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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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Short course wireless communications lecture 3

Short Course:

Wireless Communications: Lecture 3

Professor Andrea Goldsmith

UCSD

March 22-23

La Jolla, CA


Lecture 2 summary

Lecture 2 Summary


Capacity of flat fading channels

Capacity of Flat Fading Channels

  • 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


Frequency selective fading channels

P

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


Linear modulation in fading

Linear Modulation in Fading

  • 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


Variable rate variable power mqam

One of the

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)


Optimal adaptive scheme

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


Diversity

Diversity

  • 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

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


Mcm and ofdm

S

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


Spread spectrum

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


Course outline

Course Outline

  • 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


Course outline1

Course Outline

  • 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


Multiuser channels uplink and downlink

Uplink (Multiple Access

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


Bandwidth sharing

  • Code Space

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


Multiple access ss

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)

a2

a1


Multiuser detection

Multiuser Detection

  • 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


Ideal multiuser detection

Ideal Multiuser Detection

-

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

Random Access

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 capacity fundamental limit on data rates

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


Multiuser fading channel capacity

Multiuser Fading Channel Capacity

  • 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


  • Broadcast channels with isi

    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


    Broadcast mimo channel

    Broadcast MIMO Channel

    Non-degraded

    broadcast channel

    MIMO MAC capacity easy to find

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


    Course outline2

    Course Outline

    • 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

    BS

    Spectral Reuse

    In licensed bands

    and unlicensed bands

    Wifi, BT, UWB,…

    Cellular, Wimax

    • Reuse introduces interference

    Due to its scarcity, spectrum is reused


    Cellular system design

    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 functions

      • Call setup, handoff, routing, adaptive schemes, etc.


    Design issues

    Design Issues

    • Reuse distance

    • Cell size

    • Channel assignment strategy

    • Interference management

      • Multiuser detection

      • MIMO

      • Dynamic resource allocation

    8C32810.44-Cimini-7/98


    Interference friend or foe

    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


    Mimo in cellular

    MIMO in Cellular

    • 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

    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


    Rethinking cells in cellular

    Rethinking “Cells” in Cellular

    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


    Cellular system capacity

    Cellular System Capacity

    • 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


    Area spectral efficiency

    Area Spectral Efficiency

    • 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 =


    Ase vs cell radius

    ASE vs. Cell Radius

    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]


    Improving capacity

    Improving Capacity

    • Interference averaging

      • WCDMA

    • Interference cancellation

      • Multiuser detection

    • Interference reduction

      • Sectorization and smart antennas

      • Dynamic resource allocation

      • Power control

    • MIMO techniques

      • Space-time processing


    Dynamic resource allocation allocate resources as user and network conditions change

    BASE

    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


    Interference alignment

    Interference Alignment

    • 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!


    Ad hoc networks

    Ad-Hoc Networks

    • 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


    Capacity

    Capacity

    • 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


    Network capacity results

    Network Capacity Results

    • 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

    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 .


    Extensions

    Extensions

    • 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


    Is a capacity region all we need to design networks

    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


    Ad hoc network achievable rate regions

    2

    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


    Achievable rates

    Achievable rate

    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


    Example six node network

    Example: Six Node Network

    Capacity region is 30-dimensional


    Capacity region slice 6 node network

    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 network design issues

    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.


    Medium access control

    Hidden

    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


    Frequency reuse

    Frequency Reuse

    • 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

    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.


    Short course wireless communications lecture 3

    • 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


    Introduction to routing

    Introduction to Routing

    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


    Routing techniques

    Routing Techniques

    • 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

    If exploited via cooperation and cognition

    Interference: Friend or Foe?

    Friend

    Especially in a network setting


    Cooperation in wireless networks

    Cooperation in Wireless Networks

    • 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, …


    Generalized relaying

    RX1

    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 both interference and message

    Beneficial to forward bothinterference and message


    In fact it can achieve capacity

    In fact, it can achieve capacity

    P3

    P1

    Ps

    D

    S

    P2

    P4

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


    How to use feedback in wireless networks

    How to use Feedback in Wireless Networks

    Noisy/Compressed

    Output feedback

    CSI

    Acknowledgements

    Network/traffic information

    Something else


    Mimo in ad hoc networks

    MIMO in Ad-Hoc Networks

    • 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


    Diversity multiplexing delay tradeoffs for mimo multihop networks with arq

    Multiplexing

    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

    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


    Asymptotic dmdt optimality

    Asymptotic DMDT Optimality

    • 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


    Crosslayer design in ad hoc wireless networks

    Crosslayer Design in Ad-Hoc Wireless Networks

    • Application

    • Network

    • Access

    • Link

    • Hardware

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


    Delay throughput robustness across multiple layers

    Delay/Throughput/Robustness across Multiple Layers

    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


    Cross layer protocol design for real time media

    Cross-layer protocol design for real-time media

    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


    Video streaming performance

    Video streaming performance

    s

    5 dB

    3-fold increase

    100

    1000

    (logarithmic scale)


    Short course wireless communications lecture 3

    FLoWS

    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


    Approaches to network optimization

    Approaches to Network Optimization*

    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


    Dynamic programming dp

    Dynamic Programming (DP)

    • 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


    Network utility maximization

    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


    Course outline3

    Course Outline

    • 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


    Scarce wireless spectrum

    Scarce Wireless Spectrum

    $$$

    and Expensive


    Cognitive radio paradigms

    Cognitive Radio Paradigms

    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


    Underlay systems

    Underlay Systems

    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


    Interweave systems

    Interweave Systems

    • 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


    Overlay systems

    Overlay Systems

    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


    Performance gains from cognitive encoding

    • outer bound

    • our scheme

    • prior schemes

    Performance Gains from Cognitive Encoding

    • Only the CR

    • transmits


    Broadcast channel with cognitive relays bccr

    Broadcast Channel with Cognitive Relays (BCCR)

    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


    Short course wireless communications lecture 3

    Wireless Sensor Networks

    • 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


    Energy constrained nodes

    Energy-Constrained Nodes

    • 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.


    Short course wireless communications lecture 3

    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


    Modulation optimization

    Modulation Optimization

    Tx

    Rx


    Key assumptions

    Key Assumptions

    • 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


    Multi mode operation transmit sleep and transient

    Transmit

    Transient Energy

    Circuit

    Multi-Mode OperationTransmit, Sleep, and Transient

    • Deadline T:

    • Total Energy:

    where a is the amplifier efficiency and


    Energy consumption uncoded

    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)


    Total energy mqam

    Total Energy (MQAM)


    Energy consumption coded

    Energy Consumption: Coded

    • 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.


    Mqam optimization

    MQAM Optimization

    • 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


    Coded mqam

    Coded MQAM

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

    90% savings

    at 1 meter.


    Minimum energy routing

    Minimum Energy Routing

    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


    Cooperative compression

    Cooperative Compression

    • 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


    Green cellular networks

    “Green” Cellular Networks

    • 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 applications and qos

    Wireless Applications and 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

    Challenges to meeting 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.


    Distributed control over wireless links

    Distributed Control over Wireless Links

    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.


    Course summary

    Course Summary

    • 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

    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


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