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Transcript of THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor ([email protected]) Federal...
![Page 1: THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu) Federal Communications Commission May 29, 2001 May 29, 2001 -](https://reader036.fdocuments.in/reader036/viewer/2022062717/56649e2f5503460f94b1fdfd/html5/thumbnails/1.jpg)
THE WIRELESS REVOLUTION:A Signal Processing Perspective
Vince Poor
Federal Communications CommissionMay 29, 2001
May 29, 2001 - 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
• Conclusion
May 29, 2001 - The Wireless Revolution
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THE ROLE OF SIGNAL PROCESSING IN WIRELESS
May 29, 2001 - 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
• Use of a public resource (the radio spectrum)
• Convergence with the Internet
The Role of Signal Processing in Wireless
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Wireless Applications
• Mobile telephony/data/multimedia (3G)
• Nomadic computing
• Wireless LANs
• Bluetooth (piconets)
• Wireless local loop
• Wireless Internet/m-commerce
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
The Role of Signal Processing in Wireless
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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0
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30
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ece
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51
Mobile Subscriptions as a %of all telephone Subscriptions
Source: ITU
Mobile PhonesSubscribers per 100 inhabitants, 1998
The Role of Signal Processing in Wireless
There’s Plenty of Room to Grow - I
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Mobile PhonesMarket Penetration, 2000
The Role of Signal Processing in Wireless
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)
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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 (MorphICs)
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 (phase-shifting masks)
The Role of Signal Processing in Wireless
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Advances in ASIC Technology
Courtesy of: Andy Viterbi
Microns
.8
.5
.35.25
.18
Time 1991 Future199819971995
The Role of Signal Processing in Wireless
5/30/00: 25 nm gate announced with optical lithography using phase-shifting masks (T. Kailath, et al.).
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Fleming Valve 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
• Introduction
• Space-time Multiuser Detection (3G)
• Turbo Multiuser Detection (2.5G)
• Quantum Multiuser Detection (?G)
May 29, 2001 - The Wireless Revolution
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INTRODUCTION
Some Recent Signal Processing Advances
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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 Signal Processing Advances
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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
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SPACE-TIME MUD
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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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
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• 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
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• 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
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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 (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
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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
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Simulation [K = 8; L = 3; P = 3]
Direct-sequencespread-spectrum(16 chips/bit).
Space-Time MUD
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– 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
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TURBO MUD
Some Recent Signal Processing Advances
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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
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• 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
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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
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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
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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
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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
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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
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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
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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
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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
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Simulation Example [K = 4;
Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers
Turbo MUD
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QUANTUM MUD
Some Recent Signal Processing Advances
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• 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
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Quantum MUD Design Problem
Quantum MUD
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A Two-User Quantum Channel
Quantum MUD
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Two-User Example: Error Probabilities
Quantum MUD
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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:
May 29, 2001 - The Wireless Revolution
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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
May 29, 2001 - The Wireless Revolution
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THANK YOU!
May 29, 2001 - The Wireless Revolution