PERSPECTIVES ON THE WIRELESS REVOLUTION: Signal Processing and Education Vince Poor
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PERSPECTIVES ON THE WIRELESS REVOLUTION:
Signal Processing and Education
Vince Poor([email protected])
Stanford Wireless Seminar: May 16, 2000
May 16, 2000 - Perspectives on the Wireless Revolution
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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
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THE ROLE OF SIGNAL PROCESSING IN WIRELESS
May 16, 2000 - Perspectives on the Wireless Revolution
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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
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Wireless is Rapidly Overtaking Wireline
The Role of Signal Processing in Wireless
Source:The EconomistSept. 18-24, 1999
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Traffic Increasingly Consists of Data
Source: http://www.qualcomm.com
The Role of Signal Processing in Wireless
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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
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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
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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
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Not Growing Exponentially
• Spectrum - 3G auctions!
• Battery power
• Terminal size
The Role of Signal Processing in Wireless
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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
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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
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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
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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
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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
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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
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User 1
User 2
User K
r1(t)
r2(t)
rP(t)
Multi-{Access, Antenna, Path} Channel
Space-Time MUD
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Asynchrony, multipath, fading, dispersion, dynamism, etc.
Single-Antenna Reception
Space-Time MUD
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• 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
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• 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
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Maximum LikelihoodSequence Detection
OR
Iterative InterferenceCancellation
Space-Time Multiuser Receiver
Space-Time MUD
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• 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
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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
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Space-Time MUD
Simulation [K = 8; N = 16; L = 3; P = 3]
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– 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
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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)}
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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
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Simulation Example [K = 4;
Turbo MUD
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• 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
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Quantum MUD
A Two-User Quantum Channel
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Quantum MUD Design Problem
Quantum MUD
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Error Probabilities
Quantum MUD
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THE WIRELESS REVOLUTION @ PRINCETON
May 16, 2000 - Perspectives on the Wireless Revolution
http://courseinfo.princeton.edu/courses/ELE391_S2000
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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
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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
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“Wireless for Poets”? - Not exactly.
ELE391 - MAJORS
ELEECONORFECSWWS/PoliticsOther SEASOther ScienceOther SS/H
The Wireless Revolution @ Princeton
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Course Organization
• Part I: Wireless Technology
• Part II: Economic, Political & Social Issues
• “Wireless News” - Daily e-letter
• Final Papers
The Wireless Revolution @ Princeton
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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
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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
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“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
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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