Short Course:Wireless Communications: Lecture 3

Professor Andrea Goldsmith

UCSDMarch 22-23La Jolla, CA

Lecture 2 Summary

Capacity of Flat Fading Channels

Four casesNothing knownFading statistics knownFade value known at receiverFade value known at receiver and

transmitterOptimal Adaptation

Vary rate and power relative to channel

Optimal power adaptation is water-filling

Exceeds AWGN channel capacity at low SNRs

Suboptimal techniques come close to capacity

Frequency Selective Fading Channels

For TI channels, capacity achieved by water-filling in frequency

Capacity of time-varying channel unknown

Approximate by dividing into subbandsEach subband has width Bc (like

MCM).Independent fading in each

subbandCapacity is the sum of subband

capacities

Bc fP

1/|H(f)|2

Linear Modulation in Fading

BER in AWGN:In fading gs and therefore Ps

randomPerformance metrics:

Outage probability: p(Ps>Ptarget)=p(g<gtarget)

Average Ps , Ps:

Combined outage and average Ps

ggg dpPP ss )()(0

sMMs QP g

Variable-Rate Variable-Power MQAM

UncodedData Bits Delay Point

SelectorM(g)-QAM ModulatorPower: S(g)

To Channel

g(t) g(t)

log2 M(g) Bits One of theM(g) Points

BSPK 4-QAM 16-QAM

Goal: Optimize S(g) and M(g) to maximize EM(g)

Optimal Adaptive Scheme

Power Water-Filling

Spectral Efficiency

Practical ConstraintsConstellation and power

restrictionConstellation updates.Estimation error and delay.

SS

K K K( )g g gg gg

1 10

0

0 else

g

1

0g

1gK

gk g

RB

p dK K

log ( ) .2

g

gg

g gEquals Shannon capacity with

an effective power loss of K.

DiversitySend bits over independent

fading pathsCombine paths to mitigate fading

effects.

Independent fading pathsSpace, time, frequency,

polarization diversity.

Combining techniquesSelection combining (SC)Equal gain combining (EGC)Maximal ratio combining (MRC)

Can almost completely eliminate fading effects

Multiple Input Multiple Output (MIMO)Systems

MIMO systems have multiple (r) transmit and receiver antennas

With perfect channel estimates at TX and RX, decomposes into r independent channelsRH-fold capacity increase over SISO

systemDemodulation complexity reductionCan also use antennas for diversity

(beamforming)Leads to capacity versus diversity

tradeoff in MIMO

MCM and OFDMMCM splits channel into flat fading

subchannelsFading across subcarriers degrades

performance. Compensate through coding or adaptation

OFDM efficiently implemented using FFTs

OFDM challenges are PAPR, timing and frequency offset, and fading across subcarriers

xcos(2pf0t)

xcos(2pfNt)

SR bps

R/N bps

R/N bps

QAMModulator

QAMModulator

Serial To

ParallelConverter

Spread Spectrum In DSSS, bit sequence modulated by

chip sequence

Spreads bandwidth by large factor (K)Despread by multiplying by sc(t) again

(sc(t)=1) Mitigates ISI and narrowband

interferenceISI mitigation a function of code

autocorrelationMust synchronize to incoming signalRAKE receiver used to combine

multiple paths

s(t) sc(t)

Tb=KTc Tc

S(f)Sc(f)

1/Tb 1/Tc

S(f)*Sc(f)

2

Course OutlineOverview of Wireless CommunicationsPath Loss, Shadowing, and WB/NB

FadingCapacity of Wireless ChannelsDigital Modulation and its

PerformanceAdaptive ModulationDiversityMIMO SystemsMulticarrier Modulation Spread SpectrumMultiuser Communications Wireless Networks Future Wireless Systems

Lecture 3

Course OutlineOverview of Wireless CommunicationsPath Loss, Shadowing, and WB/NB

FadingCapacity of Wireless ChannelsDigital Modulation and its

PerformanceAdaptive ModulationDiversityMIMO SystemsMulticarrier ModulationSpread SpectrumMultiuser CommunicationsWireless Networks Future Wireless Systems

Multiuser Channels:Uplink and Downlink

Downlink (Broadcast Channel or BC): One Transmitter to Many Receivers.

Uplink (Multiple Access Channel or MAC): Many Transmitters to One Receiver.

R1

R2

R3

x h1(t)x h21(t)

x

h3(t)

x h22(t)

Uplink and Downlink typically duplexed in time or frequency

7C29822.033-Cimini-9/97

Bandwidth Sharing Frequency Division

Time Division

Code DivisionMultiuser Detection

Space (MIMO Systems)Hybrid Schemes

Code Space

Time

Frequency Code Space

Time

Frequency Code Space

Time

Frequency

Multiple Access SS

Interference between users mitigated by code cross correlation

In downlink, signal and interference have same received power

In uplink, “close” users drown out “far” users (near-far problem)

)()2cos(5.5.)()(5.5.

))(2cos()2cos()()()()2(cos)()()(ˆ

12210

212211

1220

222

111

p

ppp

c

T

cc

cccc

T

cc

fdddttstsdd

dttftftstststftststx

b

b

2

1

Multiuser Detection In all CDMA systems and in

TD/FD/CD cellular systems, users interfere with each other.

In most of these systems the interference is treated as noise.Systems become interference-limitedOften uses complex mechanisms to

minimize impact of interference (power control, smart antennas, etc.)

Multiuser detection exploits the fact that the structure of the interference is knownInterference can be detected and

subtracted outBetter have a darn good estimate of the

interference

Ideal Multiuser Detection

Signal 1 Demod

IterativeMultiuserDetection

Signal 2Demod

- =Signal 1

- =

Signal 2

Why Not Ubiquitous Today? Power and A/D Precision

A/D

A/D

A/D

A/DA/D

RANDOM ACCESS TECHNIQUES

7C29822.038-Cimini-9/97

Random AccessDedicated channels wasteful for

datause statistical multiplexing

TechniquesAlohaCarrier sensing

Collision detection or avoidanceReservation protocolsPRMA

Retransmissions used for corrupted data

Poor throughput and delay characteristics under heavy loadingHybrid methods

Multiuser Channel Capacity

Fundamental Limit on Data Rates

Main drivers of channel capacity Bandwidth and received SINR Channel model (fading, ISI) Channel knowledge and how it is used Number of antennas at TX and RX

Duality connects capacity regions of uplink and downlink

Capacity: The set of simultaneously achievable rates {R1,…,Rn}

R1R2

R3

R1

R2

R3

Multiuser Fading Channel Capacity

Ergodic (Shannon) capacity: maximum long-term rates averaged over the fading process.

Shannon capacity applied directly to fading channels.

Delay depends on channel variations. Transmission rate varies with channel quality.

Zero-outage (delay-limited*) capacity: maximum rate that can be maintained in all fading states.

Delay independent of channel variations. Constant transmission rate – much power needed

for deep fading.

Outage capacity: maximum rate that can be maintained in all nonoutage fading states.

Constant transmission rate during nonoutage Outage avoids power penalty in deep fades

Broadcast Channels with ISI

ISI introduces memory into the channel

The optimal coding strategy decomposes the channel into parallel broadcast channelsSuperposition coding is applied to each

subchannel.Power must be optimized across

subchannels and between users in each subchannel.

w1kH1(w)

H2(w)w2k

xk

y h x wk ii

m

kk i1 11

1

y h x wk ii

m

kk i2 21

2

Broadcast MIMO Channel

MIMO MAC capacity easy to find

MIMO BC channel capacity obtained using dirty paper coding and duality with MIMO MAC

111 n x H y 1H

x

1n

222 n x H y 2H

2n

)1 t(r

)2 t(r

Non-degraded broadcast channel

Course OutlineOverview of Wireless CommunicationsPath Loss, Shadowing, and WB/NB

FadingCapacity of Wireless ChannelsDigital Modulation and its

PerformanceAdaptive ModulationDiversityMIMO SystemsMulticarrier ModulationSpread SpectrumMultiuser CommunicationsWireless Networks Future Wireless Systems

Spectral ReuseDue to its scarcity, spectrum

is reused

BS

In licensed bands

Cellular, Wimax Wifi, BT, UWB,…

and unlicensed bands

Reuse introduces interference

BASE STATION

Cellular System Design

Frequencies, timeslots, or codes reused at spatially-separate locations

Efficient system design is interference-limited

Base stations perform centralized control functionsCall setup, handoff, routing, adaptive

schemes, etc.

8C32810.44-Cimini-7/98

Design Issues

Reuse distanceCell sizeChannel assignment strategyInterference management

Multiuser detectionMIMODynamic resource allocation

Interference: Friend or Foe?

If treated as noise: Foe

If decodable: Neither friend nor foe

INPSNR

Increases BER, reduces capacity

Multiuser detection can completely remove interference

MIMO in Cellular

How should MIMO be fully exploited?At a base station or Wifi access point

MIMO Broadcasting and Multiple AccessNetwork MIMO: Form virtual antenna

arraysDownlink is a MIMO BC, uplink is a MIMO

MACCan treat “interference” as a known signal

or noiseCan cluster cells and cooperate between

clusters

MIMO in Cellular:Other Performance

BenefitsAntenna gain extended

battery life, extended range, and higher throughput

Diversity gain improved reliability, more robust operation of services

Multiplexing gain higher data rates

Interference suppression (TXBF) improved quality, reliability, robustness

Reduced interference to other systems

Rethinking “Cells” in Cellular

Traditional cellular design “interference-limited”MIMO/multiuser detection can remove interferenceCooperating BSs form a MIMO array: what is a cell?Relays change cell shape and boundariesDistributed antennas move BS towards cell boundaryFemtocells create a cell within a cell Mobile cooperation via relays, virtual MIMO, network coding.

Femto

Relay

DAS

Coop MIMO

How should cellularsystems be designed?

Will gains in practice bebig or incremental; incapacity or coverage?

Cellular System Capacity

Shannon CapacityShannon capacity does no incorporate reuse distance.Some results for TDMA systems with joint

base station processing

User Capacity Calculates how many users can be supported for a given performance specification.Results highly dependent on traffic, voice

activity, and propagation models.Can be improved through interference

reduction techniques. (Gilhousen et. al.)

Area Spectral EfficiencyCapacity per unit area

In practice, all techniques have roughly the same capacity

Area Spectral Efficiency

BASESTATION

S/I increases with reuse distance. For BER fixed, tradeoff between reuse

distance and link spectral efficiency (bps/Hz).

Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.

A=.25D2p =

ASE vs. Cell Radius

Cell Radius R [Km]

101

100Aver

age

Are

a Sp

ectr

al

Effic

ienc

y[B

ps/H

z/K

m2 ]

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

D=4RD=6R

D=8R

fc=2 GHz

Improving Capacity Interference averaging

WCDMA

Interference cancellationMultiuser detection

Interference reductionSectorization and smart antennasDynamic resource allocationPower control

MIMO techniquesSpace-time processing

Dynamic Resource Allocation

Allocate resources as user and network conditions change

Resources:ChannelsBandwidthPowerRateBase stationsAccess

Optimization criteriaMinimize blocking (voice only systems)Maximize number of users (multiple

classes)Maximize “revenue”

Subject to some minimum performance for each user

BASESTATION

Interference Alignment

Addresses the number of interference-free signaling dimensions in an interference channel

Based on our orthogonal analysis earlier, it would appear that resources need to be divided evenly, so only 2BT/N dimensions available

Jafar and Cadambe showed that by aligning interference, 2BT/2 dimensions are availableEveryone gets half the cake!

Ad-Hoc Networks

Peer-to-peer communications No backbone infrastructure or centralized

control Routing can be multihop. Topology is dynamic. Fully connected with different link SINRs Open questions

Fundamental capacity Optimal routing Resource allocation (power, rate, spectrum,

etc.) to meet QoS

CapacityMuch progress in finding the Shannon

capacity limits of wireless single and multiuser channels

Little known about these limits for mobile wireless networks, even with simple modelsRecent results on scaling laws for

networks

No separation theorems have emergedRobustness, security, delay, and

outage are not typically incorporated into capacity definitions

Network Capacity Results

Multiple access channel (MAC)

Broadcast channel

Relay channel upper/lower bounds

Interference channel

Scaling laws

Achievable rates for small networks

Capacity for Large Networks

(Gupta/Kumar’00)Make some simplifications and

ask for lessEach node has only a single

destinationAll nodes create traffic for their

desired destination at a uniform rate l

Capacity (throughput) is maximum l that can be supported by the network (1 dimensional)

Throughput of random networksNetwork topology/packet

destinations random.Throughput l is random:

characterized by its distribution as a function of network size n.

Find scaling laws for C(n)=l as n .

Extensions Fixed network topologies

(Gupta/Kumar’01) Similar throughput bounds as random networks

Mobility in the network (Grossglauser/Tse’01) Mobiles pass message to neighboring nodes,

eventually neighbor gets close to destination and forwards message

Per-node throughput constant, aggregate throughput of order n, delay of order n.

Throughput/delay tradeoffs Piecewise linear model for throughput-delay

tradeoff (ElGamal et. al’04, Toumpis/Goldsmith’04) Finite delay requires throughput penalty.

Achievable rates with multiuser coding/decoding (GK’03) Per-node throughput (bit-meters/sec) constant,

aggregate infinite. Rajiv will provide more details

S D

Is a capacity region all we need to design networks?

Yes, if the application and network design can be decoupled

Capacity

Delay

Energy

Application metric: f(C,D,E): (C*,D*,E*)=arg max f(C,D,E)

(C*,D*,E*)

Ad Hoc Network Achievable Rate

RegionsAll achievable rate vectors

between nodesLower bounds Shannon capacity

An n(n-1) dimensional convex polyhedronEach dimension defines (net) rate from

one node to each of the othersTime-division strategyLink rates adapt to link SINROptimal MAC via centralized schedulingOptimal routing

Yields performance boundsEvaluate existing protocolsDevelop new protocols

3

1

2

4

5

Achievable Rates

A matrix R belongs to the capacity region if there are rate matrices R1, R2, R3 ,…, Rn such that

Linear programming problem: Need clever techniques to reduce

complexityPower control, fading, etc., easily

incorporatedRegion boundary achieved with optimal

routing

Achievable ratevectors achieved by time division

Capacity region is convex hull ofall rate matrices

0;1;11

in

i iin

i i RR

Example: Six Node Network

Capacity region is 30-dimensional

Capacity Region Slice(6 Node Network)

(a): Single hop, no simultaneous transmissions.(b): Multihop, no simultaneous transmissions. (c): Multihop, simultaneous transmissions.(d): Adding power control (e): Successive interference cancellation, no power control.

jiijRij ,34,12 ,0

Multiplehops

Spatial reuse

SIC

Extensions: - Capacity vs. network size - Capacity vs. topology - Fading and mobility - Multihop cellular

Ad-Hoc NetworkDesign Issues

Ad-hoc networks provide a flexible network infrastructure for many emerging applications.

The capacity of such networks is generally unknown.

Transmission, access, and routing strategies for ad-hoc networks are generally ad-hoc.

Crosslayer design critical and very challenging.

Energy constraints impose interesting design tradeoffs for communication and networking.

Medium Access Control

Nodes need a decentralized channel access methodMinimize packet collisions and insure

channel not wastedCollisions entail significant delay

Aloha w/ CSMA/CD have hidden/exposed terminals

802.11 uses four-way handshakeCreates inefficiencies, especially in multihop

setting

HiddenTerminal

ExposedTerminal

1 2 3 4 5

Frequency Reuse

More bandwidth-efficientDistributed methods needed.Dynamic channel allocation

hard for packet data.Mostly an unsolved problem

CDMA or hand-tuning of access points.

DS Spread Spectrum:Code Assignment

Common spreading code for all nodesCollisions occur whenever receiver can

“hear” two or more transmissions.Near-far effect improves capture.Broadcasting easy

Receiver-orientedEach receiver assigned a spreading

sequence.All transmissions to that receiver use the

sequence.Collisions occur if 2 signals destined for

same receiver arrive at same time (can randomize transmission time.)

Little time needed to synchronize. Transmitters must know code of

destination receiver Complicates route discovery. Multiple transmissions for broadcasting.

Transmitter-oriented

Each transmitter uses a unique spreading sequence

No collisionsReceiver must determine sequence of

incoming packet Complicates route discovery. Good broadcasting properties

Poor acquisition performancePreamble vs. Data assignment

Preamble may use common code that contains information about data code

Data may use specific codeAdvantages of common and specific codes:

Easy acquisition of preamble Few collisions on short preamble New transmissions don’t interfere with the data

block

Introduction to Routing

Routing establishes the mechanism by which a packet traverses the network

A “route” is the sequence of relays through which a packet travels from its source to its destination

Many factors dictate the “best” route

Typically uses “store-and-forward” relayingNetwork coding breaks this paradigm

SourceDestination

Routing Techniques Flooding

Broadcast packet to all neighbors

Point-to-point routingRoutes follow a sequence of linksConnection-oriented or connectionless

Table-drivenNodes exchange information to develop

routing tables

On-Demand RoutingRoutes formed “on-demand”

“A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols”: Broch, Maltz, Johnson, Hu, Jetcheva, 1998. 

If exploited via cooperation and cognition

Friend

Interference: Friend or Foe?

Especially in a network setting

Cooperation in Wireless Networks

Many possible cooperation strategies:Virtual MIMO , generalized relaying, interference

forwarding, and one-shot/iterative conferencingMany theoretical and practice issues:

Overhead, forming groups, dynamics, synch, …

Generalized Relaying

Can forward message and/or interferenceRelay can forward all or part of the

messages Much room for innovation

Relay can forward interference To help subtract it out

TX1

TX2

relay

RX2

RX1X1

X2

Y3=X1+X2+Z3

Y4=X1+X2+X3+Z4

Y5=X1+X2+X3+Z5

X3= f(Y3) Analog network coding

Beneficial to forward bothinterference and message

In fact, it can achieve capacity

S DPs

P1

P2

P3

P4

• For large powers Ps, P1, P2, analog network coding approaches capacity

How to use Feedback in Wireless Networks

Output feedbackCSIAcknowledgementsNetwork/traffic informationSomething else

Noisy/Compressed

MIMO in Ad-Hoc Networks

• Antennas can be used for multiplexing, diversity, or interference cancellation• Cancel M-1 interferers with M antennas• What metric should be optimized?

Cross-Layer Design

Diversity-Multiplexing-Delay Tradeoffs for MIMO Multihop Networks with ARQ

MIMO used to increase data rate or robustness

Multihop relays used for coverage extension ARQ protocol:

Can be viewed as 1 bit feedback, or time diversity,

Retransmission causes delay (can design ARQ to control delay)

Diversity multiplexing (delay) tradeoff - DMT/DMDTTradeoff between robustness, throughput,

and delay

ARQ ARQ

H2 H1

Error Prone

Multiplexing

Low Pe

Beamforming

Fixed ARQ: fixed window size Maximum allowed ARQ round for ith hop satisfies

Adaptive ARQ: adaptive window size Fixed Block Length (FBL) (block-based feedback, easy synchronization)

Variable Block Length (VBL) (real time feedback)

Multihop ARQ Protocols

1

N

ii

L L

iL

Block 1ARQ round 1

Block 1ARQ round 2

Block 1ARQ round 3

Block 2ARQ round 1

Block 2ARQ round 2

Block 1ARQ round 1

Block 1ARQ round 2

Block 1 round 3

Block 2ARQ round 1

Block 2ARQ round 2

Receiver has enough Information to decode

Receiver has enough Information to decode

64

Asymptotic DMDT Optimality Theorem: VBL ARQ achieves optimal DMDT in MIMO multihop

relay networks in long-term and short-term static channels.

Proved by cut-set bound

An intuitive explanation by stopping times: VBL ARQ hasthe smaller outage regions among multihop ARQ protocols

0 4 8 Channel Use

Short-Term Static ChannelAccumlatedInformation

(FBL)

re

t1 t212

Crosslayer Design in Ad-Hoc Wireless

NetworksApplicationNetwork

AccessLinkHardware

Substantial gains in throughput, efficiency, and end-to-end performance from cross-

layer design

Delay/Throughput/Robustness across

Multiple Layers

Multiple routes through the network can be used for multiplexing or reduced delay/loss

Application can use single-description or multiple description codes

Can optimize optimal operating point for these tradeoffs to minimize distortion

AB

Application layer

Network layer

MAC layer

Link layer

Cross-layer protocol design for real-time

media

Capacity assignment

for multiple service classes

Congestion-distortionoptimizedrouting

Adaptivelink layer

techniques

Loss-resilientsource coding

and packetization

Congestion-distortionoptimized

scheduling

Traffic flows

Link capacities

Link state information

Transport layer

Rate-distortion preamble

Joint with T. Yoo, E. Setton, X. Zhu, and B. Girod

Video streaming performance

3-fold increase

5 dB

100

s

(logarithmic scale)

1000

Capacity Delay

Outage

CapacityDelay

Robustness

Network Fundamental Limits

Cross-layer Design andEnd-to-end Performance

Network Metrics

Application Metrics

(C*,D*,R*)

Fundamental Limitsof Wireless Systems

(DARPA Challenge Program)

Research Areas- Fundamental

performance limits and tradeoffs - Node cooperation and

cognition- Adaptive techniques- Layering and Cross-layer

design- Network/application

interface- End-to-end performance optimization and guarantees

ABC

D

Approaches to Network Optimization*

Network Optimization

DynamicProgramming

State Space Reduction

*Much prior work is for wired/static networks

Distributed Optimization

DistributedAlgorithms

Network UtilityMaximization

Wireless NUMMultiperiod NUM

GameTheory

Mechanism DesignStackelberg GamesNash Equilibrium

Dynamic Programming (DP)

Simplifies a complex problem by breaking it into simpler subproblems in recursive manner. Not applicable to all complex problemsDecisions spanning several points in time

often break apart recursively.Viterbi decoding and ML equalization can

use DP

State-space explosionDP must consider all possible states in its

solutionLeads to state-space explosionMany techniques to approximate the state-

space or DP itself to avoid this

Network Utility Maximization

Maximizes a network utility function

Assumes Steady state Reliable links Fixed link capacities

Dynamics are only in the queues

RArtsrU kk .)(maxrouting Fixed link capacityflow k

U1(r1)

U2(r2)

Un(rn)

Ri

Rj

Optimization is Centralized

Course OutlineOverview of Wireless CommunicationsPath Loss, Shadowing, and WB/NB

FadingCapacity of Wireless ChannelsDigital Modulation and its

PerformanceAdaptive ModulationDiversityMIMO SystemsMulticarrier ModulationSpread SpectrumMultiuser Communications &

Wireless Networks Future Wireless Systems

Scarce Wireless Spectrum

and Expensive

$$$

Cognitive Radio Paradigms

UnderlayCognitive radios constrained to

cause minimal interference to noncognitive radios

InterweaveCognitive radios find and exploit

spectral holes to avoid interfering with noncognitive radios

OverlayCognitive radios overhear and

enhance noncognitive radio transmissions

KnowledgeandComplexity

Underlay SystemsCognitive radios determine the

interference their transmission causes to noncognitive nodesTransmit if interference below a given

threshold

The interference constraint may be metVia wideband signalling to maintain

interference below the noise floor (spread spectrum or UWB)

Via multiple antennas and beamforming

NCR

IP

NCRCR CR

Interweave SystemsMeasurements indicate that even

crowded spectrum is not used across all time, space, and frequenciesOriginal motivation for “cognitive” radios

(Mitola’00)

These holes can be used for communicationInterweave CRs periodically monitor

spectrum for holesHole location must be agreed upon between

TX and RXHole is then used for opportunistic

communication with minimal interference to noncognitive users

Overlay SystemsCognitive user has knowledge of

other user’s message and/or encoding strategyUsed to help noncognitive

transmissionUsed to presubtract noncognitive

interferenceRX1

RX2NCR

CR

Performance Gains from Cognitive Encoding

Only the CRtransmits

outer boundour schemeprior schemes

Broadcast Channel with Cognitive Relays (BCCR)

Enhance capacity via cognitive relaysCognitive relays overhear the source messagesCognitive relays then cooperate with the transmitter

in the transmission of the source messages

data

Source

Cognitive Relay 1

Cognitive Relay 2

Wireless Sensor Networks

Energy is the driving constraint Data flows to centralized location Low per-node rates but tens to thousands of nodes Intelligence is in the network rather than in the

devices

• Smart homes/buildings• Smart structures• Search and rescue• Homeland security• Event detection• Battlefield surveillance

Energy-Constrained Nodes

Each node can only send a finite number of bits.Transmit energy minimized by maximizing

bit timeCircuit energy consumption increases with

bit timeIntroduces a delay versus energy tradeoff

for each bit

Short-range networks must consider transmit, circuit, and processing energy.Sophisticated techniques not necessarily

energy-efficient. Sleep modes save energy but complicate

networking.

Changes everything about the network design:Bit allocation must be optimized across all

protocols.Delay vs. throughput vs. node/network

lifetime tradeoffs.Optimization of node cooperation.

Cross-Layer Tradeoffs under Energy Constraints

HardwareAll nodes have transmit, sleep, and transient modesEach node can only send a finite number

of bits Link

High-level modulation costs transmit energy but saves circuit energy (shorter transmission time)

Coding costs circuit energy but saves transmit energyAccess

Power control impacts connectivity and interference

Adaptive modulation adds another degree of freedomRouting:

Circuit energy costs can preclude multihop routing

Modulation Optimization

Tx

Rx

Key AssumptionsNarrow band, i.e. B<<fc

Power consumption of synthesizer and mixer independent of bandwidth B.

Peak power constraintL bits to transmit with deadline

T and bit error probability Pb.Square-law path loss for AWGN channel

2

2)4(,lp

GdGGEE ddrt

Multi-Mode OperationTransmit, Sleep, and

Transient

Deadline T: Total Energy:

trspon TTTT

trspon EEEE

trsynoncont TPTPTP 2)1(

,22 DSPfilIFALNAsynmixc PPPPPPP

,0( spE )2 trsyntr TPE

where is the amplifier efficiency and

Transmit Circuit Transient Energy

Energy Consumption: Uncoded

Two Components Transmission Energy: Decreases

with Ton & B. Circuit Energy: Increases with Ton

Minimizing Energy ConsumptionFinding the optimal pair ( )For MQAM, find optimal constellation size

(b=log2M)

onTB,

Total Energy (MQAM)

Energy Consumption: Coded

Coding reduces required Eb/N0

Reduced data rate increases Ton for block/convolutional codes

Coding requires additional processing

- Is coding energy-efficient - If so, how much total energy is saved.

MQAM Optimization Find BER expression for coded

MQAMAssume trellis coding with 4.7 dB

coding gainYields required Eb/N0Depends on constellation size (bk)

Find transmit energy for sending L bits in Ton sec.

Find circuit energy consumption based on uncoded system and codec model

Optimize Ton and bk to minimize energy

Coded MQAMReference system has bk=3 (coded) or 2 (uncoded)

90% savingsat 1 meter.

Minimum Energy Routing

4 3 2 10.115

0.515

0.185

0.085

0.1 Red: hub nodeGreen: relay/source

ppsRppsRppsR

208060

3

2

1

(0,0)

(5,0)

(10,0)

(15,0)

• Optimal routing uses single and multiple hops

• Link adaptation yields additional 70% energy savings

Cooperative Compression

Source data correlated in space and time

Nodes should cooperate in compression as well as communication and routing Joint source/channel/network codingWhat is optimal: virtual MIMO vs.

relaying

“Green” Cellular Networks

How should cellular systems be designed to conserve energy at both the mobile and base station

The infrastructure and protocols should be redesigned based on miminum energy consumption, includingBase station placement, cell size, distributed

antennasCooperation and cognition MIMO and virtual MIMO techniquesModulation, coding, relaying, routing, and

multicast

Wireless Applications and QoS

Wireless Internet accessNth generation CellularWireless Ad Hoc NetworksSensor Networks Wireless EntertainmentSmart Homes/SpacesAutomated HighwaysAll this and more…

Applications have hard delay constraints, rate requirements,and energy constraints that must be met

These requirements are collectively called QoS

Challenges to meeting QoS

Wireless channels are a difficult and capacity-limited broadcast communications medium

Traffic patterns, user locations, and network conditions are constantly changing

No single layer in the protocol stack can guarantee QoS: cross-layer design needed

It is impossible to guarantee that hard constraints are always met, and average constraints aren’t necessarily good metrics.

Distributed Control over Wireless Links

Automated Vehicles - Cars - UAVs - Insect flyers

- Different design principles Control requires fast, accurate, and reliable feedback. Networks introduce delay and loss for a given rate.

- Controllers must be robust and adaptive to random delay/loss.- Networks must be designed with control as the

design objective.

Course SummaryOverview of Wireless CommunicationsPath Loss, Shadowing, and WB/NB

FadingCapacity of Wireless ChannelsDigital Modulation and its

PerformanceAdaptive ModulationDiversityMIMO Systems ISI CountermeasuresMulticarrier ModulationSpread SpectrumMultiuser Communications &

Wireless Networks Future Wireless Systems

Short Course Megathemes

The wireless vision poses great technical challenges

The wireless channel greatly impedes performance Channel varies randomly randomly Flat-fading and ISI must be compensated for. Hard to provide performance guarantees (needed for

multimedia). We can compensate for flat fading using

diversity or adapting. MIMO channels promise a great capacity

increase.

OFDM is the predominant mechanism for ISI compensation

Channel sharing mechanisms can be centralized or not

Biggest challenge in cellular is interference mitigation

Wireless network design still largely ad-hoc Many interesting applications: require cross-

layer design