1. Course Summary Overview/history of wireless communications (Ch. 1)
Signal Propagation and Channel Models (Ch )
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)

2. Future Wireless Networks: The Vision
Ubiquitous Communication Among People and Devices
Wireless Internet access
Nth generation Cellular
Wireless Ad Hoc Networks
Sensor Networks
Wireless Entertainment
Smart Homes/Spaces
Automated Highways
All this and more…
Hard Delay/Energy Constraints
Hard Rate Requirements
+++

3. “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.

4. 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)

5. Signal Propagation: Main effects
Path Loss
Shadowing
Multipath d
Pr/Pt
d=vt

6. 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)

7. 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 nonlinear
Easier 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

8. Linear Modulation in AWGN
ML detection induces decision regions
Example: 8PSK
Ps (symbol error rate) depends on
# of nearest neighbors
Minimum distance dmin (depends on gs)
Approximate expression:
M is # of nearest neighbors; M relates dmin and average symbol energy.
dmin

9. Linear Modulation in Fading
In fading gs - and therefore Ps - is random
Metrics: outage probability, average Ps , or combined outage and average. Ts Ps
Outage
Ps(target) Ps Ts

10. Moment Generating Function (MGF) Approach
Simplifies average Ps calculation
Uses alternate Q function representation
Ps reduces to MGF of gs-distribution
Closed form, or simple numerical calculation for general fading distributions
In general: Fading greatly increases average Ps .

11. Doppler Effects
High Doppler causes channel phase to decorrelate between symbols
Leads to an irreducible error floor for differential modulation
Increasing power does not reduce error
Error floor depends on BDTs product (higher the larger it is)

12. ISI Effects
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) Tm

13. Capacity of Flat Fading Channels
Three cases
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

14. Variable-Rate Variable-Power MQAM
Uncoded
Data Bits
Delay
Point
Selector
M(g)-QAM
Modulator
Power: S(g)
To Channel
g(t)
log2 M(g) Bits
One of the
M(g) Points
BSPK
4-QAM
16-QAM
Goal: Optimize S(g) and M(g) to maximize EM(g)

15. Optimal Adaptive Schemegk g
Power Water-Filling
Spectral Efficiency g
Equals Shannon capacity with an effective power loss of K.

16. Practical Adaptation Constraints
Constellation restriction
Constant power restriction
Constellation updates.
Estimation error.
Estimation delay.
Lead to practical adaptive modulation schemes (Ch. 9)

17. Diversity Send bits over independent fading paths
Combine paths to mitigate fading effects.
Independent fading paths - how to create?
Space, time, frequency, polarization diversity.
Combining techniques
Selection combining (SC)
Equal gain combining (EGC)
Maximal ratio combining (MRC)
...

18. 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 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.

19. 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 here
Basis for CDMA

20. 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 interference
ISI mitigation a function of code autocorrelation
Must synchronize to incoming signal
S(f)
s(t)
sc(t)
Sc(f)
S(f)*Sc(f)
1/Tb
1/Tc Tc
Tb=KTc 2

21. RAKE Receiver Multibranch receiver
Branches synchronized to different MP components
These components can be coherently combined
Use SC, MRC, or EGC x
Demod
y(t)
sc(t) ^ dk
Diversity
Combiner x
Demod
sc(t-iTc) x
Demod
sc(t-NTc)

22. 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) a a

23. Bandwidth Sharing in general
FDMA
TDMA
CDMA
(Hybrid Schemes)
Code Space
Time
Frequency
Code Space
Time
Frequency
Code Space
Time
Frequency
7C Cimini-9/97

24. 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 ...!

25. Cellular System Design
BASE
STATION
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.

26. Design Issues Reuse distance Cell size Channel assignment strategy
Interference management
Power adaptation
Smart antennas
Multiuser detection
Dynamic resource allocation
8C Cimini-7/98

27. Dynamic Resource Allocation Allocate resources as user and network conditions change
BASE
STATION
Resources:
Channels
Bandwidth
Power
Rate
Base stations
Access
Optimization criteria
Minimize blocking (voice only systems)
Maximize number of users
Maximize “revenue”
Subject to some minimum performance for each user

28. Higher Layer Networking Issues
NETWORK ISSUES
Architecture
Mobility Management
Identification/authentication
Routing
Handoff
Control
Reliability and Quality-of-Service
8C Cimini-7/98

29. A final return to QoS...
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

30. 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).

31. Cross-layer Design (or “IET meets ITEM”)
Application
Network
Access
Link
Hardware
Delay Constraints
Rate Requirements
Energy Constraints
Mobility
Interdisciplinary research, design, and development very challenging, but necessary to meet the requirements of future wireless applications
Optimize and adapt across design layers
Provide robustness to uncertainty
Schedule dedicated resources

32. The Exam: Practical stuff
Time: Saturday, June 2nd,
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

33. 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...).

34. 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!