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# Short Course: Wireless Communications : Lecture 3 - PowerPoint PPT Presentation

Short Course: Wireless Communications : Lecture 3. Professor Andrea Goldsmith. UCSD March 22-23 La Jolla, CA. Lecture 2 Summary. Capacity of Flat Fading Channels. Four cases Nothing known Fading statistics known Fade value known at receiver Fade value known at receiver and transmitter

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#### Presentation Transcript

Wireless Communications: Lecture 3

Professor Andrea Goldsmith

UCSD

March 22-23

La Jolla, CA

• Four cases

• Nothing known

• Fading statistics known

• Fade value known at receiver

• Fade value known at receiver and transmitter

• Optimal 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

Bc

Frequency Selective Fading Channels

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

• Capacity of time-varying channel unknown

• Approximate by dividing into subbands

• Each subband has width Bc (like MCM).

• Independent fading in each subband

• Capacity is the sum of subband capacities

1/|H(f)|2

f

• BER in AWGN:

• In fading gsand therefore Psrandom

• Performance metrics:

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

• Average Ps , Ps:

• Combined outage and average Ps

M(g) Points

log2 M(g) Bits

To Channel

M(g)-QAM

Modulator

Power: S(g)

Point

Selector

Uncoded

Data Bits

Delay

g(t)

g(t)

16-QAM

4-QAM

BSPK

Variable-Rate Variable-Power MQAM

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

gk

g

Optimal Adaptive Scheme

• Power Water-Filling

• Spectral Efficiency

• Practical Constraints

• Constellation and power restriction

• Constellation updates.

• Estimation error and delay.

Equals Shannon capacity with

an effective power loss of K.

g

• Send bits over independent fading paths

• Combine paths to mitigate fading effects.

• Independent fading paths

• Space, time, frequency, polarization diversity.

• Combining techniques

• Selection 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

cos(2pf0t)

cos(2pfNt)

x

x

MCM and OFDM

R/N bps

• MCM 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

QAM

Modulator

R bps

Serial

To

Parallel

Converter

R/N bps

QAM

Modulator

Tc

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(f)

s(t)

sc(t)

Sc(f)

S(f)*Sc(f)

1/Tb

1/Tc

Tb=KTc

2

• 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

• 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

Channel or MAC):

Many Transmitters

to One Receiver.

Downlink (Broadcast Channel or BC):

One Transmitter

to Many Receivers.

x

x

x

x

h1(t)

h21(t)

h22(t)

h3(t)

Multiuser Channels:Uplink and Downlink

R3

R2

R1

Uplink and Downlink typically duplexed in time or frequency

Code Space

Code Space

Time

Time

Time

Frequency

Frequency

Frequency

Bandwidth Sharing

• Frequency Division

• Time Division

• Code Division

• Multiuser Detection

• Space (MIMO Systems)

• Hybrid Schemes

7C29822.033-Cimini-9/97

• 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)

a2

a1

• 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

-

Signal 1

=

A/D

Signal 1

Demod

A/D

A/D

A/D

A/D

Iterative

Multiuser

Detection

Signal 2

Signal 2

Demod

-

=

Why Not Ubiquitous Today?

Power and A/D Precision

RANDOM ACCESS TECHNIQUES

• 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

7C29822.038-Cimini-9/97

Multiuser Channel CapacityFundamental Limit on Data Rates

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

• 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

R3

R2

R3

R2

R1

R1

• 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

• H1(w)

H2(w)

Broadcast Channels with ISI

w1k

• 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.

xk

w2k

Non-degraded

broadcast channel

MIMO MAC capacity easy to find

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

• 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

Spectral Reuse

In licensed bands

and unlicensed bands

Wifi, BT, UWB,…

Cellular, Wimax

• Reuse introduces interference

Due to its scarcity, spectrum is reused

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.

• Reuse distance

• Cell size

• Channel assignment strategy

• Interference management

• Multiuser detection

• MIMO

• Dynamic resource allocation

8C32810.44-Cimini-7/98

Interference: Friend or Foe?

Increases BER, reduces capacity

Multiuser detection can

completely remove interference

• If treated as noise: Foe

• If decodable: Neither friend nor foe

• 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 Benefits

• Antenna 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

How should cellular

systems be designed?

• 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.

Coop

MIMO

Femto

Relay

Will gains in practice be

big or incremental; in

capacity or coverage?

DAS

• 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

• 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.

BASE

STATION

A=.25D2p =

fc=2 GHz

101

100

D=4R

Average Area Spectral Efficiency

[Bps/Hz/Km2]

D=6R

D=8R

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cell Radius R [Km]

• Interference averaging

• WCDMA

• Interference cancellation

• Multiuser detection

• Interference reduction

• Sectorization and smart antennas

• Dynamic resource allocation

• Power control

• MIMO techniques

• Space-time processing

STATION

Dynamic Resource AllocationAllocate 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

• 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 available

• Everyone gets half the cake!

• 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

• 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

• 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 less

• Each node has only a single destination

• All 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 networks

• Network 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 .

• 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

Application metric: f(C,D,E):

(C*,D*,E*)=arg max f(C,D,E)

(C*,D*,E*)

Is a capacity region all we need to design networks?

Yes, if the application and network design can be decoupled

Capacity

Delay

Energy

3

5

4

1

Ad Hoc Network Achievable Rate Regions

• All 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

vectors achieved

by time division

Capacity region

is convex hull of

all rate matrices

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

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.

Multiple

hops

SIC

Spatial

reuse

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.

Terminal

Exposed

Terminal

1

2

3

4

5

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

• More bandwidth-efficient

• Distributed 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

Destination

Source

• 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

• 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

Interference: Friend or Foe?

Friend

Especially in a network setting

• 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, …

TX1

X1

Y4=X1+X2+X3+Z4

relay

Y3=X1+X2+Z3

X3= f(Y3)

Y5=X1+X2+X3+Z5

X2

TX2

RX2

Generalized Relaying

Analog network coding

• 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

Beneficial to forward bothinterference and message

P3

P1

Ps

D

S

P2

P4

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

Noisy/Compressed

Output feedback

CSI

Acknowledgements

Network/traffic information

Something else

• 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

Error Prone

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

ARQ

ARQ

Beamforming

H2

H1

Low Pe

• 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

Multihop ARQ Protocols

• 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)

Block 1

ARQ round 1

Block 1

ARQ round 2

Block 1

ARQ round 3

Block 2

ARQ round 2

Block 2

ARQ round 1

Receiver has enough

Information to decode

Block 1

ARQ round 1

Block 2

ARQ round 1

Block 2

ARQ round 2

Block 1

round 3

Block 1

ARQ round 2

Receiver has enough

Information to decode

• 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 has

the smaller outage regions among

multihop ARQ protocols

• Application

• Network

• Access

• Link

• Hardware

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

B

• 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

Loss-resilientsource codingand packetization

Application layer

Rate-distortion preamble

Congestion-distortionoptimized

scheduling

Transport layer

Congestion-distortionoptimized

routing

Traffic flows

Network layer

Capacity assignmentfor multiple service classes

Link capacities

MAC layer

Link state information

Adaptive

link layertechniques

Joint with T. Yoo, E. Setton,

X. Zhu, and B. Girod

Link layer

s

5 dB

3-fold increase

100

1000

(logarithmic scale)

Fundamental Limits

of Wireless Systems

(DARPA Challenge Program)

Network Metrics

C

B

A

NetworkFundamental Limits

Capacity

Delay

D

Outage

Cross-layer Design and

End-to-end Performance

• 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

Capacity

(C*,D*,R*)

Delay

Robustness

Application Metrics

Network

Optimization

Dynamic

Programming

Game

Theory

Network Utility

Maximization

Distributed

Optimization

State Space

Reduction

Mechanism Design

Stackelberg Games

Nash Equilibrium

Wireless NUM

Multiperiod NUM

Distributed

Algorithms

*Much prior work is for wired/static networks

• Simplifies a complex problem by breaking it into simpler subproblems in recursivemanner.

• 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

U1(r1)

U2(r2)

Un(rn)

Network Utility Maximization

• Maximizes a network utility function

• Assumes

• Steady state

• Reliable links

• Fixed link capacities

• Dynamics are only in the queues

Ri

Rj

flow k

routing

Fixed link capacity

Optimization is Centralized

• 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

\$\$\$

and Expensive

Knowledge

and

Complexity

• Underlay

• Cognitive radios constrained to cause minimal interference to noncognitive radios

• Interweave

• Cognitive radios find and exploit spectral holes to avoid interfering with noncognitive radios

• Overlay

• Cognitive radios overhear and enhance noncognitive radio transmissions

IP

NCR

CR

CR

NCR

• 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

• 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

RX1

CR

RX2

NCR

• Cognitive user has knowledge of other user’s message and/or encoding strategy

• Used to help noncognitive transmission

• Used to presubtract noncognitive interference

• our scheme

• prior schemes

Performance Gains from Cognitive Encoding

• Only the CR

• transmits

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

Cognitive Relay 1

data

Source

Cognitive Relay 2

• Smart homes/buildings

• Smart structures

• Search and rescue

• Homeland security

• Event detection

• Battlefield surveillance

• 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

• 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

• Narrow band, i.e. B<<fc

• Power consumption of synthesizer and mixer independent of bandwidth B.

• Peak power constraint

• L bits to transmit with deadline Tand bit error probability Pb.

• Square-law path loss for AWGN channel

Transient Energy

Circuit

Multi-Mode OperationTransmit, Sleep, and Transient

• Deadline T:

• Total Energy:

where a is the amplifier efficiency and

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)

• 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.

• 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

Reference system has bk=3 (coded) or 2 (uncoded)

90% savings

at 1 meter.

0.1

Red: hub node

Green: relay/source

0.085

2

4

1

3

0.115

0.185

(15,0)

(0,0)

(5,0)

(10,0)

0.515

• Optimal routing uses single and multiple hops

• Link adaptation yields additional 70% energy savings

• 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

• How should cellular systems be designed to conserve energy at both the mobile andbase station

• The infrastructure and protocols should be redesigned based on miminum energy consumption, including

• Base station placement, cell size, distributed antennas

• Cooperation and cognition

• MIMO and virtual MIMO techniques

• Modulation, coding, relaying, routing, and multicast

Wireless Internet access

Nth generation Cellular

Wireless Ad Hoc Networks

Sensor Networks

Wireless Entertainment

Smart Homes/Spaces

Automated Highways

All this and more…

Applications have hard delay constraints, rate requirements,

and energy constraints that must be met

These requirements are collectively called 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.

Automated Vehicles

- Cars

- UAVs

- Insect flyers

- Different design principles

• Control requires fast, accurate, and reliable feedback.

• Networks introduce delay and lossfor a given rate.

- Controllers must be robust and adaptive to random delay/loss.

- Networks must be designed with control as the design objective.

• 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

• 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