Introduction to a Multichannel Noncoherent Autocorrelation ... · TU Graz - Signal Processing and...
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TU Graz - Signal Processing and Speech Communication Laboratory
Introduction to a Multichannel NoncoherentAutocorrelation UWB Receiver using OFDM
Andreas Pedross, Klaus Witrisal
Signal Processing and Speech Communication Laboratory
May 05, 2011
Andreas Pedross, Klaus Witrisal May 05, 2011 page 1/23
TU Graz - Signal Processing and Speech Communication Laboratory
Outline
Introduction
Architecture
Receiver Signal Processing
Conclusions
Andreas Pedross, Klaus Witrisal May 05, 2011 page 2/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1.
2.
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1. High Data-Rate: 437.5 Mbps
2.
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1. High Data-Rate: 437.5 Mbps
2. Low-Power!
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1. High Data-Rate: 437.5 Mbps
2. Low-Power!
How to achieve?
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1. High Data-Rate: 437.5 Mbps
2. Low-Power!
How to achieve?
◮ Ultra-Wide Band: BW 1.75 GHz @ 4 GHz center
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Introduction – What’s the goal?
Receiver Design Constraints
We want:
1. High Data-Rate: 437.5 Mbps
2. Low-Power!
How to achieve?
◮ Ultra-Wide Band: BW 1.75 GHz @ 4 GHz center
◮ Multidimensional Noncoherent Signaling Scheme!
Andreas Pedross, Klaus Witrisal May 05, 2011 page 3/23
TU Graz - Signal Processing and Speech Communication Laboratory
Outline
Introduction
Architecture
Receiver Signal Processing
Conclusions
Andreas Pedross, Klaus Witrisal May 05, 2011 page 4/23
TU Graz - Signal Processing and Speech Communication Laboratory
Architecture – General Signal (1)
Signaling Scheme
s(t) = ℜ
e+jωct
+ K−1
2∑
k=−K−1
2
skΘk(t)
with:
◮ ωc ... carrier frequency
◮ K ... dimension of signal space
◮ Θk(t) ... k-th orthogonal basis function
◮ sk ... k-th symbol element
Andreas Pedross, Klaus Witrisal May 05, 2011 page 5/23
TU Graz - Signal Processing and Speech Communication Laboratory
Architecture – General Signal (2)
Received Signal
r(t) = hch(t) ∗ s(t) + n(t)
= ℜ
e+jωct
+ K−1
2∑
k=−K−1
2
skΘ̃k(t)
+ n(t)
with:
◮ hch(t) ... communication channel
◮ Θ̃k(t) ... k-th convolved basis function
◮ n(t) ... noise
Andreas Pedross, Klaus Witrisal May 05, 2011 page 6/23
TU Graz - Signal Processing and Speech Communication Laboratory
Architecture – General Signal (3)
Which Noncoherent Receiver Design?
◮ Energy Detector?
z[n] =
∫ nTs
λ=(n−1)Ts
r2(λ)dλ
No separation between basis functions possible!
◮ Autocorrelation?
zk[n] =
∫ nTs
λ=(n−1)Ts
Θ̃k(λ)Θ̃∗k(λ − τm)dλ
Seems feasible...
Andreas Pedross, Klaus Witrisal May 05, 2011 page 7/23
TU Graz - Signal Processing and Speech Communication Laboratory
Architecture – System View
Linear
Combiner
Front End
Filterτ0
τ1
τ2
τ3
τ4
LP
ADC
ADC
ADC
ADC
ADC
ADC
LP
LP I&D
LP I&D
LP I&D
LP I&D
LP I&D
LP I&D
ADCI&D
ADCI&D
Andreas Pedross, Klaus Witrisal May 05, 2011 page 8/23
TU Graz - Signal Processing and Speech Communication Laboratory
Architecture – System Parameters
Receiver
◮ 8x AcR Channels (4x I&Q)
◮ Delay Lines: 1x 62.5 ps, 1x 250 ps, and 3x 500 ps
Signaling
◮ OFDM
◮ 4 GHz Carrier
◮ 7 Sub-Carriers, 250 MHz spacing
◮ 16 ns Symbol Period with Zero Guard Interval
◮ Truncated Root Raised Cosine Pulses
◮ OOK/PPM
Andreas Pedross, Klaus Witrisal May 05, 2011 page 9/23
TU Graz - Signal Processing and Speech Communication Laboratory
Outline
Introduction
Architecture
Receiver Signal Processing
Conclusions
Andreas Pedross, Klaus Witrisal May 05, 2011 page 10/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – OFDM
Signaling Scheme
s(t) = ℜ
e+jωct
+ K−1
2∑
k=−K−1
2
skϕ(t)e+jkωsct
with:
◮ ωc ... center frequency
◮ ωsc ... sub-carrier spacing
◮ K ... number of sub-carriers
◮ ϕ(t) ... pulse shape
◮ sk ... k-th symbol element
Andreas Pedross, Klaus Witrisal May 05, 2011 page 11/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Signaling
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
x 10−8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time [s]
ϕ(t
)
−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1
x 109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency [Hz]
magnitude o
f baseband s
ub−
carr
ier
puls
es
(left) Baseband Pulse in Time Domain, (right) BasebandSub-Carriers in Frequency Domain
Andreas Pedross, Klaus Witrisal May 05, 2011 page 12/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (1)
Delay & Multiplication
xm(t) = r(t) · r(t − τm)
=1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃∗l (t − τm)e+jωcτm
}
+
+1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃l(t − τm)e+2jωcte−jωcτm
}
+
+∑
k
skℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)+
+∑
l
slℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)+
+ n(t) · n(t − τm)
Andreas Pedross, Klaus Witrisal May 05, 2011 page 13/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (1)
Delay & Multiplication
xm(t) = r(t) · r(t − τm)
=1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃∗l (t − τm)e+jωcτm
}
+
+1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃l(t − τm)e+2jωcte−jωcτm
}
+
+∑
k
skℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)+
+∑
l
slℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)+
+ n(t) · n(t − τm)
Andreas Pedross, Klaus Witrisal May 05, 2011 page 13/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (1)
Delay & Multiplication
xm(t) = r(t) · r(t − τm)
=1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃∗l (t − τm)e+jωcτm
}
+
+1
2
∑
k
∑
l
skslℜ{
Θ̃k(t)Θ̃l(t − τm)e+2jωcte−jωcτm
}
+
+∑
k
skℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)+
+∑
l
slℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)+
+ n(t) · n(t − τm)
Andreas Pedross, Klaus Witrisal May 05, 2011 page 13/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (2)
Lowpass-Filtering
x̃m(t) = hlp(t) ∗ xm(t)
≈1
2
∑
k
s2kℜ
{
hlp(t) ∗[
Θ̃k(t)Θ̃∗k(t − τm)
]
e+jωcτm
}
+
+1
2
∑
k
∑
l,l 6=k
skslℜ{
hlp(t) ∗[
Θ̃k(t)Θ̃∗l (t − τm)
]
e+jωcτm
}
+
+∑
k
skhlp(t) ∗[
ℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)]
+
+∑
l
slhlp(t) ∗[
ℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)]
+
+ hlp(t) ∗ [n(t) · n(t − τm)]
Andreas Pedross, Klaus Witrisal May 05, 2011 page 14/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (2)
Lowpass-Filtering
x̃m(t) = hlp(t) ∗ xm(t)
≈1
2
∑
k
s2kℜ
{
hlp(t) ∗[
Θ̃k(t)Θ̃∗k(t − τm)
]
e+jωcτm
}
+
+1
2
∑
k
∑
l,l 6=k
skslℜ{
hlp(t) ∗[
Θ̃k(t)Θ̃∗l (t − τm)
]
e+jωcτm
}
+
+∑
k
skhlp(t) ∗[
ℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)]
+
+∑
l
slhlp(t) ∗[
ℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)]
+
+ hlp(t) ∗ [n(t) · n(t − τm)]
Andreas Pedross, Klaus Witrisal May 05, 2011 page 14/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (2)
Lowpass-Filtering
x̃m(t) = hlp(t) ∗ xm(t)
≈1
2
∑
k
s2kℜ
{
hlp(t) ∗[
Θ̃k(t)Θ̃∗k(t − τm)
]
e+jωcτm
}
+
+1
2
∑
k
∑
l,l 6=k
skslℜ{
hlp(t) ∗[
Θ̃k(t)Θ̃∗l (t − τm)
]
e+jωcτm
}
+
+∑
k
skhlp(t) ∗[
ℜ{
Θ̃k(t)e+jωct
}
· n(t − τm)]
+
+∑
l
slhlp(t) ∗[
ℜ{
Θ̃l(t − τm)e+jωcte−jωcτm
}
· n(t)]
+
+ hlp(t) ∗ [n(t) · n(t − τm)]
Andreas Pedross, Klaus Witrisal May 05, 2011 page 14/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Mixers
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
x 109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency [Hz]
magnitude o
f co−
and c
ross−
term
s
Mixing Product
Andreas Pedross, Klaus Witrisal May 05, 2011 page 15/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Mixers
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
x 109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency [Hz]
magnitude o
f co−
and c
ross−
term
s
Mixing Product
Andreas Pedross, Klaus Witrisal May 05, 2011 page 15/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (3)
Integrate ... and Dump
ym[n] = ym(nTs)
= hint(t) ∗ x̃m(t)|t=nTs
≈1
2
∑
k
s2kℜ
{
hlp(t) ∗ Φ̃mkk(nTs)e
+jωcτm
}
+
+1
2
∑
k
∑
l,l 6=k
skslℜ{
hlp(t) ∗ Φ̃mkl(nTs)e
+jωcτm
}
+
+ νs×n(nTs)+
+ νn×s(nTs)+
+ νn×n(nTs)
Andreas Pedross, Klaus Witrisal May 05, 2011 page 16/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Autocorrelation (4)
with the Autocorrelation Function
Φ̃mkl(t) = hint(t) ∗
[
Θ̃k(λ)Θ̃∗l (λ − τm)
]
=
∫ t
λ=t−Tint
Θ̃k(λ)Θ̃∗l (λ − τm)dλ
and Ideal Integration Filter
hint(t) = σ(t) − σ(t − Tint)
σ(t)... Heaviside function
Andreas Pedross, Klaus Witrisal May 05, 2011 page 17/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Integrate & Dump
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
x 109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency [Hz]
magnitude o
f co−
and c
ross−
term
s
Signal after Integration
Andreas Pedross, Klaus Witrisal May 05, 2011 page 18/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Integrate & Dump
−2 −1.5 −1 −0.5 0 0.5 1 1.5 2
x 109
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
frequency [Hz]
magnitude o
f co−
and c
ross−
term
s
Signal after Integration
Andreas Pedross, Klaus Witrisal May 05, 2011 page 18/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – MIMO
Typical Memoryless MIMO System
y[n] = Hs[n] + G (s[n] ⊗ s[n]) + ν[n]
G vanishes for orthogonal basis functions!
Basis Function Separation
Separation possible for H with rank K: use Linear Combiner W!
z[n] = Wy[n]
= WHs[n] + Wν[n]
Design Methods for W: ZF, MMSE, ...
Andreas Pedross, Klaus Witrisal May 05, 2011 page 19/23
TU Graz - Signal Processing and Speech Communication Laboratory
Receiver Signal Processing – Performance
4 6 8 10 12 14 16 18 20 22 2410
−4
10−3
10−2
10−1
100
avera
ge b
it−
err
or−
rate
BE
R
average Eb/N
0 [dB]
MC UWB w/ MMSE
MC UWB w/ ZF
wideband UWB w/ ED
UWB subcarrier w/ ED
Andreas Pedross, Klaus Witrisal May 05, 2011 page 20/23
TU Graz - Signal Processing and Speech Communication Laboratory
Outline
Introduction
Architecture
Receiver Signal Processing
Conclusions
Andreas Pedross, Klaus Witrisal May 05, 2011 page 21/23
TU Graz - Signal Processing and Speech Communication Laboratory
Conclusions – Finally!?
◮ A Noncoherent Multichannel Autocorrelation Receiver wasintroduced
◮ We took a close look on the Receiver Signal Processing
◮ Receiver Architecture shows a promissing performance
Andreas Pedross, Klaus Witrisal May 05, 2011 page 22/23
TU Graz - Signal Processing and Speech Communication Laboratory
Conclusions – Finally!?
◮ A Noncoherent Multichannel Autocorrelation Receiver wasintroduced
◮ We took a close look on the Receiver Signal Processing
◮ Receiver Architecture shows a promissing performanceEspecially for Noncoherent OFDM!!Even for not perfectly orthogonal basis functions!!
Andreas Pedross, Klaus Witrisal May 05, 2011 page 22/23
TU Graz - Signal Processing and Speech Communication Laboratory
References
K. Witrisal,
“Noncoherent Autocorrelation Detection of Orthogonal Multicarrier UWB signals“,IEEE International Conference on Ultra-Wideband 2008, Sept. 2008, vo. 2, pp. 161–164.
P. Meissner, and K. Witrisal,
“Analysis of a Noncoherent UWB Receiver for Multichannel Signals”,IEEE Vehicular Technology Conference, VTC2010-Spring, May 2010.
K. Witrisal, G. Leus, G. Janssen, M. Pausini, F. Troesch, T. Zasowski, and J. Romme,
“Noncoherent Ultra-Wideband Systems“,IEEE Signal Processing Magazine, vol. 26, no. 4, p. 48–66, July 2009.
Andreas Pedross, Klaus Witrisal May 05, 2011 page 23/23