PERSPECTIVES ON THE WIRELESS REVOLUTION: Signal Processing and Education Vince Poor

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

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


THE ROLE OF SIGNAL PROCESSING IN WIRELESS


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 EconomistSept. 18-24, 1999

Traffic Increasingly Consists of Data

Source: http://www.qualcomm.com


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.


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)


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)


Not Growing Exponentially

• Spectrum - 3G auctions!

• Battery power

• Terminal size


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)


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.)


Advances in ASIC TechnologyCourtesy of: Andy Viterbi - ELE391 Lecture (05/5/00)Microns

.8

.5

.35.25

.18

Time 1991 Future199819971995


Fleming Valve (British) 1910 Helical Transformer

1919

Marconi Crystal Receiver 1919 DeForest Tubular Audion

1916

Signal Processing for Wireless (v 1.0)


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)]


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


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).


“Wireless for Poets”? - Not exactly.

ELE391 - MAJORS

ELEECONORFECSWWS/PoliticsOther SEASOther ScienceOther SS/H


Course Organization

• Part I: Wireless Technology

• Part II: Economic, Political & Social Issues

• “Wireless News” - Daily e-letter

• Final Papers


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.)


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


“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.


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.


PERSPECTIVES ON THE WIRELESS REVOLUTION: Signal Processing and Education Vince Poor

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