Stanford Wireless Seminar:
May 16, 2000
PERSPECTIVES ON THE
WIRELESS REVOLUTION:
Signal Processing and Education
Vince Poor
(poor@princeton.edu)
May 16, 2000 - Perspectives on the Wireless Revolution
OUTLINE
• The Role of Signal Processing in Wireless
• Some Recent Signal Processing Advances
– Space-time Multiuser Detection
– Turbo Multiuser Detection
– Quantum Multiuser Detection
• The Wireless Revolution @ Princeton
May 16, 2000 - Perspectives on the Wireless Revolution
THE ROLE OF SIGNAL
PROCESSING IN WIRELESS
May 16, 2000 - Perspectives on the Wireless Revolution
Motivating Factors
• Telecommunications is a $1012/yr. business
• c. 2005: wireless > wireline
• > 109 subscribers worldwide
• Explosive growth in wireless services (3G,
WLL’s, WLAN’s, Bluetooth, etc.)
• Rapid convergence with the Internet
The Role of Signal Processing in Wireless
Wireless is Rapidly Overtaking Wireline
Source:
The Economist
Sept. 18-24, 1999
The Role of Signal Processing in Wireless
Traffic Increasingly Consists of Data
Source: http://www.qualcomm.com
The Role of Signal Processing in Wireless
Demand Growing Exponentially
Source: CTIA
- There are now 92,182,894 in U.S., according to www.wow-com.com
- Every 2.25 secs., a new subscriber signs up for cellular in U.S.
The Role of Signal Processing in Wireless
Wireless Challenges
• High data rate (multimedia traffic)
• Networking (seamless connectivity)
• Resource allocation (quality of service - QoS)
• Manifold physical impairments
• Mobility (rapidly changing physical channel)
• Portability (battery life)
• Privacy/security (encryption)
The Role of Signal Processing in Wireless
Wireless Channels
• Fading: Wireless channels behave like linear systems
whose gain depends on time, frequency and space.
• Limited Bandwidth (data rate, dispersion)
• Dynamism (random access, mobility)
• Limited Power (on at least one end)
• Interference (multiple-access, co-channel)
The Role of Signal Processing in Wireless
Not Growing Exponentially
• Spectrum - 3G auctions!
• Battery power
• Terminal size
The Role of Signal Processing in Wireless
Moore’s and “Eveready”’s Laws
10000000
1000000
Signal Processor
Performance
(~Moore’s Law)
100000
10000
1000
100
Battery Capacity
(i.e. Eveready’s Law)
10
Courtesy of: Ravi Subramanian - ELE391 Lecture (03/24/00)
The Role of Signal Processing in Wireless
20
20
16
20
12
20
08
20
04
20
00
20
96
19
92
19
88
19
84
19
19
80
1
Signal Processing to the Rescue
• Source Compression
• Transmitter Diversity (Fading Countermeasures):
– Spread-spectrum: CDMA, OFDM (frequency selectivity)
– Temporal error-control coding (time selectivity)
– Space-time coding (angle selectivity)
• Advanced Receiver Techniques:
– Interference suppression (multiuser detection - MUD)
– Multipath combining & space-time processing
– Equalization
– Channel estimation
• Improved Micro-lithography (T. Kailath, et al.)
The Role of Signal Processing in Wireless
Advances in ASIC Technology
Microns
Courtesy of: Andy Viterbi - ELE391 Lecture (05/5/00)
.8
.5
.35
.25
.18
Time
1991
1995
1997
1998
The Role of Signal Processing in Wireless
Future
Signal Processing for Wireless (v 1.0)
Fleming Valve
(British)
1910
Marconi Crystal Receiver
1919
Helical Transformer
1919
DeForest Tubular Audion
1916
The Role of Signal Processing in Wireless
SOME RECENT SIGNAL
PROCESSING ADVANCES
• Space-time MUD (3G) [Wang & Poor (SP’99), Dai
& Poor (ISSSTA2000), et al.]
• Turbo MUD (2+G) [Wang & Poor (COM’99), et al.]
• Quantum MUD (?G) [Concha & Poor (ISIT2000)]
May 16, 2000 - Perspectives on the Wireless Revolution
First, A Few Words About MUD
[Also recall SV’s May 11 talk.]
• Multiuser detection (MUD) refers to data detection in
non-orthogonal multiplexes
• MUD can potentially increase the capacity (e.g., bitsper-chip) of interference-limited systems significantly
• MUD comes in various flavors
– Optimal (max-likelihood, min-probability-of-error)
– Linear (matched filter, decorrelator, MMSE)
– Nonlinear interference cancellation
Some Recent Signal Processing Advances
Multi-{Access, Antenna, Path} Channel
r1(t)
User 1
r2 (t)
User 2
rP (t)
User K
Space-Time MUD
Single-Antenna Reception
Asynchrony, multipath, fading, dispersion, dynamism, etc.
Space-Time MUD
Space-Time MA Signal Model
• Transmitted signal due to the k-th user:
M 1
xk (t )  Ak  bk (i ) sk (t  iT ),
i 0
k  1,, K.
[bk(i): data symbol; sk(t): spreading waveform]
• Vector channel of the k-th user:
L
h k (t )   a kl gkl (t   kl ).
l 1
[kl: path delay;
gkl: path gain;
akl: array response]
• Received signal:
K
r (t )   xk (t )  h k (t )   n(t ).
k 1
Space-Time MUD
Sufficient Statistic
• Composite data signal
M 1 K
L
S (t; b)    Ak bk (i )  a kl gkl sk (t  iT   kl ).
i 0 k 1
l 1
• Log-likelihood function of received signal r(t)

L({r(t) :   t  }b)  (b)  2R{ (b)}  S(t;b) dt,
2
 (b)   S (t; b) H r (t )dt
M 1 K
k (i )
y

L
H 
   Ak bk (i )  g kl* a kl  r (t ) sk (t  iT   kl )dt.



i 0 k 1
l 1
z kl ( i )
• Sufficient statistic {yk(i)}: space-time matched filter output.
Space-Time MUD
Space-Time Multiuser Receiver
Maximum Likelihood
Sequence Detection
OR
Iterative Interference
Cancellation
Space-Time MUD
Optimal Space-Time MUD
• Maximum likelihood sequence detection maximizes:
(b)  2R{bT Ay}  bT AHAb,
 H [0]

 H [ 1]

H




H
[1]
H
[0]
H
[]

H
[1]

[]
H
H
[]

[0]
H

H
[]
H
[]

[ 1]
H

• Computational complexity: O(2(+1)K)
Space-Time MUD
[]
H
[0]
H
H
[ 1]





[1] 
H 
[0]
H 
[: multipath delay spread]
Linear S-T Interference Cancellers
y  HAb  v
[ Decorrelator: sgn(R {H-1y}); MMSE: sgn(R {(H+2A-2)-1y}) ]
Problem:
Solve
Cx  y
– Gauss-Seidel Iteration: (Serial IC)
– Jacobi Iteration: (Parallel IC)
with
C  CL  D  CU
x m  D  CL  CU xm 1  D  CL  y
1
x m  D1(C L  CU )x m1  D1y
• Computational complexity: O(K  mmax)
Space-Time MUD
1
Simulation
[K = 8; N = 16; L = 3; P = 3]
Space-Time MUD
Nonlinear S-T Interference Cancellers
y  HAb  v
– Decision Feedback:
ˆ  sgn( F H y  (F diag( F)Ab
ˆ ))
b
Cholesky Decomposition:
C  FHF
– Successive Cancellation:
bm  sgn y (C L  CU )bm1   sgn y  (H  D)bm 1 
– EM/SAGE-Based IC: (Interfering symbols are “hidden” data)
– Turbo MUD: - Coded channels (b has constraints).
Space-Time MUD
Turbo CDMA Channel and Receiver
Channel Input
Information Bits
Convolutional
Encoders
Channel Output
Channel Output
CDMA
Channel
Interleaver
SISO
MUD
{PMUD (b j y)}
{Pdecoder (bj y)}
Int.
Output Decision
2 K  2
vs.
De-Int.
SISO
Decoders
2 K
Turbo MUD
 Soft-input/soft-output (SISO)
 Iterative
 Interleaving removes correlations
SISO MUD
• To get posterior probabilities, we should use MAP
detection.
• MAP MUD is prohibitively complex O(2K)
[K = # users]
• Other MUD’s (e.g., MMSE) don’t give posteriors.
• But, the MMSE detector output is approx. equal to the
desired symbol + Gaussian error. [Poor & Verdu IT’97]
• From this, posterior probabilities can be estimated
from the MMSE detector output.
Turbo MUD
Simulation Example [K = 4; r  0.7]
Turbo MUD
Quantum MUD
• A basic element of MUD is the (space-time)
matched-filter-bank sufficient statistic.
• With quantum measurements, the type of
measurements is restricted (uncertainty principles
apply)
• In this case, the observation instrument must be
designed jointly with the detector.
• Photon counting is usually not optimal.
Quantum MUD
A Two-User Quantum Channel
Quantum MUD
Quantum MUD Design Problem
Quantum MUD
Error Probabilities
Quantum MUD
THE WIRELESS REVOLUTION
@ PRINCETON
http://courseinfo.princeton.edu/courses/ELE391_S2000
May 16, 2000 - Perspectives on the Wireless Revolution
ELE391: The Wireless Revolution
(Telecommunications for the 21st Century)
• What: A new course (Spring2000) for majors and
non-majors (approximately 120 undergraduates).
• Motivation: Significant student curiosity about the
current wireless boom, both within/without EE.
• Prerequisite: Freshman calculus.
The Wireless Revolution @ Princeton
Objectives: Things to Learn
• Wireless technology (digital transmission,
access techniques, networking, applications).
• Economic/business aspects of wireless.
• Social dimensions of wireless.
• Politics of wireless (regulation, standards).
The Wireless Revolution @ Princeton
“Wireless for Poets”? - Not exactly.
ELE391 - MAJORS
ELE
ECON
ORFE
CS
WWS/Politics
Other SEAS
Other Science
Other SS/H
The Wireless Revolution @ Princeton
Course Organization
• Part I: Wireless Technology
• Part II: Economic, Political & Social Issues
• “Wireless News” - Daily e-letter
• Final Papers
The Wireless Revolution @ Princeton
Part I: Wireless Technology
•
•
•
•
•
•
•
•
•
Organization of telecommunications networks
Multimedia transmission (mod/demod, A/D, compression, etc.)
Radio network management (access methods, protocols)
Physical limitations on wireless networks
The radio spectrum (physical characteristics, allocation)
History and evolution of wireless technology
Profile of current wireless services (cellular, WLL, WLAN, etc.)
Cellular telephony (current & emerging systems)
Other emerging technologies (m-Internet, Bluetooth, PDA’s,
etc.)
The Wireless Revolution @ Princeton
Part II: Economic, Political & Social Issues
• The main businesses involved in wireless (OEM’s, service providers, etc.)
• Ed Zschau (Harvard Business School): the wireless market space
• Ravi Subramanian (MorphICs): deconstruction of the wireless industry
• Ed Felten (CS): security and privacy
• Chris Fine (Goldman-Sachs): a Wall Street perspective (M&A, etc.)
• Wayne Wolf (EE): comparison of Marconi and Internet eras
• Eszter Hargittai (Sociology): technology diffusion
• Dale Hatfield (FCC): spectrum management
• Ruby Lee (EE): multimedia information appliances
• Mike Feher (wireless antiquary): demo of antique wireless apparatus
• Andy Viterbi (Viterbi Fund): how new technology created the wireless mania
The Wireless Revolution @ Princeton
“Wireless News” (Greatest Hits)
• Mergers: Vodafone/Mannesmann ($185B); Bell Atlantic/
Airtouch/GTE
(Verizon);
SBC/Bellsouth;
Pacific
Century/Cable & Wireless HKT; Royal KPN/DoCoMo
• IPO’s: Palm, AT&T Wireless ($10B), etc.
• European 3G Auctions: UK - £22 billion/150 rounds
• Iridium: Crispy satellites.
• New Devices: Nokia, NEC, Palm, Pocket PC, etc.
• m-Everything: stocks, banking, food, golf, bingo, etc.
The Wireless Revolution @ Princeton
Things to Remember
• Wireless is one of the most exciting technologies of our time.
• It’s enormous, global, and growing very rapidly.
• The opportunities for innovation and impact - technical,
economic, social, political - are limitless.
• We are living in a period like the Marconi era, with
convergence of wireless and the Internet likely to make major
changes in society.
• Signal processing is the great enabler.
May 16, 2000 - Perspectives on the Wireless Revolution