Physical layer network coding for next generation wireless
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Transcript Physical layer network coding for next generation wireless
Physical layer network
coding for next generation
wireless broadband
Alister Burr, University of York
[email protected]
Agisilaos Papadogiannis, Chalmers University
11th March 2010
1
Outline
The challenge of next generation wireless
networks
Next generation network architectures
MIMO and MIMO cellular
Multi-user and Network MIMO
Physical layer network coding
Conclusions
11th March 2010
2
Wireless networks – some history
Began with Marconi in 1890’s
…
1st generation
2nd generation
digital, some data ~1992
3rd generation
analogue, telephony ~1980 (Japan)
CDMA, flexible services, up to 384 kbit/s ~2002
4th generation
OFDM(A), full Internet access, up to 1 Gbit/s ???
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4G
The “next generation” has been discussed ever
since 3G standards were finalised a decade ago
However starting from 2002 ITU-R has defined
the requirements for IMT-Advanced
however it was not initially clear what form the
“fourth generation” might take
which has since then been generally accepted as
the definition of 4G
Key requirement is 100 Mbit/s for high mobility
and 1 Gbit/s for low mobility
Standards currently under development:
Mobile WiMAX (IEEE 802.16e/m)
3GPP LTE-Advanced
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The dream
To provide full Internet connectivity to everyone,
anywhere
which means wirelessly
Next generation wireless research has usually
focussed on a ‘headline’ maximum data rate
but of course this will not be the rate most users
experience,
and probably is not the most important figure
In densely-populated cities a network for everyone
must provide extremely high capacity densities
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Required capacity density
Average population density in European
cities ranges from 3400 - 5400/km2
however in commercial district in working
hours it will be much higher
say 8000/km2
Suppose 10% subscribe, and 20% of those
require access at busy hour
Expected data rate 5 Mbit/s
8000/km2 10% 20% 5 Mbit/s = 800 Mbit/s/km2
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Capacity density of 1G
e.g. AMPS, U.S.A:
~400 channels in each direction
~15 km radius cells 700 km2
re-use ~1:10 0.06 channels/km2,
equivalent
to approx 1.8 kbit/s/km2
in approx 50 MHz
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Current and 4G systems
Currently one base station serves about
1km2
4G bandwidths proposed are ~ 40 MHz
Best available bandwidth efficiency
averages about 2 bits/s/Hz across cell
hence capacity density is 80 Mbit/s/km2
assumes 100% frequency re-use
We need an order of magnitude more!
10 more bandwidth unlikely to be available
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Increased density
We can also
increase
number of cells
BUT
need many
more cell sites
interference
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Cell edge problem
We may be able to increase
bandwidth efficiency
(bits/s/Hz)
use (e.g) advanced MIMO
techniques
BUT mobile close to cell
edge suffers interference
from adjacent cells
Conventionally we reduce
frequency re-use
but this reduces available
bandwidth by factor 3 or
more
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BuNGee
University of York is part of a European project
tackling these problems
Proposes:
Beyond Next Generation Mobile Broadband
(BuNGee)
new hierarchical network architecture based on
wireless backhaul
Advanced MIMO techniques for high bandwidth
efficiency
Self-organising network for
optimal spectrum use
Goal: 1 Gbit/s/km2
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11
Outline
The challenge of next generation wireless
networks
Next generation network architectures
MIMO and MIMO cellular
Multi-user and Network MIMO
Physical layer network coding
Conclusions
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12
Achieving capacity density
Will probably need a combination of the
approaches mentioned:
More spectrum
Improved bandwidth efficiency
especially
increased use of MIMO
Increased frequency re-use (100%)
Reduced cell size
requires
low base station installation cost,
and a cost-effective backhaul network
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Wireless backhaul
Simple comparison with 4G proposals
suggests we may need ~10 BSs per km2!
We believe that the only cost-effective way
to provide this is by wireless backhaul
However must allow for spectrum used by
backhaul links
Hence must minimise backhaul load
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BuNGee architecture
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Some figures
Assume HBS serves 1 km2
Assume total 40 MHz available
20 MHz for MS-ABS (access links);
20 MHz ABS-HBS (backhaul)
Assume average 2 bits/s/Hz across cell
Then capacity per ABS = 20 2 = 40 Mbit/s
No. ABS per HBS = 1 Gbit/s / 40 Mbit/s = 25
Area served by ABS = 1 km2/25 = 40 000 m2, or
200m square
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Wireless mesh network
Since cells are very small,
mobile (MS) may be
served by more than one
ABS
MSs now served by
optimum combination of
available ABSs
Practically abolishes
HBS
concept of cells!
Overall network looks more
like a wireless mesh
network
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ABS
MS
ABS
MS
Relay
ABS
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Wireless relaying
A cell can be extended
by adding fixed, or
infrastructure relays
very similar
architecture to wireless
backhaul
with relays replacing
ABSs
may allow direct
connection of MSs to
hub
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Relay
Relay
18
Hierarchical wireless network
A generalised framework
for network architectures
involving wireless backhaul
and/or relaying
we might allow:
more than one layer of
relays
direct connections between
nodes on the same level
MSs to connect to different
relay levels
Again, similar to mesh
network in structure
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Outline
The challenge of next generation wireless
networks
Next generation network architectures
MIMO and MIMO cellular
Multi-user and Network MIMO
Physical layer network coding
Conclusions
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20
MIMO link model
ENCODER
nT
H21
HnR1
s
MIMO = Multiple Input, Multiple Output
H11
DECODER
n
R
r
i.e. multiple antennas at each end of a link
Input and output signal can be modelled as (1 nT) and
(1 nR) vectors, s and r; noise (1 nR): n
channel modelled as a matrix H: element Hij gives propagation
between transmit antenna j and receive antenna i
r=Hs+n
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Eigendecomposition
n
1
s'
nT
H
r
n
n
r'
VH
nR
Multiply by matrices U and V at input and output of channel, where
U
s
columns of U,V are transmit and receive eigenvectors of the channel
Then U H VH =
a diagonal matrix with the square roots of the eigenvalues of HHH on the
diagonal, and r’i = i s’i + n’i
i.e. we create a set of uncoupled channels, whose power gains are the
eigenvalues
Each eigenvector can be treated as a steering vector for antenna array
transmit/receive beam patterns
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Beamforming model
n
s'
U
VH
r'
Another way of viewing MIMO:
each input stream corresponds to a beam from the
antenna array towards a multipath signal
can create as many such beams as there are
antennas
hence can transmit up to n = min(nT, nR) beams
provided there are enough multipaths
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MIMO capacity
Capacity and
bandwidth efficiency
approximately
multiplied by no. of
streams, n
Slope of curves
proportional to n
called multiplexing
gain
Dramatic capacity
gain!
35
25
20
15
10
5
0
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nT=4 nR=4
nT=2 nR=4
nT=1 nR=4
nT=4 nR=2
nT=2 nR=2
nT=4 nR=1
nT=1 nR=1
30
Capacity (bits/s/Hz)
Rayeigh capacity
0
5
10
15
SNR(dB)
20
25
30
24
MIMO in interference
Uint
d int
sint
Beamformer
H int
r
d
U
s
H
n
W
x
V
dˆ
matched
filter
MIMO cellular system also subject to interference
Beamformer applies linear weights to maximise
SNIR at output
filters signals from different directions to maximise
signal to noise-plus-interference ratio
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Capacity of MIMO cellular
4 antenna
elements
MIMO system
capacity around
3 SISO
and more than
1.5 SIMO
(smart antenna)
Beamformer
(“prewhitening”)
very important
Interference
limited
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Implications for 4G networks
MIMO can dramatically increase link
capacity
and significantly increase cellular capacity
Note that capacity is mainly affected by
n = min(nT, nR)
Still severely limited by inter-cell
interference
Can we reduce the effect of interference?
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Outline
The challenge of next generation wireless
networks
Next generation network architectures
MIMO and MIMO cellular
Multi-user and Network MIMO
Physical layer network coding
Conclusions
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Multi-user MIMO systems
It has been known since the 1960s that the
optimum means of sharing a channel
between several users may be by
simultaneous, mutually interfering
transmission
as opposed to time-division multiplexing, or
other orthogonal multiplexing
Information-theoretic approach:
Multiple Access Channel - MAC (uplink)
Broadcast Channel - BC (downlink)
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2-user SISO MAC
R1 C1 I X1 ; Y X 2 log 2 1 P1 N
Rate region: set of
achievable rates of the two
R2 C2 I X 2 ; Y X1 log 2 1 P2 N
sources
R1 R2 Csum I X1 , X 2 ; Y
C1 and C2 are capacities of
log 2 1 P1 P2 N
two channels without the
R2
other
“Corner points” achieved
by successive interference
cancellation
“Time sharing” sum rate
limited to dashed line
C2
time-sharing
rate
Csum
in general achievable sum
rate Csum exceeds this
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C1
R301
Multi-user MIMO MAC
In a multi-user MIMO multiple access channel
(uplink),
sum rate capacity limit is capacity of MIMO channel
formed by combining all Tx antennas of all users
For nU users with nT Tx antennas, average Tx
power Si and Tx time Ti (each)
CTS
where:
nU
S
i 1 i
S ; i U1Ti T ;
n
1 nU
Ti CnT ,nR
T i 1
Csum CnU nT ,nR
T
Si CnT ,nR S
Ti
C
nU
S
i 1 i
nU nT ,nR
S
CnT ,nR S denotes capacity of nTnR MIMO channel
with SNR S
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Multi-user MIMO
Conventional TDMA/FDMA is equivalent to timesharing
divides “headline” rate by no. of channels
MU-MIMO allows several users to share same
time slot/channel
Users/BS can act as a single nT nU nR MIMO
system
nT
Usually more BS
than terminal antennas
multiplexing gain no
longer limited by
no. terminal antennas
nU
BS
nR
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Sum rate (black) and time-share capacity (red), 8 users, nT=4, nR=4
40
Sum rate (black) and time-share capacity (red), 8 users, nT=2, nR=8
80
35
70
30
60
Capacity (bits/s/Hz)
Capacity (bits/s/Hz)
Symmetric and asymmetric
25
20
15
50
40
30
10
20
5
10
0
0
5
10
15
SNR (dB)
20
25
30
0
0
5
10
15
SNR (dB)
20
25
30
Modest advantage when nT = nR (symmetric links)
Large advantage when nT nR (asymmetric links)
max. multiplexing gain becomes min(nR, nTnU)
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Broadcast channel
Broadcast channel simply means one transmitter
to many receivers
obvious example is radio/TV broadcasting, where
same message is intended for all
also applies to cellular downlink, where a different
message is intended for each receiver
R2
In latter case can define a
C2
capacity region like that for
multi-user MIMO
time-sharing
Csum
rate
Again time sharing
(TDMA/FDMA) is sub-optimal
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C1
R
341
Dirty paper coding (DPC)
This is an information-theoretic result which applies to a
channel subject to interference where the interference is
known at the transmitter
Achievable capacity of this channel is
log2(1 + PS/Pn)
r=s+i+n
where s is the information signal, i is the interference, and n is
noise
i.e. the same capacity as if the interference were not present
note this is not achieved by “pre-cancelling” the interference
On a broadcast downlink, the signal to one user is
interference to another, and is known at the transmitter
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Precoding in practice
Dirty paper precoding in principle
operates by selecting a codebook
(set of transmitted codewords)
depending on the interference
A more practical scheme following
the same principle is TomlinsonHarashima precoding (THP)
Use modulo function f(x), and
transmit:
where d is data, i is interference and
k is some integer
At the receiver apply the modulo
operation again:
interference is removed
some degradation due to “folding” of
noise
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f(x)
x
A
A A
f x mod x , A
2 2
s f d i d i kA
d f r f s i n
f d n kA
d n k A
36
Linear beamforming
Or simpler still, we can simply form beams to
each user
ensuring also that we null interference to other
users
this is a purely linear operation
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Network MIMO
So far we have assumed that signals from
other cells must be treated as interference
However it is possible for several (in
principle, all) base stations to cooperate to
transmit to a given mobile
Then there is in principle no CCI!
or to receive from that mobile
since all received signals are exploited as
signals
The entire system then operates as a multiuser MIMO system with (on the uplink)
nTnU nC transmit and nRnC receive
antennas
where nC is the number of cooperating
cells
in principle multiplexing gain approaches
min(nRnC, nTnU nC)
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Practical limitations
There are of course practical limitations to this
concept:
Where should the processing be performed?
In a hierarchical network like BuNGee, at the hub
Distributed methods also possible, with processing
at cooperating BSs
Computational complexity
Synchronisation
Is it feasible to keep cooperating base stations
phase synchronous? (especially for downlink)
Backhaul capacity
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Backhaul capacity requirements
On the downlink, if two BSs cooperate to
communicate with an MS, that MS’s data
should be sent to both
On the uplink, neither may be able to
decode the MS without the signal from
the other
could double backhaul requirements
hence analogue signal may need to be
transmitted over the backhaul in high
precision
may increase backhaul requirements by
several times
b1s1
b2s2
r1
r2
s1
s2
Need to ensure backhaul links are
efficiently used
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a1 r1 + a2 r2
40
Limitations of in-band backhauling
If we use wireless
backhaul, we must
account for bandwidth
occupied
Can we re-use the same
spectrum in backhaul and
access segments?
in-band backhauling
Duplexing restrictions of
ABSs usually prevent
same resources being
used in the two segments
in the same place
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Outline
The challenge of next generation wireless
networks
Next generation network architectures
MIMO and MIMO cellular
Multi-user and Network MIMO
Physical layer network coding
Conclusions
11th March 2010
42
Network coding
A network node applies a joint
coding function to two (or more)
incoming data streams
instead of simply switching between
them
In this simple example (the “butterfly
network”) the central node applies
the XOR function (modulo-2
addition)
then both streams can be recovered
at both output nodes
without network coding the central
link would have to have twice the
capacity to achieve this
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Two-way relay channel (2WRC)
SA
DB
b
a
ab
R
ab
SB
DA
Allows a relay to support transmissions in two
directions at once
Relay broadcasts XOR combination of two
incoming streams
Each destination can then reconstruct data
intended for it by XOR combination with the data
it transmitted
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Physical layer network coding
(PLNC)
In a wireless network, we do not have
discrete, non-interfering paths
except by using TDMA or FDMA
Signals:
are broadcast to all nodes within range
combine additively in signal space
However it is still possible to extract a joint
information stream equivalent to XOR
combination
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PLNC for 2WRC
a
DB
ab
b
a+b
R
System operates in two
phases
SA
Phase 1: sources transmit
simultaneously
Phase 2: relay transmits
Assume both sources
transmit BPSK
{+1, -1} {1, 0}
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ab
SB
DA
Phase 1
Phase 2
SA, SB
SA, SB
time
a
0
0
1
1
b
0
1
0
1
a+b
-2
0
0
+2
ab
0
1
1
0
46
For comparison
SA
DB
a
ab
Without network
coding
Network layer
network coding
Physical layer
network coding
R
b
SB
ab
DA
SA
SB
R(A)
R(B)
time
SA
SB
R(AB)
time
SA, SB
R(AB)
time
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Relay data compression
R
S
Cooperative diversity: relay provides extra diversity if S –
D link fades
Phase 1: Source transmits to relay and destination
Phase 2: Relay transmits to destination
Note signal at relay is correlated with Phase 1 signal at
destination
D
since both arise from the same data
This allows distributed compression using Slepian-Wolf
coding
reduces
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the data relay must transmit
48
Slepian-Wolf coding
If two data sources are
correlated, their joint
information content is less
than the sum of their
separate content
Can exploit this to
compress the data
S1
S2
C1
C2
R1
R2
S’1
D
S’2
R2
even though encoders
are separate
White area on graph gives
rate region
range of possible
compressed rates to
allow reconstruction
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R1
49
PLNC in network MIMO
T1
Example:
2 terminals connected
to hub via 3 BS
B1
T2
B2
B3
B2 can use PLNC, to share
its link with the hub between
Hub
two terminals
It can use distributed compression, since
the data is correlated with that via B1 and
B3
Reduces backhaul load
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Two-way relaying in hierarchical
wireless network
Duplexing constraints can be
alleviated using 2-way relaying
allows access and backhaul
resource to be shared at each ABS
however neighbouring ABSs and
corresponding MSs may interfere
We have analysed this
considering a simple scenario
Assume 2 ABSs share resources
over 2 slots
i.e. both ABSs and MSs transmit
simultaneously on same channel
consider amplify-and-forward and
network coded decode-and-forward
relaying
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Capacity results
Plot resulting capacity
against strength of
interfering links
for small interference
DF is better
for large interference
AF capacity increases
again
both MSs can exploit
both ABSs
exploits a form of
network MIMO
Generally 2 slots are
better than three or four
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Conclusions (1)
Next generation wireless networks will be
required to handle much larger capacity densities
MIMO techniques can greatly increase link
capacity
Multi-user MIMO can greatly increase capacities
but still seriously affected by interference in cellular
networks
especially for asymmetric systems
Network MIMO can further increase capacity
and also largely eliminate interference
however increases backhaul load
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Conclusions (2)
Physical Layer Network Coding can better
exploit backhaul capacity
by allowing backhaul links to be shared
by exploiting correlation of signals travelling by
different routes
by overcoming duplexing constraints on spectrum
sharing
All these techniques will be essential elements of
next generation networks!
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Acknowledgements
With thanks to our collaborators :
Prof. Jan Sykora, Czech Technical University,
Prague
Prof. Tad Matsumoto, JAIST, Japan
Prof. Meixia Tao, Shanghai Jiao Tong University
and funders:
European Commission, FP7 project “BuNGee”
RCUK “Sciences Bridges China” programme
COST 2100 programme
Czech Technical University
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