the wireless data crunch: motivating research in wireless
Post on 03-Feb-2022
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The Wireless Data Crunch: Motivating Research in Wireless
Communications
Stephen Hanly
CSIRO-Macquarie University Chair in Wireless Communications
stephen.hanly@mq.edu.au
Wireless Growth Rate • Cooper’s law:
Number of “conversations” (voice or data) over a given area in
all of the useful radio spectrum has doubled every two-and-a-
half years for the past 104 years.
Wireless Growth Rate
1,000,000 x improvement in 45 years:
25 x ← due to more spectrum
5 x ← dividing spectrum into
narrower slices (FDM)
5 x ← advanced modulation and
coding techniques
1600 x ← frequency re-use
Wireless Data Crunch
In Feb. 2012, CISCO predicts:
• Mobile data demand will increase by 18 x between 2011-2016
By 2016:
• 2/3 mobile data traffic will be video
• Mobile connection speeds will need to increase by 9 x
• 1.4 mobile devices per capita
But how can networks adapt to this rate of change?!
This is the data crunch that is driving forward research in wireless communications
Claude Shannon and Capacity • In 1948, Claude Shannon wrote
down “Newtons laws of the
information age” in his classic
paper: “A Mathematical Theory
of Communication”
• This paper allows us to compute
the maximum possible bit rate, in
bits/sec, of any point-to-point
communication channel
• For the Gaussian noise channel:
bits/sec )1log( SNRWC
Claude Shannon and Capacity
• Formula above is for the additive white
Gaussian noise (AWGN) channel
• Sometimes you hear people claim to have
“broken the Shannon limit”
• The reality is they have only broken that
particular formula
• Just means channel model is NOT the
AWGN channel
Shannon gave the method to get the capacity of any point to
point channel
bits/sec )1log( SNRWC
Spectral Efficiency
• Since bandwidths can vary, its more useful to talk of
spectral efficiency, in bits/sec/Hz
• Let the received power be P mw and the Gaussian noise power
spectral density be s2 mw/Hz; so the
• Then the spectral efficiency limit is
2s
PSNR
zbits/sec/H 1log2
12
s
PC
• Later in the talk we’ll introduce an important extension:
area spectral efficiency, in bits/sec/Hz/m2
Claude Shannon and Capacity • Shannon didn’t just tells us a
capacity result
• He also indicated how to get
there: eg. use random Gaussian
codebooks in the AWGN channel.
• These ideas pointed the way to
Turbo codes and low density
parity check codes
• He also showed how to analyze
error probabilities
Random Code
• And design communication systems: eg. the source–channel
separation theorem tells us to design a layered system
x1
y ×
Beyond Shannon: Multiple Users • Shannon focused on the point-to-point
channel
• A cell in a mobile radio network has
multiple users
• The uplink (mobile to base station link) is
called a multiple access channel (MAC)
• Researchers that followed Shannon
(Ahlswede and Liao ’72) found the
capacity region of a MAC
• The downlink is called a broadcast channel
The Multiple Access Channel (MAC) • Lets look at the simplest two user
MAC: AWGN at the base station
• There is interference between the
two links which is reflected in a
tradeoff:
If R1 is the bit rate of user 1 and
R2 is the bit rate of user 2 then
there is a tradeoff between these
two rates
• There is a capacity region that
describes the set of achievable
rates
R1
R2
2-user Rate Region of AWGN MAC
R1
R2
x
x = interference as noise
= successive decoding
= FDMA curve
• The dominant line gives the best
pairs of rates – the optimal tradeoff
• FDMA curve touches the dominant
line at one point
• Treating interference as noise is
suboptimal
• Successive decoding is optimal
What About the Real World?
• In the real world we have cells
• Mobiles in one cell will interfere at another cell’s base station
2-user AWGN Interference Channel
• The rate region for this channel is
unknown
• An open problem for over 40 years!
• Recent work has characterized the rate region to within 1
bit/sec/Hz (Etkin, Tse, and Wang ‘08). Thus:
Rate region is known quite precisely when the noise is low
MACs in the Interference Channel
• User 1 and user 2 are heard at Rx1: MAC1
• User 1 and user 2 are heard at Rx2: MAC2
• The intersection of both rate regions is achievable
Message 1 Rx1 Message 1 Tx1
Message 2 Tx2 Message 2 Rx2
MACs in the Interference Channel
×
MAC2
MAC1
R1
R2
× = treat interference as noise
MACs in the Interference Channel
R1
R2 MAC2
MAC1
×
× = treat interference as noise
Han Kobayashi Scheme • Each user splits its data into two parts: private and common
• The private message is only decoded by the desired receiver (Rx1)
• The common message is decoded by both and cancelled
• The private data rate is too high to be decoded at Rx2 and is treated as Gaussian noise
Treat private message as noise at Rx2
Private message Common message Tx1 Rx1
Tx2 Rx2
Han Kobayashi Scheme
• Etkin, Tse and Wang ‘08 show this scheme can be optimized to
be within 1 bit/sec/Hz of capacity
• Still uses Gaussian random codes
• Will not work once we go to three or more users!
• Beyond Shannon: random codes are no longer any good
• New research shows we must look for structured codes
• A new concept called “interference alignment”
• Suppose users 1 and 2 use a random Gaussian codebook:
Gaussian Han-Kobayashi Not Optimal
Random Code
Sum of Two Random Codebooks Lattice Code for Users 1 and 2
User 0 Code Interference from users 1 and 2 fills the space: no
room for user 0.
Lattice codes can achieve constant gap
Gaussian Models
• It remains that the Gaussian model is a robust model
• It can be shown that the following formula gives achievable
rates for interference channels:
zbits/sec/H energy noise energy ceinterferen
energy signal1log
2
1
C
• Even this simple model is highly complex!
• Energy allocation can be optimized across bandwidth and time
• These optimization problems have been shown to be
mathematically intractable for large numbers of links
Symmetric Network Problem
N links:
• All links gains have the same value
• All cross-link gains have the same (other) value
e
e
1
1
N=2
Solution to Symmetric Network Problem
We might expect optimal solution to be:
• FDMA between the links when the cross-gain (e) is high
• Wideband (WB) frequency sharing when e is low
f 2
W
2
W
FDMA
one band for each link f 2
W
2
W
One band shared by all links
WB
Solution to Symmetric Network Problem
When the cross-gain (e) is low:
• FDMA between the links when the SNR is high
• Wideband (WB) frequency sharing when the SNR is low
• A mixture of these when the SNR is medium
f 2
W
2
W
FDMA
one band for each link f 2
W
2
W
One band shared by all links
WB
TV White Spaces and Unused Spectrum
• Is the spectrum really that congested?
• What about white spaces?
– eg TV bands that are not being used?
• Licensed (primary) users may not be active in some areas
• Room for secondary, unlicensed users?
• New spectrum opening up, and spectrum auctions
Fragmented Spectrum
Cognitive Radio
• Smart, agile radios that can sense and occupy un-utilized
spectrum
• Require flexible hardware, tuneable frequencies
• Overlay: search out unused bands – the FDM approach
• Underlay: UWB radios taking the WB approach
• Successive decoding can be used to strip off strong
interference
• Interference alignment strategies can reduce the spectral
footprint
Multiple Input, Multiple Output (MIMO)
• Multiple antennas can be used at the base
station
• Multiple users provide multiple antennas
• Similar in a lot of ways to a point to
point MIMO channel
• Beamforming:
– N antennas can create up to N non-
interfering beams
– Main challenge is channel
measurement
Coordinated Beamforming • If base stations share channel state information (CSI) over the
backhaul network coordinated beamforming
Cell 1 Cell 2
CSI
Distributed Antennas
• Throw away cells altogether (new architecture!)
• Backhaul transports both CSI and user data
• Now a giant broadcast channel with distributed antennas!
• Base stations must cooperate
CSI data
Small Cells • Spectral efficiency is really
measured in bits/sec/Hz/m2
• If we can decrease the cell sizes
then we increase spectral efficiency
by increasing frequency re-use
• Macrocells, microcells, picocells
• Tiny picocells can be used in
network hotspots
Femtocells • Femtocells are tiny cells formed
with cheap, off-the-shelf base
stations
• Femto base station are like WiFi
access point but use cellular
frequencies – plug and play
• They offload cellular traffic
onto owner’s broadband ISP
connection
• Networks are heterogeneous – a mix of short and long links,
mixture of planned and unplanned layouts
Multi-tier Heterogeneous Networks
Many research challenges:
• Closed versus open access
• Modelling and design (mixture of planned and unplanned)
• Interference avoidance (long versus short links)
• Power control
• Cell association
• Base station coordination
• Handovers (cell to cell and tier to tier)
Conclusions
• Exciting time to be in wireless research
• Great challenge is to drastically increase bits/sec/Hz/m2 to
match forecast demand
• Interference and congestion are the major challenges!
• Huge gap between theory and practice
– in MIMO, interference alignment, coordinating base stations
• New architectures and new algorithms
• Small cells, heterogeneous networks and cognitive radios offer
a prospect to meet the data crunch challenge!
Thankyou!
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