signal processing in communications issues and trends vincent poor ([email protected]) uc, irvine...
TRANSCRIPT
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SIGNAL PROCESSING IN SIGNAL PROCESSING IN COMMUNICATIONSCOMMUNICATIONS
Issues and TrendsIssues and Trends
Vincent PoorVincent Poor([email protected])([email protected])
UC, IrvineUC, IrvineFebruary 18, 2004February 18, 2004
Signal Processing in Communications
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Communications in the 21st CenturyCommunications in the 21st Century
• High-Level Trends – Dramatic growth rates in capacity demands:
• wireless
• broadband
– Increase in shared (multiple-access, interference) channels:• cellular (IS-95, 3G)
• WiFi/Bluetooth/UWB (unlicensed spectrum)
• DSL (cross-talkers in twisted-pair bundles)
• Basic Resources– Bandwidth - tightly constrained – Power - tightly constrained – Diversity - exists naturally, and can be created – Intelligence - growing exponentially
Signal Processing in Communications
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Signal Processing in CommunicationsSignal Processing in Communications
• Principal Roles – Source compression
– Mitigation of physical layer impairments • fading, dispersion, interference
– Exploitation of physical layer diversity • spatial, temporal, spectral
• Catchwords– Turbo - near-optimal, low-complexity iterative processing
– MIMO - multiple antennas at transmitter and receiver
– Cross-Layer - design across the PHY/MAC boundary
– Quantum - exploitation of quantum effects
Signal Processing in Communications
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The Rest of This TalkThe Rest of This Talk
• Recent Results in ... – Turbo
– MIMO
– Cross-Layer
– Quantum
• Context: Multiuser Detection (MUD) – Unifies receiver processing in shared access systems
Signal Processing in Communications
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MUDMUD
Signal Processing in Communications
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Multiple-Access Channel (MAC)Multiple-Access Channel (MAC)
Modulator Channel010…
110…
SignalProcessing
110…Modulator
SignalProcessing 010…
......
......
MUD
Signal processing (MUD) is used to separate the users.Signal processing (MUD) is used to separate the users.
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Multiuser Detection (MUD)Multiuser Detection (MUD)
......
Matched Filter,User 1
Matched Filter,User 2
DecisionLogic
MAC
110…
010…
MUD
OptimalOptimal, , linearlinear, , iterativeiterative & & adaptiveadaptive versions (more in a minute) versions (more in a minute)
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MUD - Linear ModelMUD - Linear ModelK users, each transmitting B symbols, yields a linear model for inputs to the decision logic:
yy = = H bH b + + NN(0, (0, 22HH))
• y = KB-long sufficient statistic vector
• b = KB-long vector of symbols
• H = KBKB matrix of cross-correlations
The purpose of the decision logic is to fit this model.
MUD
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MUD - VarietiesMUD - Varieties
yy = = H bH b + + NN(0, (0, 22HH))
• Optimal [Complexity O(2K); = delay spread] – ML: argmax l(y|b) – MAP: argmax p(bk,i|y)
• Linear [Complexity O((KB)3)]
– Decorrelator: sgn{H-1 y}
– MMSE: sgn{(H+ 22II)-1 y}
• Iterative [Complexity O(Knmax), etc.]
– Linear IC: Gauss-Siedel, Jacobi, conjugate-gradient– Nonlinear IC: the above with intermediate hard decisions– EM: symbols are stochastic– Turbo: symbols are constrained via channel coding, etc.
• Adaptive [Sampling, followed by LMS, RLS, subspace, etc.]
[Dai & Poor, T-SP’02]
MUD
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TURBOTURBO
Signal Processing in Communications
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Turbo ProcessingTurbo Processing
Inference Engine 2
(APP Calculator)
Inference Engine 1
(APP* Calculator)
{Prior, Observations, Constraints}1 {Prior, Observations, Constraints}2
{APP}1 {Updated Prior}2
{Updated Prior}1 {APP}2
Iterate until the APPs stabilize. Complexity Complexity (IE1)+ Complexity (IE2)
*APP = a posteriori probability Turbo
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Convolutional Encoders
Interleavers MAC
Data for K Users Channel Input Channel Output
SISOMUD
K SISO Decoders
De-InterleaversInterleavers
Channel Output
Output Decision Soft-input/soft-output (SISO) Iterative Interleaving removes correlations
{Pdecoder(bk,i y)}
2KΔ +2νvs. 2KΔν
Turbo MUDTurbo MUD
{PMUD(bk, i y)}
Turbo
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Turbo MUD Example [K = 4; Turbo MUD Example [K = 4; = 0.7] = 0.7]
Turbo
Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers
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Application to UWB SystemsApplication to UWB Systems
• K-user (impulse radio) UWB system transmits Nf
short pulses for each information symbol.
• Pulse positions change from pulse-to-pulse with a pseudorandom pattern, unique to each user.
• “Turbo” MUD Algorithm [Fishler & Poor, IEEE-SP]:– Iterate between two detectors, with intermediate
exchanges of soft information:
• “Pulse” detector: performs MUD on a pulse-by-pulse basis, ignoring the fact that multiple pulses contain the same symbol.
• “Symbol” detector: exploits the fact that multiple pulses contain the same symbol (“repetition decoder”).
Turbo
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Nc = 20, Nf = 10, K = 20 (strong interferers, 6dB above user of interest)
Simulation - UWB Turbo DetectorSimulation - UWB Turbo Detector
Turbo
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Turbo
Other ApplicationsOther Applications
• DSL [Dai & Poor, JSAC ’02]
• Powerline Comms. [Dai & Poor, Comm. Mag. ‘03]
• Channel Estimation (MCMC) [X. Wang, et al.]
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MIMOMIMO
BS
MS
MS
MS
MS
Signal Processing in Communications
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SPACE-TIME MUD SPACE-TIME MUD (The SIMO Case)(The SIMO Case)
BSMS
MSMS
MS
MIMO
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Multipath SIMO MA ChannelMultipath SIMO MA Channel
€
r2 (t)
€
rP (t)
......
......
€
r1(t)User 1: 010…
User 2: 110…
User K: 011…
MIMO
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Sufficient Statistic Sufficient Statistic (Space-Time Matched Filter Bank(Space-Time Matched Filter Bank))
As before, linear model with As before, linear model with optimaloptimal, , linearlinear, , iterativeiterative & & adaptiveadaptive versions. versions.
......
TemporalMatchedFilters
{k, l, p}
DecisionLogic
110…
010…
011…
BeamFormers
{k, l}
RAKEs{k}
€
r2 (t)
€
rP (t)
€
r1(t)
...... ...... ...... ......
KKLLPP KKLL KK
K Users; P Receive Antennas; L Paths/User/Antenna
[Wang & Poor, T-SP’99]
MIMO
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MIMO MUDMIMO MUD
MS BS
MSMS
MS
Observation: MIMO MUD is the same as SIMO MUD, except for the decision algorithm - MI adds further constraints on b.
MIMO
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Space-time Coded SystemsSpace-time Coded Systems
• Space-time Coded Systems
– Single-user Channels: • Encoding of symbols across multiple transmit antennas.
• ST block codes [Alamouti, Tarokh, et al.]; ST trellis codes [Tarokh,
et al.]; unitary ST codes [Hochwald, Marzetta, et al.].
– Multiuser Channels [Jayaweera & Poor, EJASP ‘02]: • Separation Th’m: “Full-diversity-achieving single-user ST trellis
codes also achieve full diversity in multiuser channels with joint ML detection & decoding (assumes large SNR, quasi-static Rayleigh fading, etc.).”
• Turbo-style iteration among ST trellis decoding & MUD achieves near-ML performance.
• Blind adaptive linear MUD for ST block coding via subspace tracking & Kalman filtering [Reynolds,Wang & Poor, T-SP’02]
MIMO
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Turbo IC Space-Time MUD Turbo IC Space-Time MUD (MISO Case)(MISO Case)
MIMO
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Performance (4 Performance (4 1; K=4) 1; K=4)
-5 0 5 1010-3
10-2
10-1
100Turbo IC-MMSE-ST-MUD Decoder FER Vs. SNR
Eb/N0
FER
it: 1
it: 2
it: 3
it: 4
16-states trellis space-time code
Turbo IC-MMSE-ST-MUD: rho=0.75, K=4, X Ants.=4Single User Bound
MIMO
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BLAST-Type SystemsBLAST-Type Systems
• BLAST (Bell Labs Layered Space-Time Architecture)
– Basic BLAST [Foschini, et al.]: • Distributes symbols of a single user on multiple tx antennas.
• Uses MUD to separate different streams using spatial signature.
• Capacity (Rayleigh) linear in the min{#tx,#rx}antennae.
• Capacity gain degrades in interference-limited channels.
– Turbo BLAST [Dai, Molisch & Poor, T-WC’04]:
• MUD restores the BLAST capacity gain in interference channels.
MIMO
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CROSS-LAYERCROSS-LAYER
Signal Processing in Communications
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• Use of advanced signal processing at the
nodes of a wireless network has effects at the
MAC layer.
• Examples using MUD include effects on:
– Capacity: users-per-dimension - cellular [Tse & Hanley;
Yao, Poor & Sun; Comaniciu & Poor] & ad hoc [Comaniciu
& Poor] networks
– Utility: bits-per-joule [Meshkati, et al.]
Effects of MUD on Wireless Effects of MUD on Wireless Higher-Layer FunctionalityHigher-Layer Functionality
Cross-Layer
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Wireless Multi-Hop NetworkWireless Multi-Hop Network
• Network DiameterNetwork Diameter = maximum number of hops to reach any node = maximum number of hops to reach any node• ConnectivityConnectivity between nodes depends on PHY (e.g., node-layer processing) between nodes depends on PHY (e.g., node-layer processing)
Cross Layer[Comaniciu & Poor, T-WC’04]
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NN = number of nodes that can be supported in the network = number of nodes that can be supported in the networkDD = network diameter (maximum number of hops to reach any node) = network diameter (maximum number of hops to reach any node)LL = spreading gain (BW fixed) = spreading gain (BW fixed)Target SIR = 5Target SIR = 5
Ad-hoc Network Capacity v.DiameterAd-hoc Network Capacity v.Diameter
Cross Layer
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Competition in Multiple-Competition in Multiple-Access NetworksAccess Networks
• Consider a multiple-access network.
• User terminals are like players in a game,
competing for network resources; each would like to
maximize its own utility.
• The action of each user affects the utility of others.
• Can model this as a non-cooperative game.
Cross-Layer[Meshkati, et al., Allerton’03]
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Game Theoretic FrameworkGame Theoretic Framework
€
uk = utility =throughput
transmit power=
Tk
pk
bits
Joule ⎡ ⎣ ⎢
⎤ ⎦ ⎥
Throughput: Tk = Rk f(k), where f(k) is the frame success
rate, and k is the received SIR of user k.
Game: G = [{1, …, K}, {Ak}, {uk}]
K: total number of users
Ak: set of strategies for user k
uk: utility function for user k
Cross-Layer
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Large-System AnalysisLarge-System Analysis
€
uk
=R
kf (γ * )
γ *σ 2h
kΓ where
Γ MF = 1−α γ * for α <1
γ *
Γ DE = 1− α for α < 1
Γ MMSE = 1− α γ *
1+ γ * for α < 1+
1γ *
with h k
= hkp
2
p=1
P
∑ and α =α
P
• Consider random CDMA with spreading gain N.• As K , N with K/N = ; Nash equilibria:
Two mechanisms:Two mechanisms:• power poolingpower pooling• interference reductioninterference reduction
Cross-Layer
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• Multiuser detectors achieve higher utility and can
accommodate more users compared to matched filter.
• Significant performance improvements are achieved when
multiple antennas are used compared to single antenna case.
Numerical ExampleNumerical Example
Cross-Layer
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QUANTUMQUANTUM
MUDMUD
Signal Processing in Communications
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010…Modulator Channel
PhysicalMeasurement
DecisionLogic 010…
110…Modulator
PhysicalMeasurement
Quantum MACQuantum MAC
• Measurements may not be compatible
• “No cloning” theorem
• “Instrument” and detector must be designed jointly
Quantum
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2-User Example: Error Probabilities2-User Example: Error Probabilities
CounterCounter: ML MUD based on photon counts in each mode.: ML MUD based on photon counts in each mode.OptimalOptimal: Optimal quantum MUD: Optimal quantum MUD
30 photons (average)30 photons (average)per user.per user.
SourceSource: Concha & : Concha & Poor, Poor, IT’04IT’04
Quantum
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ConclusionConclusion
• Signal processing is central to the enablement of higher communications capacity, especially for shared access channels.
• Things to watch: – Turbo – MIMO – Cross-Layer– Quantum
Signal Processing in Communications
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THANK YOU!THANK YOU!
Signal Processing in Communications
Two New Books:
• Wireless Communication Systems: Advanced Techniques for Signal Reception (with X. Wang; Prentice-Hall, 2004)
• Wireless Networks: Multiuser Detection in Cross-Layer Design (with C. Comanciu and N. Mandayam; Kluwer, 2004)