short course: wireless communications : lecture 3

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

Bcf

P

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

dpPP ss )()(0

sMMs QP

Variable-Rate Variable-Power MQAM

UncodedData Bits Delay

PointSelector

M(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.

S

S

K K K( )

1 10

0

0 else

g

1

0

1

K

gk g

R

Bp d

K K

log ( ) .2

Equals Shannon capacity with

an effective power loss of K.

Diversity

Send 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 channels RH-fold capacity increase over SISO

system Demodulation complexity reduction Can also use antennas for diversity

(beamforming) Leads to capacity versus diversity

tradeoff in MIMO

MCM and OFDMMCM splits channel into flat fading

subchannels Fading 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

x

cos(2pf0t)

x

cos(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

interference ISI mitigation a function of code

autocorrelation Must synchronize to incoming signal RAKE 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 Outline Overview of Wireless Communications Path Loss, Shadowing, and WB/NB

Fading Capacity of Wireless Channels Digital Modulation and its

Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser Communications Wireless Networks Future Wireless Systems

Lecture 3

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 Division Multiuser Detection

Space (MIMO Systems) Hybrid Schemes

Code Space

Time

Frequency Code Space

Time

FrequencyCode 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

1221

0

212211

122

0

222

111

c

T

cc

cccc

T

cc

fdddttstsdd

dttftftstststftststx

b

b

a2

a1

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-limited Often 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 known Interference can be detected and

subtracted out Better 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 Access

Dedicated channels wasteful for data use statistical multiplexing

Techniques Aloha Carrier sensing

Collision detection or avoidance Reservation protocols PRMA

Retransmissions used for corrupted data

Poor throughput and delay characteristics under heavy loading Hybrid 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 channels Superposition coding is applied to each

subchannel. Power must be optimized across

subchannels and between users in each subchannel.

w1k

H1(w)

H2(w)

w2kxk

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

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 functions Call 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

IN

PSNR

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 Access Network MIMO: Form virtual antenna

arrays Downlink is a MIMO BC, uplink is a MIMO

MAC Can treat “interference” as a known signal

or noise Can 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 interference Cooperating BSs form a MIMO array: what is a cell? Relays change cell shape and boundaries Distributed antennas move BS towards cell boundary Femtocells 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 Capacity Shannon 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 Efficiency Capacity 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

100A

vera

ge

Are

a S

pec

tra

l E

ffic

ien

cy[B

ps/

Hz/

Km

2 ]

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

D=4R

D=6R

D=8R

fc=2 GHz

Improving Capacity

Interference averaging WCDMA

Interference cancellation Multiuser detection

Interference reduction Sectorization and smart antennas Dynamic resource allocation Power control

MIMO techniques Space-time processing

Dynamic Resource Allocation

Allocate resources as user and network conditions change

Resources: Channels Bandwidth Power Rate Base stations Access

Optimization criteria Minimize 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

Capacity

Much 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 models Recent results on scaling laws for

networks

No separation theorems have emerged

Robustness, 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 nodes Lower bounds Shannon capacity

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

one node to each of the others Time-division strategy Link rates adapt to link SINR Optimal MAC via centralized scheduling Optimal routing

Yields performance bounds Evaluate existing protocols Develop 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

complexity Power control, fading, etc., easily

incorporated Region boundary achieved with optimal

routing

Achievable ratevectors achieved by time division

Capacity region is convex hull ofall rate matrices

0;1;11

i

n

i ii

n

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 method Minimize packet collisions and insure

channel not wasted Collisions entail significant delay

Aloha w/ CSMA/CD have hidden/exposed terminals

802.11 uses four-way handshake Creates 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 nodes Collisions occur whenever receiver can

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

Receiver-oriented Each 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 collisions Receiver must determine sequence of

incoming packet Complicates route discovery. Good broadcasting properties

Poor acquisition performance Preamble vs. Data assignment

Preamble may use common code that contains information about data code

Data may use specific code Advantages 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” relaying Network coding breaks this paradigm

SourceDestination

Routing Techniques Flooding

Broadcast packet to all neighbors

Point-to-point routing Routes follow a sequence of links Connection-oriented or connectionless

Table-driven Nodes exchange information to develop

routing tables

On-Demand Routing Routes 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 conferencing

Many theoretical and practice issues: Overhead, forming groups, dynamics, synch, …

Generalized Relaying

Can forward message and/or interference Relay 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 feedback CSI Acknowledgements Network/traffic information Something 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/DMDT Tradeoff 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 1

Block 1ARQ round 2

Block 1ARQ round 2

Block 1ARQ round 3

Block 1ARQ round 3

Block 2ARQ round 1

Block 2ARQ round 1

Block 2ARQ round 2

Block 2ARQ round 2

Block 1ARQ round 1

Block 1ARQ round 1

Block 1ARQ round 2

Block 1ARQ round 2

Block 1 round 3Block 1 round 3

Block 2ARQ round 1

Block 2ARQ round 1

Block 2ARQ round 2

Block 2ARQ round 2

Receiver has enough Information to decodeReceiver has enough Information to decode

Receiver has enough Information to decodeReceiver 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

Networks

ApplicationNetwork

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

A

B

Application layer

Network layer

MAC layer

Link layer

Cross-layer protocol design for real-time

media

Capacity assignment

for multiple service classes

Capacity assignment

for multiple service classes

Congestion-distortionoptimizedrouting

Congestion-distortionoptimizedrouting

Adaptivelink layer

techniques

Adaptivelink layer

techniques

Loss-resilientsource coding

and packetization

Loss-resilientsource coding

and packetization

Congestion-distortionoptimized

scheduling

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

Capacity

Delay

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

A

BC

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 problems Decisions spanning several points in time

often break apart recursively. Viterbi decoding and ML equalization can

use DP

State-space explosion DP must consider all possible states in its

solution Leads to state-space explosion Many 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 .)(max

routing Fixed link capacityflow k

U1(r1)

U2(r2)

Un(rn)

Ri

Rj

Optimization is Centralized



Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser 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

Knowledge

andComplexi

ty

Underlay Systems Cognitive radios determine the

interference their transmission causes to noncognitive nodes Transmit if interference below a given

threshold

The interference constraint may be met Via wideband signalling to maintain

interference below the noise floor (spread spectrum or UWB)

Via multiple antennas and beamforming

NCR

IP

NCRCR CR

Interweave Systems Measurements indicate that even

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

(Mitola’00)

These holes can be used for communication Interweave CRs periodically monitor

spectrum for holes Hole location must be agreed upon between

TX and RX Hole is then used for opportunistic

communication with minimal interference to noncognitive users

Overlay Systems

Cognitive 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 bound

our schemeprior schemes

Broadcast Channel with Cognitive Relays (BCCR)

Enhance capacity via cognitive relays Cognitive relays overhear the source messages Cognitive 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 time Circuit energy consumption increases with

bit time Introduces 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

Hardware All nodes have transmit, sleep, and

transient modes Each 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 energy

Access Power control impacts connectivity and

interference Adaptive modulation adds another degree

of freedom

Routing: Circuit energy costs can preclude

multihop routing

Modulation Optimization

Tx

Rx

Key Assumptions

Narrow band, i.e. B<<fcPower 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(,

G

dGGEE 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 a 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 Consumption Finding 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 MQAM Assume trellis coding with 4.7 dB

coding gain Yields required Eb/N0 Depends 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

0.1Red: hub nodeGreen: relay/source

ppsR

ppsR

ppsR

20

80

60

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 coding What 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 Summary Overview of Wireless Communications Path Loss, Shadowing, and WB/NB


Performance Adaptive Modulation Diversity MIMO Systems ISI Countermeasures Multicarrier Modulation Spread Spectrum Multiuser 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

short course: wireless communications : lecture 3

Documents

fading gs

independent fading

power relative

receiverfade value

subbandseach subband

low snrssuboptimal techniques

ps randomperformance

outage probability