



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 Correlation:

Sufficient Statistics (a recall)

u k

 

( , )



I k

 h lm

 lm



* c lm

 

( , )



I k

 * lm



* h c lm lm



 Generalization: u k

 

( , )



I k

 * lm

 lm lm



– MRC (= Matched Filter) : z lm

 h

* lm

– Zero Forcing (ZF):

– MMSE: z lm

 h lm z lm

 h h

* lm

2   2 h lm

* lm

2

 Combiner: b lm

 lm lm h replaced by a generic variable z b is for (z*c) term in the previous Fig



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UMTS: Conventional FDE

 Note similarity to UMTS FDE shown here

 Underlying approximation behind this: channel matrix R is circulant

–

F : FFT matrix

– H : diagonal matrix

. .

H 

H y

FFT

C l,m

Channel combiner

IFFT

H l,m

Frequency domain equalizer

Sample selection and decimation

Despreading u k,i c k,i



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Architecture II: Simpler version in multiuser case y

If multiple codes (base station) are used then this structure needs a chain for each code

-> complex

AFB1 w(n)

C l,m

Channel combiner b l,m correlator



(1) l,m

AFB2 g(n) u

1 signal waveform generator p

1 u

I

H l,m correlator



(I) l,m

AFB2 g(n)

Equivalency holds between these two

-> much simpler structure signal waveform generator p

I y

AFB1 w(n)

C l,m

Channel equalization b l,m

SFB g(n)

Pulse shaping

H l,m



Decimation and

Despreading u

1,00E-03

6

1,00E-01

1,00E-02

1,00E+00 u

Simulation Results

 “WCDMA” : conventional time domain matched filter implementation

 “GMC” : architecture I

Pedestrian A - Real CE

Pedestrian A - Real CE

1,00E+00

WCDMA - S = 64

GMC -config 1- S = 64

WCDMA - S = 16

GMC - config 1- S = 16

WCDMA - S =256

GMC - config 1- S = 256

1,00E-01

1,00E-02

1,00E-03

WCDMA - Load = 1/8

GMC - config 1- Load = 1/8

WCDMA - Load = 1/4


WCDMA - Load =1/2


WCDMA - Load = 1

GMC - config 1- Load = 1

8 10 12 14

Eb/No (dB)

System load (I/S) = 1/4

16 18 20

1,00E-04

6 8 10 12 14

Eb/No (dB)

16

Spreading factor S= 16

18 20

Perfect matching



1,00E-01

1,00E-02

1,00E-03

1,00E-04

1,00E-05

0,5

1,00E+00 u

Architecture II vs FDE

Vehicular A - Perfect CE - QPSK - 8 users - MMSE - FEC

1 1,5 2

Eb/No (dB)

2,5 3

Simulation Results

3,5

GMC

FFTI

1,00E+00

1,00E-01

1,00E-02

1,00E-03

1,00E-04

1,00E-05

4

Vehicular A - Perfect CE - 16 QAM - 15 users - MMSE - FEC

5 6

Eb/No (dB)

7 8

Perfect matching

GMC

FFT

Does TF processing offer any benefits in faster fading channels is still an open question



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Receiver - Synchronization



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 OFDM symbol timing is usually proposed in time domain crosscorrelation techniques although otherwise a frequency domain receiver chain is used in OFDM

 However, it is possible to apply frequency domain processing in

OFDM as well

 It is indeed very well known (or should be) that frequency domain matched filtering yields to a fast acquisition

– See spread spectrum (e.g. GPS) literature

 Herein, we briefly describe how TF idea can be used for joint time-frequency acquisition

– being a special application of STFT filtering



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 Most timing systems are based on a pilot signal

– Know symbols are transmitted

– Symbols are often taken from a pseudo noise or, equally, direct sequence

(DS) code

– Therefore, considered DS acquisition is a generic approach

 Usually timing and frequency uncertainties are divided into small cells

– A cell with particular trial timing and frequency values is called a test cell

– The receiver tries to find in which cell the signal is

 After acquisition timing and frequency estimates are usually fine tuned

– Tracking mode

 Since acquisition is based on threshold comparison of decision variable to a threshold also thresholding device is needed

– Based on simulations or hardware test

– Automatic based on detection theory – often CFAR



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 It is well known that FFT based filtering is computationally efficient way to implement filters

– Block based processing

• The filter length is a natural block size

– Overlap-save (OLS) or overlap-add (OLA) methods have to be used for proper convolution

– Results latency related to the block size.

• In real time applications (like in audio SP), this latency may be unacceptable, especially with long filters

• Latency can be reduced using block or partitioned filtering where the filter is chopped into smaller blocks

 It is fairly easy to see that block filtering is a special case of the short time Fourier transform (STFT) based filtering

– No windows, no overlap

– Can be implemented by STFT or filter banks (FB)

 It is quite natural to apply STFT filtering to MF

– Generic MF

– Lower latency to decision unit

• The output block length is N/L chips where L is the number of non-overlapping blocks

• In the traditional implementation the output block size is N chips

– Windows are known to be a necessary element in efficient spectrum sensing since they reduce spectrum leakage causing wider “spectrum”

• Applications: narrowband interference mitigation, spectrum sensing in cognitive radios

 Although here discussed as MF it can be used also as a generic filter

• Pulse shaping applications

• Low latency applications

• When FFT size is limited but when a longer filter is required



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Leave FFT out and you have usual partitioned filter for timing

- Just sum as in DC component

PMFs denote partitioned parts of the MF


Idea of Joint Timing and Frequency Acquisition

PMF 1 frequency

PMF 2 ...

FFT

PMF N

A test cell with particular trial timing and frequency values

Search the cell that exceeds the detection threshold i) serial search: each FFT output block separately ii) after all time cells are computed (from the matrix)



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Frequency Domain Partitioned MF: Joint Acquisition

No windows

No overlap

The zero frequency FFT bin corresponds the output without

Doppler processing

This means that the sum of columns is needed if Doppler processing is not required.

M M

N

M

Cycle 2

M

FFT nextpow2(2M

-1)

FFT nextpow2(2M

-1)

FFT nextpow2(2M

-1)

Cycle 1

FFT (Doppler processing)

H

T

Cycle 2

IFFT nextpow2(2M

-1)

IFFT nextpow2(2M

-1)

IFFT nextpow2(2M

-1)

Cycle 1

Add tail to next head

Signal matrix

(STFT)

Multiply elementwise by filter’s

STFT

PMF1 output

PMF2 output

PMF3 output



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TF Filtering: windows, overlap, no Doppler

Input

STFT stream

N

M

Input signal segmenting overlapping

Example with L=2 and R=M/2

Window + FFT (2M)

N

Add tail to next head

2M tail head

2. cycle

1. cycle

Columnwise IFFT + addition or

Final output, 1 period

Each cycle results 2M vector with head and tail columnwise addition + single IFFT

Form input matrix (2M x LM/R) multiply by filter

(matrix x matrix)

M/R, the step size of filter cycles

A filtering cycle results an output block of size M, L cycles needed to finish filtering of a N chip DS signal



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Results

 We applied the proposed acquisition scheme for

– DS acquisition

– Joint timing-frequency acquisition of DS signals

• GPS BOC(1,1)

 Effects of

– windows,

– overlapping,

– multipath channel



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Flat Rayleigh channel

-No overlap

-No windows

Code length N=64

M=R=32

M=window size

R=hop size (controls overlap, M=R no overlap)

Nice match between theory and simulations


Results: DS acquisition



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Frequency selective

Rayleigh channel

-two equpower taps,

-one chip apart

--No overlap

--No windows

Code length 64

M=R=32

M=window size

R=hop size

Px2 = either 1. or 2. is accepted as the correct path

-diversity gain!

Selects between two and only another is OK



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Flat Rayleigh fading channel

-overlap

-windows

-equal Pfa: fair comparison !!

Code length 64

M=64, R varies

M=window size

R=hop size

Pfa set by simulations for windowing & overlap cases



Windowing causes losses if Pfa is kept constant!

- Even 3 dB



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Results: joint DS acquisition

AWGN channel

-no overlap

-no windows

Code length 1023 (GPS code)

M varies

M=window size

K=FFT size for Doppler processing

It is known that the system has:

-Mismatch loss if frequency error increase, reduce by larger FFT size or, equally, smaller block size

-Scalloping loss if actual frequency is between the two frequency bin centers

Freq.centers

Freq. uncert.



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Simulations

 K=16, M=64

 Freq. cell size FT=1 (1 kHz if T=1 ms)

 Performance decreases as FT increases

– As it should

– Or if FT is between multiples of 1 (scalloping case)

Probability 1 is achieved by input SNR -15 dB, this agrees with theory

In FT=1.5 case the actual freq. is between the two bins and the receiver selects half of time the wrong cell



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Simulations

 K’=64, M=32

 Freq. cell size FT=0.5

(500 kHz if T=1 ms)

 Scalloping loss reduction scheme

– Larger FFT with zero padding

Input SNR

The performance is better now! All FTs close the ideal.



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Other Topics

Other issues considered in the project that are of interest



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Selection of Windows

 It is possible to optimize windows such that used approximated

2D single tap channel model results minimum error

– Signal power as reference

 Results show that minimum error is obtained with zero delay and

Doppler spread

 Error increases as either one or both increase

 However, in many practical case the error is of order -20 dB or smaller



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Selection of Windows

 Here is example for UMTS: tight windows with corresponding residual channel modeling errors

 N denotes time grid size T in samples, M is the FFT size

 See TF = N/M < 1, overcritical case

 Sampling rate: 2 samples/chip



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 MC signals have PAPR problem

PAPR Reduction

 Does GMC has that too, or does windowing help on this?



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PAPR Characteristc for Various Window Shapes

Propability that PAPR is larger than a threshold PAPR

0

PAPR reduction method not applied

Result:

Windows are not helpful on this



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Adaptive Bit and Power Loading

 Adaptive bit and power loading for GMC signals

– Not MC or SC but GMC

– Can TF characteristics used succesfully?

– Do TF characteristics cause harm or new problems when old methods are applied?



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Example Simulation Results

– exponentially decying power channel

 Hughes-Hartogs (H-H) algorithm applied

 As can be seen, more bits are put there where channel is good



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Power allocation based on the theoretical formula approximating negligible overlapping of atoms

Power allocation after negative levels nulling

Power allocation before negative levels nulling

Result:

Less power to bad cells



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GMC Signals for Double-Spread Channels

 We recognized that OFDM is a special case of GMC signals

– Undercritical due to cyclic prefix

• After IFFT TF=1 holds but not after adding CP

CP

CP x

0 x

0

P/S

P/S

D/A A/D IFFT

FFT x

M



2 x

M



1 noise x

M



2 x

M



1

 OFDM is indeed a biorthogonal signal since the transmission and receiver windows are biorthogonal g t w t l

( ), m

( )

 



T cp

0 T s t



T cp

0



T s t

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GMC Signals for Double-Spread Channels

 OFDM

– CP handles inter block interference (IBI) between OFDM symbols: due to delay spread

– However, inter carrier interference (ICI) due to Doppler spread remains a problem

– Use of CP wastes TF space (capacity)

 Can we do better?

 E.g., use well localized pulses, critical or overcritical region (instead of undercritical) in GMC signals

Possible in GMC

OFDM



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Multicarrier signaling in the overcritical TF region w

Modulation Channel

Information bits

Coding &

Symbol

Mapping s

Precoding

P s

T H y s : i.i.d. information symbols (e.g., QAM)

P

: precoding matrix (tall); design parameter s : “TF symbols”

T

G

: modulation matrix (fat); design parameter

H : LTV channel operator w : AWGN y : received block (time domain) y



HT Ps

 w

G



T

G

D ( )

 v

Channel diagonalization h : TF channel representation v : error term due to AWGN and imperfect channel diagonalization



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 Channel model (discrete time)

– 6 i.i.d. taps

–

7 10



3

Doppler spread (normalized to sampling freq.)

 Proposed system parameters:

– M = 64 sub-carriers

– Redundancy in time domain

(ρ=1, 2, 3, 4)

– Rectangular transmit pulse

– Optimized precoder for SNR=20dB

 CP-OFDM parameters

– Varying # of sub-carriers

 Proposed scheme significantly outperforms OFDM

Numerical results



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 Simulation parameters

– 64 sub-carriers

– ρ = 4

– Modulation: 4-QAM

– Code rate: 1/2

– Code: (3,6) LDPC of length

1008 bits

Numerical results

 Results:

– LMMSE performance close to full CSI processing

– TF decoupler incurs a 3dB loss

– OFDM fails (significant error floor)

– Improved performance with increasing codeword length



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URANUS

Implementation of GMC Transceiver in a

Demonstrator Platform

Presentation by

Peter Jung, Alexander Vießmann, Christoph Spiegel representing the URANUS partners

UDE, CEA-LETI, TID, TUKL, UOULU



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 Main Objective the URANUS Validation Platform

 Architecture Paradigms

 URANUS Concept Validation

 Transceiver Structure

 Building Blocks

 Performance Evaluation

 Demonstration of the URANUS Validation Platform



Outline

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 Main Objective the URANUS Validation Platform




 Building Blocks





Outline

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Main Objective of the URANUS Validation Platform /1

 Proof of Viability and Feasibility of the URANUS GMCR Concept on a Commercially Available Hardware, the Greenside Platform

 Software and Hardware Complexity Analysis of the URANUS GMCR

Concept

 Identification of Advantages of the URANUS GMCR Concept compared to conventional realisations

 Industrial Exploitation Opportunities, i.e.

Application Specific Instruction Set Processor (ASIP) Integration

Ready Concept



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Main Objective of the URANUS Validation Platform /2

 The main task for the URANUS Validation Platform is to develop a flexible validation platform for key components of the URANUS

concept.

 To reach this goal the work plan foresees different steps:

– Specification of the building blocks.

– Architectural exploration of key building blocks.

– Design of building blocks for the integration into the validation platform.

– Baseband validation of the URANUS transceiver ‘prototype’ under realistic conditions.



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 Architecture Paradigms



 Building Blocks





Outline

1

2 u

1 – multiple HW

1

SW HW

2

SW HW

1

2

1

2

Conceivable Architectures

2 – single HW

SW

HW

HW

1

2

HW

4 – Uranus 3 – generic SDR

SW HW

1

2

CPD

1

2

SW

HW

1

2



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Pros and Cons at a Glance

Area

Power

RT Reconfig.

NRT Reconfig.

IPs Reusability

Upgradeability

Time to Market

Testability

Mult. HW Single HW Generic

SDR

High

High

Low

Low

Low

Low

Medium

Medium

URANUS

Low

Low

Medium High

Low Low High High

Low

Low

Low

Low

Medium Low

Predef.

Predef.

High

High

Low

High

High

High

Low

High



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 URANUS Concept Validation


 Building Blocks





Outline

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Petri Net Based Design Flow

Specification

(systems, standards)

Flexibility

→ parametrizable HW

Technologies

(e.g. ASIC, FPGA, DSP)

Requirements

(users, services)

System Design

Scalability

→ modular HW accelerators

Adaptivity

→ URANUS: CPD/GMCR

Simulation/Implementation

OK?

Yes

Verification/Demonstrator

Yes

OK?

No

No

Product Realization

Legend:

ASIC: Application Specific Integrated Circuit

CPD: Canonical Parametric Description

DSP: Digital Signal Processor

FPGA: Field Programmable Gate Array

GMCR: Generalized Multi-Carrier Representation

HW: Hardware



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MODE II:

WiMAX Improved

Outer Modem Profile

 high rate

WIMAX services

Five Demonstrator Modes

MODE I:

Real-Time Short-

Range/Indoor Profile

 UMTS/W-CDMA

384 kbit/s service

 WiMAX services up to ≈ 20 Mbit/s

 user defined mode

MODE V:

Research Demo

ASIP

 Architectural study of the outer modem

MODE III:

Advanced GMC

Hardware Centric

 HW implementation and optimization of

HW/FW/SW split of

(future) GMCR

MODE IV:

Research Demo

Advanced Sync

 FW implementation of the advanced

GMCR based synchronization



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Outline



 Transceiver Structure

(Hardware Architecture, Protocol Stack Architecture, Building

Blocks, Interfaces Between Building Blocks)

 Building Blocks





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Hardware Architecture: Laboratory Set-up



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Hardware Architecture: ST GreenSIDE Platform

JTAG Connector

Greenside ASSP

FPGA

Interface Connector

SRAM

FLASH

Dual Ethernet

JTAG: Joint Test Action Group

FPGA: Fiele programmable gate array

SRAM: Static Random Acess Memory

ASSP: Application specific standard product



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Protocol Stack Architecture

ARM926 

ST140 DSPs 

FPGA 

ST140 DSPs 

Application Layer

- Mode of operation selection

- QoS measurement presentation and reports

- content provision (audio/video, web browsing)

Transport & Networking Layers

- Ethernet/IP drivers

- USB drivers

Radio Resource Management

- Mode of operation / link control

- Front-end control

Outer Modem

- FEC

MAC & DLC

Inner Modem

- CPD/GMCR simple point-to-point connectivity not standard specific

ARQ for packet access standard specific definition of building blocks



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Building Blocks: Scheduling



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Building Blocks: Transmitter



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Building Blocks: Receiver



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Interfaces Between Building Blocks: Demo

Mode I



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Interfaces Between Building Blocks: Demo

Mode II



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Interfaces Between Building Blocks: Demo Mode III



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Demo Mode III Complexity

Module VHDL Quantity Slices

2032

272

2469

4

4

Block RAM

1k x 18 bits

4

Multiplier

18bits x 18bits

9 AFB y

CPICH generator

Pilot waveform

Generator

Signal waveform generator

Walsh-Hadamard code

Signal Gen window

2

Correlator 2

Hadamard multiplication 2

Channel estimation

Input from DSP

Output to DSP

Total Module Leti

Filling

2

2476

18

53

41

27

2841

126

103

11996

83%

4

5

9

9

4

4

6 4

6

1

31

32%

58

60%



XC2V3000 :

14336 slices

96 BRAM

96 Multipliers

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Demo Mode IV Architecture

 The sync block itself does the matched filtering operation using

STFT principle

– A generalized frequency domain filtering process

– Windows and overlapping are possible unlike in the usual frequency domain processing

Block diagram of the operations of the SYNC unit



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Demo Mode V: Flexi ble Tre llis P rocessor (FlexiTreP)

 Exploit programmability

– Simple programming model

– Decoding algorithms e.g. Log-MAP, Viterbi (control flow)

 Exploit hardware reconfigurability (data management)

– Fast context switching

– Multi context instructions: simplifies instructions & reduces program size

  Partially dynamically reconfigurable ASIP

 Specific application: application knowledge is key

– Full ASIP approach i.e. no predefined configurable pipeline template

– „Just enough flexibility“: energy efficiency

– Assembler code



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Outline





 Building Blocks

(Application Layer, Transport & Network Layers, Radio Resource

Management, Medium Access Control & Logical Link Control,

Outer Modem, DSP Based Inner Modem, FPGA Based Inner

Modem)





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ARM Software (Uranus library)

Ethernet Driver: 2 Ethernet interfaces

ARP: Translation IP/ethernet hardware address

IP: IP fragmentation not managed automatically liburanus.a

System clock

ARP

Message passing to/from DSP

Time-stamper

High level protocol messages

RRM, MAC & LLC

UDP

, APP

UDP: 1472 bytes of payload maximum

High level messages:

APP  Application

RRM  Radio Resources Management

MAC & LLC  Medium Access Control &

Logical Link Control

Time-stamper: Enables the measurement of the time elapsed between different events with a precision is 50 microseconds

IP

Ethernet driver

System clock: Enables the programming of alarms with a precision of milliseconds

Message passing to and from the DSPs:

Exchange of information and commands between the

ARM processor and the DSPs.

To be implemented during the integration.



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GUI Main Window

There are controls for:

•

Editing CPD parameters

• Managing ARM internal slots

• Creating automatic test sequences

• Controlling Tests

•

Creating Video-streaming sequences

•

Controlling the videostreaming

•

Launching the real-time display



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CPD Configuration/Edition (1 of 3)



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Main window after editing a CPD set

No Changes in main window

The CPD sets are stored in files that can be used in other places of the GUI



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Internal ARM slots configuration



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Main window after configuring the ARM slots

The name of the Slots configuration in use is displayed (and stored in a file for future reuse)



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Automatic test sequence configuration



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Main window after Test configuration

The controls for the test are activated. Enabling the user to:

• Actual upload of the configuration to the platform

• Step by step execution of the test

• Run the automatic test

• Pause the execution of an automatic test

• Retrieve the test results from the ARM internal slots.



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Video streaming configuration



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Main window after video streaming configuration

The controls for the videostreaming control are activated. Enabling the user to:

• Actual upload of the configuration to the platform

• Run/stop the platform behaving as a videostreaming bridge



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GUI: Real-time display



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 Building Blocks

 Performance Evaluation




Outline

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Verification Process



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Selected Performance Results



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Video Streaming



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Demonstration of the

URANUS Validation Platform

