Research Projects in Wireless

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21 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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CS Colloquium



Research Projects in Wireless
Communication Networks

Xin Liu


Computer Sciences Department


University of California, Davis

2

Wireless Networks


Cellular systems


1G: analog


2G: digital


3G: data


Wireless LAN


IEEE 802.11


Ad
-
hoc wireless networks


Military, emergency, etc.


Wireless Sensor networks

3

Research Topics


Digital signal processing


Smart antenna


Scheduling


Power management


Topology management


Mobility management


Routing (for ad hoc networks)


……



4

Unique Features


Motivated by some unique features in
wireless communication systems
:



Scarce

radio resource


Limited power


Timing
-
varying
channel conditions


Shared media




5

Scarce Radio Resource


Wireline networks


High bandwidth and reliable channel


Core router: Gbps
-
Tbps



Wireless systems


Limited nature resource (radio frequency)


Capacity is limited by available frequency


3G data rate: up to 2Mbps


IEEE 802.11b: up to 11Mbps


Requirement:
spectrum efficiency

6

Power


Battery power is still the bottleneck


Important for hand
-
held equipment


Critical for wireless sensor networks


What can we do?


Power management
---

use the available
power efficiently


7

Channel Conditions



Decides transmission performance



Determined by


Strength of desired signal


Noise level


Interference from other transmissions


Background noise


Time
-
varying and location
-
dependent.

8

Interference and Noise

Noise
Interference
Interference
Interference
Desired
Signal
9

Propagation Environment

Shadowing
Multi-path Fading
Strong
Weak
Path Loss
10

Time
-
varying Channel Conditions


Due to users’ mobility and variability in the
propagation environment, both desired signal and
interference are
time
-
varying and location
-
dependent



A measure of channel quality
:


SINR

(
Signal to Interference plus Noise Ratio
)


11

Illustration of Channel Conditions

Based on Lee’s path loss model, log
-
normal shadowing, and Raleigh fading

12

Performance vs. Channel Condition


Voice users: better voice quality at high SINR
for a fixed transmission rate;


Data users: higher transmission rate at high
SINR for a given bit error rate;


Adaptation techniques are specified in 3G
standards.


TDMA: adaptive coding and modulation


CDMA: variable spreading and coding



13

Shared Media


Shared media: everyone can hear each other



Can hurt


Can help


Multi
-
user diversity

14

Interference

Noise
Interference
Interference
Interference
Desired
Signal
15

Helper

Relay:

Coherent Relay:

16

Multi
-
user Diversity


Different users see different channels at different time

17

Opportunistic scheduling




Motivation:


Spectrum efficiency


Time
-
varying channel
conditions


Multi
-
user diversity

Question
: how to handle channel variability?

18

Opportunism


Traditional design: point to point


Channel variability: source of unreliability



Opportunism: embrace channel variability


Multiple users share resource


Exploits favorable channel conditions.


19

Myopic Opportunism



Greedy algorithm: best user to transmit


Good throughput


Unfairness



Starvation

20

Opportunistic Scheduling


Basic idea: schedule users in a way that
exploits variability in channel conditions.


Opportunistic: choose a user to transmit when
its channel condition is good.


Fairness/QoS requirements: opportunism
cannot be too greedy.


Each scheduling decision depends on


channel conditions


fairness or QoS requirements.

21

System Model


Time
-
slotted systems





Each user has a certain requirement.



TDMA or time
-
slotted CDMA systems (e.g., IS
-
856, known as Qualcomm HDR)


Both uplink and downlink.


Time
22

Overview

Estimate
Utility
Values
Apply
Scheduling
Polity
Update
Parameters
Measure
Channel
Conditions
i
U
i
V
23

Performance Measure


Based on utility value


Reflects channel condition.


U
i
k

: utility value of user
i

at time
k

.


If time slot
k

is assigned to user
i
, user

i

will receive a
utility value of
U
i
k
.


Measures the
worth

of the time slot to user
i
.


Examples of utility:


Throughput


Throughput


cost of power consumption.


Utility values are comparable and additive.



24

Utility Values


{
U
i
k
,
k=1,2,3…
} is a
stochastic process.


25

A Framework for Opportunistic Scheduling



Objective
: Maximize the sum of all users’
utility values while satisfying the QoS
requirements of users.



Scheduling decision depends on:


Utility values
(reflecting channel conditions)


QoS/fairness requirements.



26

A Case Study: Temporal Fairness Scheduling


?
)
(

),
,...
(
Given
}
,...
2
,
1
{
)
(

policy

Scheduling

:
users

of
Number

:
1

0,

:

1
1






U
U
Q
U
U
N
Q
Q
N
r
r
r
k
N
k
N
i
i
i
i

time.
the
of
portion


gets
user
each

:
fairness

Temporal
i
r
27

Objective


Maximize average system utility subject to the
fairness constraints
r
i
.


System utility:












28

Scheduling Problem Formulation


Optimal scheduling problem




where


is the set of all policies.



No channel model assumed.


No assumption on utility functions.


General distributions of .


Users’ utility values can be correlated.




29

An Optimal Scheduling Policy


















Choose the ``relatively
-
best'' user to transmit.



v
i
*

: “off
-
sets” used to achieve the fairness requirement.


30

Property









Improves performance for
all
.



Gain depends on channel variability.



A certain level of average utility guarantee for
each user
.



31

Scheduling Gain



Opportunistic scheduling gain increases with


channel independence (across users)


channel variability (over time)


number of users.



32

System Performance

1.01]

0.99

1.00

1.01

1.01

0.99

1.00

99
.
0
[

:
ratio

Fairness
33

Joint Scheduling and Power Allocation


Joint scheduling and power allocation:
intercell
-
interference management.


Interference limits the system capacity.


Power allocation: interference management.


Opportunistic scheduling: multi
-
user diversity.


Two decision variables:


which user


how much power.


34

Objectives


Objective 1:


minimize total transmission power


guarantee a minimum
-
utility for each user.



Objective 2:


maximize net utility


tradeoff between throughput and transmission
power (interference to other cells).


guarantee a minimum
-
utility for each user.



35

A To
-
do List


May induce variability if needed.


Can be used in distributed manners.


Many to many


Large sensor networks


Real
-
time traffic


Multi
-
carrier systems


A different design aspect


Problems in information theory


Future wireless systems: exploit opportunistic
methods

(IS
-
856).








36

Wireless Sensor Network Potential


Micro
-
sensors, on
-
board processing, and
wireless interfaces all
feasible at very small
scale


can monitor
phenomena “up
close”


Will enable spatially
and temporally dense
environmental
monitoring


will reveal previously
unobservable
phenomena

Seismic Structure
response

Contaminant
Transport

Marine
Microorganisms

Ecosystems,
Biocomplexity

Ref: based on slides by D. Estrin

37

Enabling Technologies

Embedded

Networked

Sensing

Control system w/

Small form factor

Untethered nodes


Exploit

collaborative

Sensing, action

Tightly coupled to physical world

Exploit spatially and temporally dense, in situ,
sensing and actuation

Ref: based on slides by D. Estrin

38

Challenges

By no means this is a complete list:


Self
-
configured


Random deployment of sensor networks


Long
-
lived sensor systems


Sensors have very limited battery power


Reliability


Harsh environment


Unreliable sensors


Cost


Scalability


Massive data


Compression and aggregation


Time synchronization, data query, localization, storage, etc.

39

A Random Deployed Sensor Network

GATEWAY

MAIN SERVER

CONTROL

CENTER

40

Topology control


Many
-
to
-
one communication


Unbalanced load


Uneven power consumption


“Important” nodes in the route die quickly


Possible approaches


More power at closer nodes


Data compression and aggregation

41

The Problem


Objective
: minimize # of sensors needed to build a
sensor network that covers a given area for a
certain amount of time.


Communication consumes a lot of power





R: rate, D: distance between transmitter and receiver


Put nodes with heavier load closer










5
2
,





RD
P
1
84
.
0
,
1
,
2
,
1
4
2
2
4
1
1
2
1
2
1






D
R
D
R
D
D
R
R
42

Approach


Non
-
trivial: sensor placement, routing, power management





To consider:


Linear and planar network


Random and non
-
random topology


Other power consumption


Approaches:


Understand
fundamental

principles


Build
practical

solutions








P1

P2

43

Coverage and Connectivity

44

Coverage and Connectivity


Traditional work: full coverage and
connectivity, K
-
coverage, etc.



Our
objective
: Cover and connect a large
portion of the area


Quantify the size of uncovered area


How many nodes needed


What is the density needed

45

Cost and Reliability


Layered structure


More expensive nodes with more functionality


Objective
: minimize the total cost, including
different types (cost) of nodes, while
maintaining the desired performance


Reliability



important, especially for large scale network



nodes damages, out of power, etc.

46

Parking Lot Patrol Problem


Sensors on parking meters


Build a wireless sensor network to report
illegal parking


Patrolman to find the reported events



Applications:


Border patrol


Speeding monitoring

47

What Do We Stand?


History: a successful story, an industry of $$$$$$


Current: Policy re
-
examination underway


Increased unlicensed spectrum allocation


Exploration of “underlays”, e.g., UWB


Exploration of “overlays”, e.g., opportunistic use of
committed but unused bandwidth


Future:


more spectrum


better ratio equipment, DSP technologies, longer
battery life


Better networks


Cool applications



48