the wireless revolution: a signal processing perspective vince poor ([email protected]) federal...

THE WIRELESS REVOLUTION:A Signal Processing Perspective

Vince Poor

([email protected])

Federal Communications CommissionMay 29, 2001

May 29, 2001 - 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

• Conclusion


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

• Use of a public resource (the radio spectrum)

• Convergence with the Internet

The Role of Signal Processing in Wireless

Wireless Applications

• Mobile telephony/data/multimedia (3G)

• Nomadic computing

• Wireless LANs

• Bluetooth (piconets)

• Wireless local loop

• Wireless Internet/m-commerce


Wireless is Rapidly Overtaking Wireline


Source:The EconomistSept. 18-24, 1999

Traffic Increasingly Consists of Data

Source: http://www.qualcomm.com


Demand Growing Exponentially


Source: CTIA

- As of 05/01/01, there were 114,546,113, in U.S., according to www.wow-com.com - Every 2.25 secs., a new subscriber signs up for cellular in U.S.

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51

Mobile Subscriptions as a %of all telephone Subscriptions

Source: ITU

Mobile PhonesSubscribers per 100 inhabitants, 1998


There’s Plenty of Room to Grow - I

Mobile PhonesMarket Penetration, 2000


There’s Plenty of Room to Grow - II

76% 72%67%

58%50%

46%39%

7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Courtesy of: Tom Sugrue (FCC)

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 (MorphICs)

1

10

100

1000

10000

100000

1000000

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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 (phase-shifting masks)


Advances in ASIC Technology

Courtesy of: Andy Viterbi

Microns

.8

.5

.35.25

.18

Time 1991 Future199819971995


5/30/00: 25 nm gate announced with optical lithography using phase-shifting masks (T. Kailath, et al.).

Fleming Valve 1910

Helical Transformer 1919

Marconi Crystal Receiver 1919 DeForest Tubular Audion

1916

Signal Processing for Wireless (v 1.0)


SOME RECENT SIGNAL PROCESSING ADVANCES

• Introduction

• Space-time Multiuser Detection (3G)

• Turbo Multiuser Detection (2.5G)

• Quantum Multiuser Detection (?G)


INTRODUCTION

Some Recent Signal Processing Advances

First, A Few Words About MUD • Multiuser detection (MUD) refers to data detection in

a non-orthogonal multiplex; it’s of interest, e.g., in– CDMA channels – TDMA channels with channel imperfections– DSL with crosstalk

• MUD can potentially increase the capacity (e.g., bits-per-chip) of interference-limited systems significantly

• MUD comes in various flavors – Optimal (max-likelihood, MAP)

– Linear (decorrelator, MMSE)

– Nonlinear interference cancellation


Some Recent Developments • The basic idea of MUD is to exploit (rather than

ignore) cross-correlations among signals to improve data detection. Recent developments in this area:

• Space-Time MUD – Joint exploitation of spatial and temporal structure.

• Turbo MUD – Joint exploitation of temporal structure induced by channel

coding, and the multi-access channel.

• Quantum MUD – Joint exploitation of quantum measurements and the multi-

access channel.Some Recent Signal Processing Advances

SPACE-TIME MUD


User 1

User 2

User K

r1(t)

r2(t)

rP(t)

Multi-{Access, Antenna, Path} Channel

Space-Time MUD

Non-orthogonal signaling, multipath, fading, dispersion, dynamism, etc.

Single-Antenna Reception

)(1 ts)(1 ib)(1 th)(1 tx

)(2 ts)(2 ib)(2 th)(2 tx

)(tsK)(ibK

)(thK)(txK

---

---

+ +

)(tn

)(tr

Space-Time MUD

• Transmitted signal due to the k-th user:

xk(t) = bk(i)sk(t−iT)i=1

B

∑ , .,,1 Kk L=

[bk(i): data symbol; sk(t): signaling waveform]

• Vector channel (impulse response) 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 MA Signal Model

Space-Time MUD

• Log-likelihood function of the received signal r(t):

L({r(t) :−∞<t<∞}b)∝ Ω(b) ≡2Re{bTy}−bTHb

yk(i) = gkl* akl

H r(t)sk(t−iT−τkl)dt−∞

∞

∫l=1

L

∑

• H is a matrix of cross-correlations among the received

signals

• Sufficient statistic {yk(i)}: space-time matched filter output

A Sufficient Statistic: Space-Time Matched Filter Bank

[kl: path delay; gkl: path gain; akl: array response]

Space-Time MUD

Maximum LikelihoodSequence Detection

OR

Iterative InterferenceCancellation

Space-Time Multiuser Receiver

Space-Time MUD

• Maximum likelihood sequence detection maximizes (over b):

Ω(b) =2R{bTy}−bTHb

H ≡

H [0] H[1] L H[Δ]

H[−1] H[0] H [1] L H[Δ]

H [−Δ] L H[0] L H[Δ]

H[−Δ] L H[−1] H[0] H[1]

H[−Δ] L H[−1] H[0]

⎡

⎣

⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢

⎤

⎦

⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥

[: multipath delay spread]• Computational complexity: O(2K)

Optimal Space-Time MUD

Space-Time MUD

y=Hb+σv

[ Decorrelator: sgn(Re {H-1y}); MMSE: sgn(Re {(H+2I)-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 mmax)

Solve

Space-Time MUD

Simulation [K = 8; L = 3; P = 3]

Direct-sequencespread-spectrum(16 chips/bit).

Space-Time MUD

– Decision Feedback:

Cholesky Decomposition: C =FHF

ˆ b =sgn(F−Hy−(F−diag(F)ˆ 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

– Turbo MUD: - Coded channels (b has constraints).

y=Hb+σv

Space-Time MUD

TURBO MUD


MUD & The Decoding of Error-Control Codes

• Recall: the basic idea of MUD is to exploit cross-correlations among signals to improve data detection.

• Similarly, error-control coding exploits dependencies among channel symbols to improve data detection.

• Turbo MUD is a technique for jointly exploiting these two types of dependencies.

Turbo MUD

• The convolutional code & the multiaccess channel form

a concatenated code.

• Like other concatenated codes, this code can be

efficiently decoded via a turbo-style receiver.

Coded Multiple-Access Channel

Convolutional Encoders

InterleaversMultiaccess

Channel

Information Bits Channel Input Channel Output

Basic Idea of Turbo MUD:

Turbo MUD

r(t) = bk,i(dk)pk(t−iT) +σ n(t)i=1

B

∑k=1

K

∑

Rate-R-Coded Multiaccess Signal Model

Received Signal:

• K = # active users.

• B = # channel symbols per frame

• dk = set of RB data symbols transmitted by user k

• bk(dk) = vector of channel symbols obtained by encoding dk

• pk = rec’d waveform of user k ; 1/T = per-user signaling rate.

• {n(t)} = unit AWGN; = noise intensity

Turbo MUD

As before, the vector y of matched-filter outputs:

is sufficient for inferring b1(d1) b2(d2) ... bK(dK) and d1 d2 ... dK.

Sufficient Statistic

yk(i) = r(t)pk(t−iT)dt−∞

∞

∫ , k=1,...K, i =1,...,B

y=Hb+N(0,σ 2H)

(Hn,m= pk(t−iT)pl(t−jT)dt)−∞

∞

∫

Turbo MUD

max[2 ′ y b− ′ b Hb]

Optimal MUD/Decoding

ML Detection (b)/Decoding (d):

MAP Detection/Decoding: maxP(symbolvalue|y)

O(2) - convolutionally encoded symbols, constraint length orthogonal signaling [BCJR, Viterbi algo, etc.]

O(2K) - uncoded symbols, delay spread [MLSD; MAP MUD]

Complexity per Symbol (Assume Binary Symbols):

(Hn,m=0, ∀ |n−m|>KΔ)

(Hn,m=0, ∀ n≠m)

Turbo MUD

Turbo MUD: The Main Idea

• For constraint-length-convolutionally coded transmission on an asynchronous K-user multiaccess channel, optimal detection/decoding has complexity O(2K) [Giallorenzi & Wilson].

• This complexity can be reduced to O(2K) + O(2) via the turbo principle [Moher].

• I.e., iterate between MUD and channel decoding, exchanging soft (channel) symbol information at each iteration.

Turbo MUD

Convolutional Encoders

InterleaversMultiaccess

Channel

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(bk,i y)}

22 +K vs. K2

Multiaccess Channel & Turbo Receiver

{PMUD(bk, i y)}

Turbo MUD

SISO MUD

• To get posterior probabilities from the multiuser detector, we should use MAP MUD.

• MAP MUD is prohibitively complex O(2K) [K = # users]

• This differs from standard turbo decoding, in which the constituent decoders are of similar complexity.

• Many lower complexity approaches: [Alexander et al.; Honig et al., Lu & Wang, Müller & Huber, Naguib & Sheshadri, Reed et al., Schlegel, Tarköy, Wang & Chen, Wang & Poor (COM’99), & others]

Turbo MUD

y=Hb+N(0,σ 2H)

Recall: Low Complexity MUD

Recall the Model:

• MUD fits this model to the observations.

• As noted before, in addition to ML/MAP, there are many low-complexity techniques for doing this; e.g.,– Linear MUD: decorrelator, MMSE, bootstrap (v. efficient

iterative implementation as linear interference cancellers (IC’s))

– Nonlinear IC’s: successive cancellation, multistage, EM/SAGE

• Generally, these don’t allow the computation of the posterior probabilities needed for turbo MUD.

Turbo MUD

Low Complexity SISO MUD

• Conventional MMSE MUD:

• MMSE output desired symbol + Gaussian error

[Poor & Verdú, IT’97]

• From this, posterior probabilities can be estimated

from the MMSE detector output.

• This yields an effective low-complexity SISO MUD.

• MMSE w/ Priors:

≅

ˆ b =sgn{(H+σ 2I)−1y}

(H+σ 2C-1)−1[y−H˜ b ]

Turbo MUD

Simulation Example [K = 4;

Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers

Turbo MUD

QUANTUM MUD


• A basic element of MUD is the matched-filter-bank sufficient statistic.

• With quantum measurements, observation 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 Design Problem

Quantum MUD

A Two-User Quantum Channel

Quantum MUD

Two-User Example: Error Probabilities

Quantum MUD

Conclusion

• The transformation from wireless voice to wireless data is causing exponentially increasing demand for wireless capacity.

• Signal processing is the great enabler: – Source compression– Fading countermeasures/transmitter diversity– Interference suppression/space-time processing – Micro-lithography

• Recent advances:


Conclusion - Cont’d• MUD exploits signal cross-correlations to substantially improve

data detection.• Space-time MUD

– Combines exploitation of temporal & spatial cross-correlations.• Turbo MUD

– Combines exploitation of cross-correlations introduced by the channel with exploitation of dependence introduced by coding.

• Quantum MUD – Combines exploitation of cross-correlations with the instrument

design for the quantum channels.• Some Open Issues

– Space-time MUD: Hardware implementation– Turbo MUD: Adaptivity, convergence behavior– Quantum MUD: Relevance in applications


THANK YOU!


the wireless revolution: a signal processing perspective vince poor ([email protected]) federal...

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