signal processing in communications issues and trends vincent poor ([email protected]) uc, irvine...

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

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 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


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


MUDMUD


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.

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)

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

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

TURBOTURBO


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

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

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

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

Nc = 20, Nf = 10, K = 20 (strong interferers, 6dB above user of interest)

Simulation - UWB Turbo DetectorSimulation - UWB Turbo Detector

Turbo

Turbo

Other ApplicationsOther Applications

• DSL [Dai & Poor, JSAC ’02]

• Powerline Comms. [Dai & Poor, Comm. Mag. ‘03]

• Channel Estimation (MCMC) [X. Wang, et al.]

MIMOMIMO

BS

MS

MS

MS

MS


SPACE-TIME MUD SPACE-TIME MUD (The SIMO Case)(The SIMO Case)

BSMS

MSMS

MS

MIMO

Multipath SIMO MA ChannelMultipath SIMO MA Channel

€

r2 (t)

€

rP (t)

......

......

€

r1(t)User 1: 010…

User 2: 110…

User K: 011…

MIMO

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

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

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

Turbo IC Space-Time MUD Turbo IC Space-Time MUD (MISO Case)(MISO Case)

MIMO

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

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

CROSS-LAYERCROSS-LAYER


• 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

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]

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

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]

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

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

• 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

QUANTUMQUANTUM

MUDMUD


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

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

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


THANK YOU!THANK YOU!


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)

signal processing in communications issues and trends vincent poor ([email protected]) uc, irvine...

Documents

communications slide

mud signal processing

quantum mud signal processing

turbo signal processing

modulator signal processing

conclusion signal processing

minute slide

it04 quantum slide