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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!