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 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 +++

“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 Multipath d 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 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

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

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

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 .

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)

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

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

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)

Optimal Adaptive Scheme gk g Power Water-Filling Spectral Efficiency g 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 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) ...

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 here Basis 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 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

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)

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

Bandwidth Sharing in general FDMA TDMA CDMA (Hybrid Schemes) Code Space Time Frequency Code Space Time Frequency Code Space Time Frequency 7C29822.033-Cimini-9/97

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 ...!

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.

Design Issues Reuse distance Cell size Channel assignment strategy Interference management Power adaptation Smart antennas Multiuser detection Dynamic resource allocation 8C32810.44-Cimini-7/98

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

Higher Layer Networking Issues NETWORK ISSUES Architecture Mobility Management Identification/authentication Routing Handoff Control Reliability and Quality-of-Service 8C32810.53-Cimini-7/98

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

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”) 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

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!