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PERSPECTIVES ON THE WIRELESS REVOLUTION:
Signal Processing and Education
Vince Poor(poor@princeton.edu)
Stanford Wireless Seminar: May 16, 2000
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
The Role of Signal Processing in Wireless
Source:The EconomistSept. 18-24, 1999
Traffic Increasingly Consists of Data
Source: http://www.qualcomm.com
The Role of Signal Processing in Wireless
Demand Growing Exponentially
- 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
Source: CTIA
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
Courtesy of: Ravi Subramanian - ELE391 Lecture (03/24/00)
1
10
100
1000
10000
100000
1000000
10000000
1980198419881992 1996 20002004 2008201220162020
Battery Capacity(i.e. Eveready’s Law)
Signal Processor Performance (~Moore’s Law)
The Role of Signal Processing in Wireless
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 TechnologyCourtesy of: Andy Viterbi - ELE391 Lecture (05/5/00)Microns
.8
.5
.35.25
.18
Time 1991 Future199819971995
The Role of Signal Processing in Wireless
Fleming Valve (British) 1910 Helical Transformer
1919
Marconi Crystal Receiver 1919 DeForest Tubular Audion
1916
Signal Processing for Wireless (v 1.0)
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., bits-per-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
User 1
User 2
User K
r1(t)
r2(t)
rP(t)
Multi-{Access, Antenna, Path} Channel
Space-Time MUD
Asynchrony, multipath, fading, dispersion, dynamism, etc.
Single-Antenna Reception
Space-Time MUD
• Transmitted signal due to the k-th user:
,)()()(1
0∑ −=−
=
M
ikkkk iTtsibAtx .,,1 Kk L=
[bk(i): data symbol; sk(t): spreading waveform]
• Vector channel of the k-th user:
∑ −==
L
lklklklk tgath
1).()( τδ
[kl: path delay; gkl: path gain; akl: array response]
• Received signal:
∑ +∗==
K
kkk tnthtxtr
1).()()()( σ
Space-Time MUD
Space-Time MA Signal Model
• Composite data signal
∑ ∑ ∑ −−≡−
= = =
1
0 1 1).()();(
M
i
K
k
L
lklkklklkk iTtsgaibAtS τb
• Log-likelihood function of received signal r(t)
L({r(t) :−∞<t<∞}b)∝ Ω(b) ≡2R{ψ (b)}− S(t;b)
2dt,
−∞
∞
∫
.)()()(
)();()(
1
0 1
)(
1)(
*∑ ∑ ∑ ∫ −−=
∫=
−
= = =
∞∞−
∞∞−
M
i
K
k
iy
L
liz
klkHklklkk
H
k
kl
dtiTtstragibA
dttrtS
44444 844444 76
4444 34444 21τ
ψ bb
• Sufficient statistic {yk(i)}: space-time matched filter output.
Sufficient Statistic
Space-Time MUD
Maximum LikelihoodSequence Detection
OR
Iterative InterferenceCancellation
Space-Time Multiuser Receiver
Space-Time MUD
• Maximum likelihood sequence detection maximizes:
Ω(b) =2R{bTAy}−bTAHAb,
⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
≡
−Δ−
−Δ−
ΔΔ−
Δ−
Δ
]0[]1[][
]1[]0[]1[][
][]0[][
][]1[]0[]1[
][]1[]0[
HHH
HHHH
HHH
HHHH
HHH
L
L
LL
L
L
H
[Δ: multipath delay spread]• Computational complexity: O(2( +1)K)
Optimal Space-Time MUD
Space-Time MUD
y=HAb+σv
[ Decorrelator: sgn(R {H-1y}); MMSE: sgn(R {(H+2A-2)-1y}) ]
– Gauss-Seidel Iteration: (Serial IC)
Problem: Cx=y with C =CL +D+CU
– Jacobi Iteration: (Parallel IC) xm =−D−1(C L +CU )xm−1 +D−1y
xm =−D+CL( )−1CUxm−1 + D+CL( )
−1y
Linear S-T Interference Cancellers
• Computational complexity: O(K Δmmax)
Space-Time MUD
Solve
Space-Time MUD
Simulation [K = 8; N = 16; L = 3; P = 3]
– Decision Feedback:
Cholesky Decomposition: C =FHF
ˆ b =sgn(F−Hy−(F−diag(F)Aˆ b ))
– Successive Cancellation:
bm =sgny−(C L +CU )bm−1( )=sgny−(H−D)bm−1( )
– EM/SAGE-Based IC: (Interfering symbols are “hidden” data)
Nonlinear S-T Interference Cancellers
Space-Time MUD
– Turbo MUD: - Coded channels (b has constraints).
y=HAb+σv
Convolutional Encoders
InterleaverCDMAChannel
Information Bits Channel Input Channel Output
SISOMUD
SISO Decoders
De-Int.Int.
Channel Output
Output Decision Soft-input/soft-output (SISO) Iterative Interleaving removes correlations
{Pdecoder(bj y)}
ν22 +K vs. νK2
Turbo MUD
Turbo CDMA Channel and Receiver
{PMUD(bj y)}
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;
Turbo 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
Quantum MUD
Quantum MUD
A Two-User Quantum Channel
Quantum MUD Design Problem
Quantum MUD
Error Probabilities
Quantum MUD
THE WIRELESS REVOLUTION @ PRINCETON
May 16, 2000 - Perspectives on the Wireless Revolution
http://courseinfo.princeton.edu/courses/ELE391_S2000
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
ELEECONORFECSWWS/PoliticsOther SEASOther ScienceOther 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
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