Course Summary Overview/history of wireless communications (Ch.

1)

Signal Propagation and Channel Models (Ch. 2 + 3)

Fundamental Capacity Limits (Ch. 4)

Modulation and Performance Metrics (Ch. 5)

Impact of Channel on Performance (Ch. 6)

Adaptive Modulation (Ch. 9)

Diversity (Ch. 7)

Spread Spectrum (Ch. 13)

Cellular Networks (Ch. 15)

Future Wireless Networks:The Vision

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

Ubiquitous Communication Among People and Devices

• Hard Delay/Energy Constraints• Hard Rate Requirements

• +++

“Mega-themes” of TTT4160-1

The wireless vision poses great technical challenges The wireless channel greatly impedes performance

Low fundamental capacity. Channel is randomly time-varying ISI and other interference must be compensated for ... Hard to provide performance guarantees (needed for multimedia!).

We can compensate for flat fading using diversity or adaptation.

(MIMO channels promise a great capacity increase.)

A plethora of ISI compensation techniques exist Various tradeoffs in performance, complexity, and implementation.

Design Challenges, cont’d

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

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

Applications are heterogeneous - with hard constraints that must be met by the network(s)

Energy, delay, and rate constraints change design principles across all layers of the protocol stack (cross-layer design)

Signal Propagation: Main effects

Path Loss

Shadowing

Multipathd

Pr/Pt

d=vt

Statistical Multipath Model

Random # of multipath components, each with varying amplitude, phase, doppler, and delay

Narrowband channel (signal BW smaller than coherence BW): FLAT fading Signal amplitude varies randomly (complex Gaussian). Characterized by 2nd order statistics (Bessel function), average fade duration,

etc.

Wideband channel: FREQUENCY-SELECTIVE Characterized in general by channel scattering function (simplified: Bc BD)

Modulation Considerations

We want: high rates, high spectral efficiency, high power efficiency, robustness to channel variations, cheap implementations... Trade-off required!

Linear Modulation (MPAM, MPSK, MQAM) Information encoded in amplitude/phase More spectrally efficient than nonlinearEasier to adapt to channel conditions. Issues: differential encoding, pulse shaping, bit mapping.

Nonlinear modulation (FSK) Information encoded in frequency More robust to channel and amplifier nonlinearities

Linear Modulation in AWGN

ML detection induces decision regionsExample: 8PSK

Ps (symbol error rate) depends on# of nearest neighborsMinimum distance dmin (depends on s)Approximate expression:

M is # of nearest neighbors; M relates dmin and average symbol energy.( )sMMs QP γβα≈

dmin

Linear Modulation in Fading

In fading s - and therefore Ps -

is randomMetrics: outage probability,

average Ps , or combined outage and average.

Ps

Ps(target)

Outage

Ps

Ts

Ts

sssss dpPP )()(∫=

Moment Generating Function (MGF)

Approach

Simplifies average Ps calculation

Uses alternate Q function representation

Ps reduces to MGF of s-distribution

Closed form, or simple numerical calculation for general fading distributions

In general: Fading greatly increases average Ps .

Doppler Effects

High Doppler causes channel phase to decorrelate between symbols

Leads to an irreducible error floor for differential modulationIncreasing power does not reduce error

Error floor depends on BDTs product (higher the larger it is)

Delay spread exceeding one symbol time causes ISI (self-interference).

ISI leads to irreducible error floor Increasing signal power increases ISI power

ISI requires that Ts>>Tm (Rs<<Bc)

ISI Effects

Tm0

Capacity of Flat Fading Channels

Three casesFading statistics knownFade value known at receiverFade value known at receiver and

transmitter

Optimal AdaptationVary rate and power relative to channelOptimal power adaptation is water-fillingExceeds AWGN channel capacity at low

SNRsSuboptimal techniques come close to

capacity

Variable-Rate Variable-Power MQAM

UncodedData Bits Delay

PointSelector

M()-QAM ModulatorPower: S()

To Channel

(t) (t)

log2 M() Bits One of theM() Points

BSPK 4-QAM 16-QAM

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

Optimal Adaptive Scheme

Power Water-Filling

Spectral Efficiency

S

S

K K K( )

=− ≥ =⎧

⎨⎩

1 10

0

0 else

1

0

1

Kk

R

Bp d

K K

=⎛⎝⎜

⎞⎠⎟

∞

∫log ( ) .2

Equals Shannon capacity with an effective power loss of K.

Practical Adaptation Constraints

Constellation restriction Constant power restriction Constellation updates. Estimation error. Estimation delay. Lead to practical adaptive

modulation schemes (Ch. 9)

Diversity

Send bits over independent fading pathsCombine paths to mitigate fading effects.

Independent fading paths - how to create?Space, time, frequency, polarization diversity.

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

Diversity Performance

Maximal Ratio Combining (MRC)Optimal technique (maximizes output SNR)Combiner SNR is the sum of the branch SNRs.Distribution of SNR hard to obtain.Can use MGF approach for simplified analysis.Exhibits 10-40 dB gains in Rayleigh fading.

Selection Combining (SC)Combiner SNR is the maximum of the branch

SNRs.Diminishing returns with # of antennas.CDF easy to obtain, pdf found by differentiating.Can get up to about 20 dB of gain.

Spread Spectrum

Signal occupies channel bandwidth much larger than actual signal bandwidth

Two main types:Direct Sequence Spread Spectrum

(DSSS)Frequency Hopping Spread Spectrum

Focus on DSSS hereBasis for CDMA

Direct Sequence Spread Spectrum

(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 autocorrelation

Must synchronize to incoming signal

s(t) sc(t)

Tb=KTc Tc

S(f)Sc(f)

1/Tb 1/Tc

S(f)*Sc(f)

2

RAKE Receiver Multibranch receiver

Branches synchronized to different MP components

These components can be coherently combinedUse SC, MRC, or EGC

x

x

sc(t)

sc(t-iTc)

xsc(t-NTc)

Demod

Demod

Demod

y(t)

DiversityCombiner

dk^

CDMA: 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)

7C29822.033-Cimini-9/97

Bandwidth Sharing in general

FDMA

TDMA

CDMA (Hybrid Schemes)

Code Space

Time

Frequency

Code Space

Time

FrequencyCode Space

Time

Frequency

Multiuser Detection In all CDMA systems and cellular systems

in general, 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 Must however have a good estimate of the

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

Power adaptationSmart antennasMultiuser detectionDynamic resource allocation

Dynamic Resource Allocation

Allocate resources as user and network conditions change

Resources:ChannelsBandwidthPowerRateBase stationsAccess

Optimization criteriaMinimize blocking (voice only systems)Maximize number of usersMaximize “revenue”

Subject to some minimum performance for each user

BASESTATION

NETWORK ISSUES

8C32810.53-Cimini-7/98

Higher LayerNetworking Issues

Architecture

Mobility ManagementIdentification/authenticationRoutingHandoff

Control

Reliability and Quality-of-Service

A final return to 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

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

Average constraints aren’t necessarily good metrics (e.g. in very slow fading, non-ergodic conditions).

Cross-layer Design (or “IET meets

ITEM”)

ApplicationNetworkAccessLink

Hardware

Delay ConstraintsRate RequirementsEnergy Constraints

Mobility

Optimize and adapt across design layersProvide robustness to uncertainty

Schedule dedicated resources

The Exam: Practical stuff

Time: Saturday, June 2nd, 09.00 - 13.00 Tools/aids allowed: Calculator only List/sheet containing important/relevant

formulas will be provided as part of the exam

Mostly: Expect same “style” of questions as in exercises

Exam preparations

For exercises, and solutions to exercises: Consult course web page.

For questions to exercises: Consult the teaching assistant, Changmian Wang (Sébastien de la Kethulle has graduated and has a new job)

For questions to book: Consult Changmian Wang or Geir Øien (in that order ;-) ).

For questions to lecture notes: Consult Geir Øien or Changmian Wang (in that order...).

Course curriculum

All curriculum can be found in course textbook, ”Wireless Communications” by Andrea Goldsmith

See list of chapters/sections in separate handout (can also be found on web page)

In general ”lectures and exercises define the curriculum”

Details not covered either in lectures or exercises will not be emphasized at exam!