Research Projects in Wireless
Communication Networks
Xin Liu
Computer Sciences Department
University of California, Davis
CS Colloquium
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
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Research Topics
 Digital signal processing
 Smart antenna
 Scheduling
 Power management
 Topology management
 Mobility management
 Routing (for ad hoc networks)
 ……
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Unique Features
Motivated by some unique features in
wireless communication systems:
 Scarce radio resource
 Limited power
 Timing-varying channel conditions
 Shared media
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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
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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
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Channel Conditions
 Decides transmission performance
 Determined by


Strength of desired signal
Noise level



Interference from other transmissions
Background noise
Time-varying and location-dependent.
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Interference and Noise
Noise
Interference
Interference
Desired
Signal
Interference
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Propagation Environment
Weak
Shadowing
Strong
Path Loss
Multi-path Fading
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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)
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Illustration of Channel Conditions
Based on Lee’s path loss model, log-normal shadowing, and Raleigh fading
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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
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Shared Media
 Shared media: everyone can hear each other



Can hurt
Can help
Multi-user diversity
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Interference
Noise
Interference
Interference
Desired
Signal
Interference
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Helper
Coherent Relay:
Relay:
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Multi-user Diversity
Different users see different channels at different time
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Opportunistic scheduling
 Motivation:
 Spectrum efficiency
 Time-varying channel
conditions
 Multi-user diversity
Question: how to handle channel variability?
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Opportunism
 Traditional design: point to point

Channel variability: source of unreliability
 Opportunism: embrace channel variability
Multiple users share resource
 Exploits favorable channel conditions.

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Myopic Opportunism
 Greedy algorithm: best user to transmit


Good throughput
Unfairness
Starvation
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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.
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System Model
 Time-slotted systems
Time
 Each user has a certain requirement.
 TDMA or time-slotted CDMA systems (e.g., IS-
856, known as Qualcomm HDR)
 Both uplink and downlink.
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Overview
Measure
Channel
Conditions
Estimate
Utility
Values
Vi
Ui
Apply
Scheduling
Polity
Update
Parameters
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Performance Measure
 Based on utility value
 Reflects channel condition.
 Uik : 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 Uik.
 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.
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Utility Values
 {Uik, k=1,2,3…} is a stochastic process.
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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.
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A Case Study: Temporal Fairness Scheduling
Temporalfairness: each user gets ri portionof thetime.
ri : ri  0, i 1 ri  1
N
N : Number of users
Q : Scheduling policy
Q(U) {1,2,...N }
Given (U1k ,...U Nk ), Q(U)  ?
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Objective
 Maximize average system utility subject to the
fairness constraints ri.
 System utility:
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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.
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An Optimal Scheduling Policy

 Choose the ``relatively-best'' user to transmit.
 vi* : “off-sets” used to achieve the fairness requirement.
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Property

 Improves performance for all.
 Gain depends on channel variability.
 A certain level of average utility guarantee for
each user.
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Scheduling Gain
 Opportunistic scheduling gain increases with

channel independence (across users)

channel variability (over time)

number of users.
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System Performance
Fairness ratio: [0.99 1.00 0.99 1.01 1.01 1.00 0.99 1.01]
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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.
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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.
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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).
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Wireless Sensor Network Potential
 Micro-sensors, on-
Seismic Structure
response
Marine
Microorganisms
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
Contaminant
Transport
Ecosystems,
Biocomplexity
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Ref: based on slides by D. Estrin
Enabling Technologies
Embedded
Networked
Exploit
collaborative
Sensing, action
Control system w/
Small form factor
Untethered nodes
Sensing
Tightly coupled to physical world
Exploit spatially and temporally dense, in situ, sensing and actuation
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Ref: based on slides by D. Estrin
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.
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A Random Deployed Sensor Network
GATEWAY
MAIN SERVER
CONTROL
CENTER
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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
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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
P  RD ,2    5
R: rate, D: distance between transmitter and receiver
 Put nodes with heavier load closer
R1  1, R2  2, D1  1, D2  0.84
R1D14  R2 D24  1
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Approach
 Non-trivial: sensor placement, routing, power management
P1
P2
 To consider:
Linear and planar network
 Random and non-random topology
 Other power consumption
 Approaches:
 Understand fundamental principles
 Build practical solutions

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Coverage and Connectivity
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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
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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.
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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
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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
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