Transcript Wireless Communications Research Overview

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
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
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
P
Bc
f
Linear Modulation in Fading



BER in AWGN: Ps   M Q  M g s

In fading gs and therefore Ps random

Performance metrics:
 Outage probability:
 Average Ps , Ps:
p(Ps>Ptarget)=p(g<gtarget)

Ps   Ps (g ) p(g )dg
0
 Combined
outage and average Ps
Variable-Rate Variable-Power MQAM
One of the
M(g) Points
log2 M(g) Bits
Uncoded
Data Bits
M(g)-QAM
Modulator
Power: S(g)
Point
Selector
Delay
To Channel
g(t)
g(t)
BSPK
4-QAM
16-QAM
Goal: Optimize S(g) and M(g) to maximize EM(g)
Optimal Adaptive Scheme

Power Water-Filling
g  g
S (g )  g  g

S
 0
1
K
1
gK
else
g 
R
  log   p(g )dg .
B g
g 

2

0
K
Spectral Efficiency
K
g
g0
1
K
0

1
gk
g
Equals Shannon capacity with
an effective power loss of K.
K
Practical Constraints
 Constellation and power restriction
 Constellation updates.
 Estimation error and delay.
g
Diversity

Send bits over independent fading paths
 Combine

Independent fading paths
 Space,

paths to mitigate fading effects.
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

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

MCM splits channel into flat fading subchannels


Fading across subcarriers degrades performance.
Compensate through coding or adaptation
R bps
R/N bps
Serial
To
Parallel
Converter
QAM
Modulator
x
cos(2pf0t)
R/N bps
QAM
Modulator
S
x
cos(2pfNt)

OFDM efficiently implemented using FFTs

OFDM challenges are PAPR, timing and
frequency offset, and fading across subcarriers
Spread Spectrum

In DSSS, bit sequence modulated by chip sequence
s(t)
S(f)
sc(t)
Sc(f)
S(f)*Sc(f)
1/Tb
Tc




1/Tc
Spreads bandwidth by large factor (K)
Despread by multiplying by sc(t) again (sc(t)=1)
Mitigates ISI and narrowband interference2


Tb=KTc
ISI mitigation a function of code autocorrelation
Must synchronize to incoming signal
RAKE receiver used to combine multiple paths
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
Lecture 3
Future Wireless Systems
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.
R3
x
h3(t)
x
h22(t)
x
x
h1(t)
h21(t)
R2
R1
Uplink and Downlink typically duplexed in time or frequency
Bandwidth Sharing
Code Space


Frequency Division
Time Division
Time
Code Space
Frequency
Time
Frequency

Code Division

Time
Multiuser Detection
Frequency

Space (MIMO Systems)

Hybrid Schemes
7C29822.033-Cimini-9/97
Code Space
Multiple Access SS

Interference between users mitigated by code
cross correlation
Tb
xˆ (t )   1s1 (t ) sc21 (t ) cos 2 (2pf c t )   2 s2 (t   ) sc 2 (t   ) sc1 (t ) cos( 2pf c t ) cos( 2pf c (t   )) dt
0
Tb
 .51d1  .5 2 d 2  sc1 (t ) sc 2 (t )dt  .5d1  .5d 2 cos( 2pf c ) 12 ( )
0


In downlink, signal and interference have
same received power
In uplink, “close” users drown out “far” users
(near-far problem)
2
1
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
-
Signal 1
A/D
A/D
A/D
=
Signal 1
Demod
A/D
A/D
Iterative
Multiuser
Detection
Signal 2
Signal 2
Demod
-
=
Why Not Ubiquitous Today? Power and A/D Precision
RANDOM ACCESS TECHNIQUES
Random Access

Dedicated channels wasteful for data


use statistical multiplexing
Techniques


Aloha
Carrier sensing





Reservation protocols
PRMA
Retransmissions used for corrupted data
Poor throughput and delay characteristics under
heavy loading

7C29822.038-Cimini-9/97
Collision detection or avoidance
Hybrid methods
Multiuser Channel Capacity
Fundamental Limit on Data Rates
Capacity: The set of simultaneously achievable rates {R1,…,Rn}
R3
R1
R2
R3
R2
R1

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
Multiuser Fading Channel Capacity

Ergodic (Shannon) capacity: maximum long-term rates
averaged over the fading process.




Zero-outage (delay-limited*) capacity: maximum rate
that can be maintained in all fading states.



Shannon capacity applied directly to fading channels.
Delay depends on channel variations.
Transmission rate varies with channel quality.
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
w1k
xk
H1(w)
H2(w)


y1k   h1i xk i w1k
i 1
m
y2 k   h2i xk i w2 k
i 1
ISI introduces memory into the channel
The optimal coding strategy decomposes the
channel into parallel broadcast channels


w2k
m
Superposition coding is applied to each subchannel.
Power must be optimized across subchannels
and between users in each subchannel.
Broadcast MIMO Channel
(r1  t )
n1
y1  H1x  n1
H1
x
(r2  t )
H2


n2
Non-degraded
broadcast channel
y 2  H2 x  n 2
MIMO MAC capacity easy to find
MIMO BC channel capacity obtained
using dirty paper coding and duality with
MIMO MAC
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
Due to its scarcity, spectrum is reused
In licensed bands
and unlicensed bands
BS
Cellular, Wimax
Wifi, BT, UWB,…
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
 Multiuser detection
 MIMO
 Dynamic resource allocation
8C32810.44-Cimini-7/98
Interference: Friend or Foe?

If treated as noise: Foe
P
SNR 
NI
Increases BER, reduces capacity

If decodable: Neither friend nor foe
Multiuser detection can
completely remove interference
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

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
Femto
Coop
MIMO
How should cellular
systems be designed?
Relay
DAS

Will gains in practice be
big or incremental; in
capacity or coverage?
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.
Cellular System Capacity

Shannon Capacity



User Capacity




Shannon capacity does no incorporate reuse distance.
Some results for TDMA systems with joint base station
processing
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
BASE
STATION
A=.25D2p =



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.
Average Area Spectral
Efficiency
[Bps/Hz/Km2]
ASE vs. Cell Radius
fc=2 GHz
1
10
D=4R
D=6R
D=8R
0
10
0.1
0.2
0.3
0.4
0.5
0.6
Cell Radius R [Km]
0.7
0.8
0.9
1
Improving Capacity

Interference averaging


Interference cancellation


Multiuser detection
Interference reduction




WCDMA
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

Resources:







Channels
Bandwidth
Power
Rate
Base stations
Access
BASE
STATION
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

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

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

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

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)

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

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.
S

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
D
Is a capacity region all we
need to design networks?
Yes, if the application and network design can be decoupled
Application metric: f(C,D,E): (C*,D*,E*)=arg max f(C,D,E)
Capacity
(C*,D*,E*)
Delay
Energy
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
3
 Optimal MAC via centralized scheduling
2
 Optimal routing


Yields performance bounds


Evaluate existing protocols
1
Develop new protocols
4
5
Achievable Rates
Achievable rate
vectors achieved
by time division

A matrix R belongs to the capacity region if there are rate
matrices R1, R2, R3 ,…, Rn such that
R  i 1i Ri ;
n

Capacity region
is convex hull of
all rate matrices



1
;


0
i
i
i 1
n
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
Capacity region is 30-dimensional
Capacity Region Slice
(6 Node Network)
Rij  0, ij  12,34, i  j
Multiple
hops
Spatial
reuse
SIC
(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.
Extensions:
- Capacity vs. network size
- Capacity vs. topology
- Fading and mobility
- Multihop cellular
Ad-Hoc Network
Design 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

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
Hidden
Terminal
Exposed
Terminal
1

2
3
4
5
802.11 uses four-way handshake

Creates inefficiencies, especially in multihop setting
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

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.

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
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

Flooding


Point-to-point routing



Routes follow a sequence of links
Connection-oriented or connectionless
Table-driven


Broadcast packet to all neighbors
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.
Interference: Friend or Foe?
If exploited via
cooperation and cognition
Friend
Especially in a network setting
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
TX1
RX1
Y4=X1+X2+X3+Z4
X1
relay
Y3=X1+X2+Z3
TX2

X3= f(Y3)
X2
Analog network coding
Y5=X1+X2+X3+Z5
RX2
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
In fact, it can achieve capacity
P1
S
P3
Ps
D
P2
•
P4
For large powers Ps, P1, P2, analog network coding
approaches capacity
How to use Feedback in Wireless
Networks





Output feedback
Noisy/Compressed
CSI
Acknowledgements
Network/traffic information
Something else
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
ARQ
H1
ARQ
Multiplexing
H2
Error Prone




Beamforming
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

Fixed ARQ: fixed window size


N
Maximum allowed ARQ round for ith hop
Li
satisfies
Adaptive ARQ: adaptive window size

L
i 1
i
L
Fixed Block Length (FBL) (block-based feedback, easy synchronization)
Block 1
ARQ round 1
Block 1
ARQ round 2
Block 1
ARQ round 3
Block 2
ARQ round 1
Block 2
ARQ round 2
Receiver has enough
Information to decode

Variable Block Length (VBL) (real time feedback)
Block 1
ARQ round 1
Block 1
ARQ round 2
Block 1
round 3
Block 2
ARQ round 1
Receiver has enough
Information to decode
Block 2
ARQ round 2
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

Accumlated
Information
(FBL)
Short-Term Static Channel
re
0
4 t1
8
12 t2
Channel Use
64
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
B
A

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
Cross-layer protocol design
for real-time media
Loss-resilient
source coding
and packetization
Application layer
Rate-distortion preamble
Traffic flows
Congestion-distortion
optimized
scheduling
Transport layer
Congestion-distortion
optimized
routing
Network layer
Capacity
assignment
for multiple service
classes
Link capacities
MAC layer
Link state information
Joint with T. Yoo, E. Setton,
X. Zhu, and B. Girod
Adaptive
link layer
techniques
Link layer
Video streaming performance
s
5 dB
3-fold increase
100
1000 (logarithmic scale)
Network Metrics
Network Fundamental Limits
Fundamental Limits
of Wireless Systems
Capacity
Delay
(DARPA Challenge Program)
C
B
Outage
A
D
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
Cross-layer Design and
End-to-end Performance
Capacity
(C*,D*,R*)
Delay
Robustness
Application Metrics
Approaches to Network Optimization*
Network
Optimization
Dynamic
Programming
Network Utility
Maximization
Distributed
Optimization
Game
Theory
State Space
Reduction
Wireless NUM
Multiperiod NUM
Distributed
Algorithms
Mechanism Design
Stackelberg Games
Nash Equilibrium
*Much prior work is for wired/static networks
Dynamic Programming (DP)

Simplifies a complex problem by breaking it into
simpler subproblems in recursive manner.




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

Maximizes a network utility function

Assumes



U1(r1)
Steady state
Reliable links
Fixed link capacities
U2(r2)
Ri
Rj
Un(rn)

Dynamics are only in the queues
max
U
flow k
k
(rk )
s.t Ar  R
routing
Fixed link capacity
Optimization is Centralized
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
$$$
and Expensive
Cognitive Radio Paradigms

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
Knowledge
and
Complexity
Underlay Systems

Cognitive radios determine the interference their
transmission causes to noncognitive nodes

Transmit if interference below a given threshold
IP
NCR
NCR

CR
CR
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

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

Cognitive user has knowledge of other
user’s message and/or encoding strategy
 Used
to help noncognitive transmission
 Used to presubtract noncognitive interference
CR
NCR
RX1
RX2
Performance Gains
from Cognitive Encoding
outer bound
our scheme
prior schemes
Only the CR
transmits
Broadcast Channel with
Cognitive Relays (BCCR)
Cognitive Relay 1
data
Source
Cognitive Relay 2

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
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

Each node can only send a finite number of bits.




Short-range networks must consider transmit,
circuit, and processing energy.



Transmit energy minimized by maximizing bit time
Circuit energy consumption increases with bit time
Introduces a delay versus energy tradeoff for each bit
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.
Cross-Layer Tradeoffs
under Energy Constraints

Hardware



Link



High-level modulation costs transmit energy but saves
circuit energy (shorter transmission time)
Coding costs circuit energy but saves transmit energy
Access



All nodes have transmit, sleep, and transient modes
Each node can only send a finite number of bits
Power control impacts connectivity and interference
Adaptive modulation adds another degree of freedom
Routing:

Circuit energy costs can preclude multihop routing
Modulation Optimization
Tx
Rx
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 T and bit
error probability Pb.

Square-law path loss for AWGN channel
Et  Er Gd ,
(4pd )
Gd 
2
Gl
2
Multi-Mode Operation
Transmit, Sleep, and Transient
 Deadline
 Total
T: T  Ton  Tsp  Ttr
Energy:
E  Eon  Esp  Etr
Transmit
Circuit
( E sp  0, Etr  2 PsynTtr )
Transient Energy
 (1   ) PtTon  PcTon  2 PsynTtr
where  is the amplifier efficiency and
Pc  2 Pmix  2 Psyn  PLNA  PIFA  Pfil  PDSP ,
Energy Consumption:
Uncoded

Two Components
 Transmission
Energy: Decreases with Ton & B.
 Circuit Energy: Increases with Ton

Minimizing Energy Consumption

Finding the optimal pair ( B, Ton)

For MQAM, find optimal constellation size (b=log2M)
Total Energy (MQAM)
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

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
Reference system has bk=3 (coded) or 2 (uncoded)
90% savings
at 1 meter.
Minimum Energy Routing
0.1
Red: hub node
Green: relay/source
0.085
4
(0,0)
3
0.185
(5,0)
0.515
2
(10,0)
0.115
1
(15,0)
R1  60 pps
R2  80 pps
R3  20 pps
• Optimal routing uses single and multiple hops
• Link adaptation yields additional 70% energy savings
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
How should cellular systems be designed to conserve
energy at both the mobile and base 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 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

Wireless channels are a difficult and capacitylimited 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
Automated Vehicles
- Cars
- UAVs
- Insect flyers
- Different design principles


Control requires fast, accurate, and reliable feedback.
Networks introduce delay and loss for 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












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

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



