Cross-Layer Design for Spectrum- and
Energy-Efficient Wireless Networks
Guowang Miao
miao@FreeLinguist.com
miao@FreeLinguist.com
Presenter Introduction
l Founder ofFreeLinguist
®
, a cloud platform for you to connect
with native linguists for quality language services, such as
translation, editing, or writing services.
l Expert in communications and networking
l Well known for his original contributions in building a set of
fundamental energy-efficient communications theories, which
are widely accepted nowadays.
l Inventor of energy-efficient scheduling and capacity-
approaching transmission (United States Patent7782829).
l Author of the graduate textbook entitled Fundamentals of
Mobile Data Networks (Cambridge University Press)
l Author of the book entitled Energy and Spectrum Efficient
Wireless Network Design (Cambridge University Press).
l Fruitful inventor with many granted patents, some of which
have been adopted as essential in 4G standards.
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Outline
l Introduction
l Wireless Channel Properties
l Basic Concepts
l Spectrum Efficient Design
l Energy Efficient Design
l Energy-Efficient Mobile Access Networks: A Tradeoff
Perspective
l Conclusions and References
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1. INTRODUCTION
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Ancient Wireless Communications
Invasion or no invasion?
(1/0)
WirelessCommunicationsat400–790THz(visiblelight)
Energyoffirewood:16.2megajoules/kg
Extremelyspectrumandenergyinefficient
Yet NEEDED
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Growing Need of Energy-Efficient Design
in Mobile Broadband Access Networks
l Currently, 2% of world energy consumption due to mobile communications
•Radio access network consumes 80% energy of the mobile communications
(Ericsson)
l Mobile data traffic is exploding
•AT&T mobile data traffic increases by 80x
after 2007 (Iphone debut).
•Cisco expects 26x further data traffic in 2015.
•Extrapolating Cisco traffic prediction curve,
300x data traffic in 2020.
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Growing Need of Energy-Efficient Design
in Mobile Broadband Access Networks
l Price paid for this enormous growth
• Doubling of the power consumption in cellular
networks (base stations and core network) every 4-5
years.
l Energy consumption has dramatic environmental impact
• Vodafone: total annual emission of CO
2
in 2007/8:
1.45 million tonnes
• More expected in the future
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Need of Energy Efficiency in Mobile
Devices
l Mobile devices are usually battery powered
Growingdemand
ofmobiletraffic
Exponentialgrowth
ofbatteryconsumption
(150% every two years)
Slowdevelopment
ofbattery
(10% every two years)
an exponentially increasing gap
between the energy demand and supply
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Slow advance in battery
technology /energy
industry critically limit
energy availability
Significance of
energy efficiency
Growing demand for
ubiquitous wideband
wireless applications
Spectrum is a
natural resource
that cannot be
replenished
Significance of
spectral efficiency
Affected by all layers of system design
Cross-layer optimization to exploit interactions between different
layers to fully improve both spectral and energy efficiency
Critical Demand of SE and EE
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Motivation for Cross-Layer Design
l Traditional Open System Interconnection (OSI) model
• Divide communication systems into layers
l Cons of separate design
• Information lost between layers
l Necessity of cross-layer design, especially for wireless
communications
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Revolutionary Thinking Needed
RadioResourceManagementofwirelessnetworks
allocateradioresources
modulation
/coding
power
Celldeployment
T/F/S/C-domain
channelallocation
statically
dynamically
datarate
…
assuranceofqualityofservice(QoS)
formobileusers(rate,delay,outage,coverage,etc.)
Energy IGNORED!
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Revolutionary Thinking Needed
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RadioResourceManagementofwirelessnetworks
allocateradioresources
modulation
/coding
power
Celldeployment
T/F/S/C-domain
channelallocation
statically
dynamically
datarate
…
assuranceofqualityofservice(QoS)
formobileusers(rate,delay,outage,coverage,etc.)
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2. WIRELESS CHANNEL
PROPERTIES
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Propagation and Mobility
l Propagation
• Fading, shadowing
• Reflection at large obstacles, refraction, scattering at small obstacles,
diffraction at edges
• Signal takes several paths to the receiver
l Mobility
• Variation of channel characteristics
signalatsender
signalatreceiver
LOSpulses
multipath
pulses
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signalsatsender
signalatreceiver
LOSpulses
multipath
pulses
Fundamental Problem I
HOWtoexploitwirelesschannel
propertiestoenhancebothspectral
andenergyefficiencyforsingle-link
communications?
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Multiple User Perspective
l Wireless channels
• Broadcast of all signals
• Due to frequency reuse, different users affect each other
through
• Interference
Forms of Interference
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Fundamental Problem II
HOWtoexploitinterference
propertiestoenhancebothspectral
andenergyefficiencyfor
thewholenetwork?
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3. BASIC CONCEPTS
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Physical Layer
l Physical (PHY) layer
• Deal with challenging wireless medium
• Traditionally
• Operate on a fixed set of operating points
• Fixed transmit power
• Fixed modulation and coding scheme (MCS)
• Pro:
• Simplicity
• Con:
• Channel capacity not fully exploited (low SE)
• Excessive energy consumption (low EE )
• Link adaptation: adapt to QoS and environments
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Multi-User Perspective
l Typically more than one user in the network
l Multiple users need to share wireless medium
l Medium access control: share wireless channel efficiently
• allocate wireless resources to users on demand
• multiplex/separate transmissions of different users
• avoid interference and collisions
• network-wide flexibility, efficiency, and fairness of resource
sharing
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Wireless Resources
l Orthogonal resources in four dimensions
• Space ( s )
• Time ( t )
• Frequency ( f )
• Code ( c )
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Wireless Resources
l Non-orthogonal resources
• Power
• Users with completely different ( s, t, f, c )
• Independent communications
• Two or more users with overlapping ( s, t, f, c )
• Interact with each other through mutual interference
• Controlled by power
• Examples:
• Inter-cell interference in cellular networks (s
overlap)
• Inter-symbol interference (t overlap)
• Inter-channel interference (f overlap)
• Energy consumption
B
CA
D
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MAC Classification
l MAC determines resource allocation
• Centralized and distributed MAC
l Centralized MAC
• Central controller schedules resources of all users
• Examples: data channels in cellular networks
• Pros: high performance, easy control of resource
assignments …
• Cons: high complexity, poor scalability …
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Distributed MAC
l Distributed MAC
• No central scheduler
• Individual users decide resources independently
• Use a certain local medium access policy
• Examples: Aloha, CSMA/CA, 802.11 DCF
• Low-complexity, high scalability
• Protocol design determines how network performs
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Design Rules of Distributed MAC
l Traditional rules of distributed MAC design
• Removal of Idle State
• Some users have data to transmit but decide not to while
channel is idle
• Waste of channel capacity
• Happen frequently with light network load
• Removal of Collision State
• With collision, packet transmission fails
• Waste of both channel capacity and user energy
• Happen frequently with high network load
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l Exploit wireless medium properties
• Optimize point to point communication links
• Allocate resources to share wireless medium fairly and efficiently
l Enhance spectral and energy efficiency through joint optimization of
• Physical layer: power control, adaptive modulation and coding, etc., i.e.
link adaptation
• Medium access control (MAC) layer: control the medium access in a
distributed or centralized way
based on knowledge of channel state information.
CSI can be obtained through reciprocity in TDD systems or independent
feedback channels.
Focus
Time
User1
SNR
User2
User3
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4. CROSS-LAYER OPTIMIZATION
FOR SPECTRAL EFFICIENCY
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Multiuser Diversity in A Single
Channel System
l Exploit channel property
• Schedule the user with good channel quality
l Techniques required to exploit multiuser diversity:
• Channel state information feedback
• Adaptive modulation and coding
• Fast channel-aware packet scheduling
l Diversity gain increases with the number of users
Time
User1
SNR
User2
User3
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System Model
l
1
Q
1
(t) r
1
(t)
l
2
Q
2
(t) r
2
(t)
l
M
Q
M
(t) r
M
(t)
OFDM
DSAor/andAPA
QueueInformation
ChannelInformation
User1
User2
UserM
§ RateAdaptation(RA)
§ DynamicSubcarrier
assignment(DSA)
§ AdaptivePowerAllocation
(APA)
f
SNR
2
(f)
1
(f)
User1
User2
f
Power
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l Utility: the level of satisfaction that a user gets from using some resources
(economics concept)
l Utility functions are determined by applications
l Optimization
• Objective: to maximize the sum of utilities in the system
• Subject to: the degrees of freedom of resource allocation
• DSA: Orthogonality of subcarriers
• APA: Maximum total transmit power
• Pros:
• Application-oriented resource allocation
• Flexibility
• Fairness & QoS provisioning
Cross-Layer Optimization Based on
Utility Functions
Utility
Rate
Utility
Rate
Delay
Utility
Voice
Best-effort
Time-sensitive
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Scope
Objective: To maximize
the total utility
Utility functions w. r. t.
average delays
Utility functions w. r. t.
instantaneous data rates
Utility functions w. r. t.
average data rates
Best-effort traffic
DSA and APA
optimization
algorithms
Fairness
Stability
QoS differentiation for heterogeneous traffic
Utility functions w.
r. t. data rates
Channel-aware
scheduling
Channel- and
queue-aware
scheduling
Time-sensitive traffic
w.r.t. : with respect to
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Scope
Objective: To maximize
the total utility
Utility functions w. r. t.
average delays
Utility functions w. r. t.
instantaneous data rates
Utility functions w. r. t.
average data rates
Best-effort traffic
DSA and APA
optimization
algorithms
Fairness
Stability
Utility functions w.
r. t. data rates
Channel-aware
scheduling
Channel- and
queue-aware
scheduling
Time-sensitive traffic
w.r.t. : with respect to
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Utility-Based Dynamic Subcarrier
Assignment
l Sorting-Search Algorithm for
DSA
• Complexity is about
M
2
Klog
2
(K)
• Nearly optimal
l Nonlinear combinatorial
optimization problem
with computational
complexity
M
K
.
•
M
: the number of users
•
K
: the number of
subcarriers
r
2
r
1
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Utility-Based Adaptive Power Allocation
l Multi-level water-filling
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Simulation Results
5dB
10dB
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Scope of Research
Objective: To maximize
the total utility
Utility functions w. r. t.
average delays
Utility functions w. r. t.
instantaneous data rates
Utility functions w. r. t.
average data rates
Best-effort traffic
DSA and APA
optimization
algorithms
Fairness
Stability
Utility functions w.
r. t. data rates
Channel-aware
scheduling
Channel- and
queue-aware
scheduling
Time-sensitive traffic
w.r.t. : with respect to
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Channel-Aware Scheduling Using
Rate-Based Utility Functions
l Users care about the average data rate during 1 to 2 seconds, not the
instantaneous one.
• Average data rate:
• Optimization objective:
l The solution for DSA is very simple.
l A utility function is associated with a kind of fairness
l Multichannel proportional fair scheduling
Priority
Achievableinstantaneousrate
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Scope of Research
Objective: To maximize
the total utility
Utility functions w. r. t.
average delays
Utility functions w. r. t.
instantaneous data rates
Utility functions w. r. t.
average data rates
Best-effort traffic
DSA and APA
optimization
algorithms
Fairness
Stability
QoS differentiation for heterogeneous traffic
Utility functions w.
r. t. data rates
Channel-aware
scheduling
Channel- and
queue-aware
scheduling
Time-sensitive traffic
w.r.t. : with respect to
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l Utility functions w.r.t. average waiting time
• Average waiting time,
W
Satisfaction level,
U
(
W
)
l Optimization problem of MDU scheduling
• Objective: to maximize the total utility with respect to the predicted
average waiting time at each time slot
l Joint channel- and queue-aware scheduling
• Awareness of channel conditions improve network capacity
• Awareness of queue information ensure QoS
Max-Delay-Utility (MDU) Scheduling
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Stability Region
l Ergodic capacity region vs. Stability region
• Ergodic capacity region consists of all (long-term) average data rate vectors
under all possible resource allocation schemes, given the statistics of the
channels
• Stability region of a scheduling policy is defined to be the set of all possible
arrival rate vectors for which the system is stable under the policy.
• Stability region Ergodic capacity region
• Maximum stability region: the largest stability region that can be achieved by
some scheduling schemes ( Ergodic capacity region)
• MDU has maximum stability region.
0
λ
1
λ
2
r
1
r
2
0
AMC,NoDSA
AMC,DSA
NoAMC,NoDSA
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Recap
l Centralized Cross-Layer Optimization for SE
l Intelligent resource allocation by exploiting channel
and queue information
• Efficient resource allocation algorithms
l Diverse QoS provisioning offered by utility functions
l Significant performance gains: multiuser diversity,
frequency diversity, and time diversity
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Distributed MAC - Traditional Slotted Aloha
l Time divided into slots of equal size
l Users wait until beginning of slot to transmit
l If collision: retransmit with probability p until success.
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Access Modeling
Controlthe
contentionprobability
Fading on one
subchannel
l Cons of separate design: 1. transmit a frame when channel is in deep
fading; 2. May not transmit but channel is in good condition
l With cross-layer design
• Transmit a frame when channel gain is above a threshold
• Randomize transmission since channel varies randomly
Findoptimalvalues
tomaxnetwork
performance
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Opportunistic Random Access –
Infrastructure Networks
l Exploits variation inherent in wireless channels to increase
network throughput.
l Each user knows its own channel state
l [Qin 03] Each user transmits only if its channel power gain is
above a pre-determined threshold that is chosen to maximize
the probability of successful transmissions.
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Channel-Aware Aloha
l N users in the system to send data
l Each user knows the distribution of its channel gain
l Each user chooses a threshold
Ho
and sends data only if the
channel gain is above
Ho
(binary scheduling).
Contentionprobability:1/N
AsymptoticallyoptimalinN
Asymptoticallyachieve1/eofthe
centralizedsystem’sthroughput
[YU06]:binaryschedulingmaximizes
thesum-throughput
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Opportunistic Random Access – Ad Hoc
Networks
lUbiquitouscommunicationscomplicatenetworktopology
lNocentralschedulerforgoodscalability
lAgenericsolution
0.1
10.1
1.8
1.3
0.3
0.9
2.6
26.1
16.1
Channel
gain
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Cross-Layer Design Objective
l Objective:
• Consider both overall network throughput and fairness
• Find optimal threshold configuration and adaptive modulation
and coding (AMC)
l Cross-layer design criterion
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Decentralized Optimization for
Multichannel Random Access (DOMRA)
l Original problem: difficult to globally optimize
l Objective function is equivalent to:
• reduce transmission collisions of the whole network.
• maximize the achieved data rate of each BS with
transmission capability limit.
l Problem decomposition:
• Subproblem 1: find optimal thresholds
• Resolves network collision
• Achieve proportional fairness
• Subproblem 2: find optimal power allocation policy
• Optimize individual transmission performance
• Satisfy average and instantaneous power constraints
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Optimal DOMRA - MAC Layer
Local knowledge:
|T
i
| : number of users receiving packets from User
i
|R
i
|: number of users sending packets to User
i
Two-hop knowledge (typical in routing discovery):
: total number of users sending packets to the
interfering neighbors of User i
l Optimal predetermined threshold
l Neighborhood Information
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Threshold Adaptation
Lowthreshold
Veryhigh
threshold
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Link Adaptation
• Capability-limited water-filling power allocation
Duetopeak
powerconstraint
Duetoaverage
powerconstraint
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Sub-Optimality Gap
l Gap to the global optimum.
• Obtain feasible decentralized policy
through subproblem decomposition
• Global optimum
• Global network knowledge
• Difficult to solve
• Exhaustive search
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Cochannel Interference Avoidance MAC (CIA-
MAC)
l Co-Channel Interference (CCI): the major factor limiting system
capacity
l CIA-MAC: improve downlink QoS of cell-edge users
l Severe interferers: dominant interfering BSs; randomize
transmission
Optimizedby:DOMRA
ThresholddesigntocontrolBSrandomtransmission
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How to Decide Severe Interferer?
l Severe Interference: no MAC frame recovered after CRC due to CCI
• Interference to Carrier Ratio (ICR) of Interferer i:
• Severe Interferer Judgment
• Interferer i is a severe interferer when
where is named CIA-MAC trigger.
• Transmission of severe interferer will always cause failure of
packet reception in the MAC layer.
l CIA-MAC is triggered when it achieves better network throughput
E(|H
i
|
2
P
i
)
ICR
i
=
E(|H|
2
P)
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Performance Improvement
CIA-MACwinsbecause: 1.fullfrequencyreuse;
2.intelligentrecognitionofsevereinterferers.
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Existing Random Access Schemes
l Channel-Aware Aloha, DOMRA: Aloha based, collision of entire
data frames result in low channel utilization
l Design signaling negotiation to avoid collision
l Existing schemes (e.g. CSMA-CA):
• Backoff when collision without considering CSI
• Drawback: deferring transmission may result in data
communications in deep fades.
Time
User1
SNR
User2
User3
First try of U2 and U3, a collision
t
1
t
2
t
3
U3 first counts down to 0 at t
2
, transmits, but in a deep fade
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Infrastructure Networks[Qin04]
l Opportunistic Splitting Algorithms
• Distributed splitting algorithm to reduce this contention.
• Resolve a collision and find the user with the best channel gain
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Opportunistic Splitting Algorithms [Qin04]
l In the beginning of each mini-slot, users with
H
l
<h<
H
h
will transmit
l At the end of each mini-slot, BS feeds back (0, 1, e) to all users
• 0: idle
• 1: success
• e: collision
l Users update the two thresholds,
H
l
and
H
h
, and continue
contention in the following mini-slots.
• Updates to minimize the collision probability in the following mini-
slots
• Example: e: increase
H
l
to reduce the number of users in the
range
l Finally only one user is expected to have gain
H
l
<h<
H
h
.
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Ad Hoc Networks
0.1
10.1
1.8
1.3
0.3
0.9
2.6
26.1
16.1
Channel
gain
Can distributed random access algorithms achieve
the performance of centralized algorithms?
How to do it?
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Channel Aware Distributed MAC (CAD-MAC)
l Different traffic flows contend for channel access
• Senders determine channel access
• Two types of contentions
l Type-I contention
• Links with the same transmitter
• Central scheduling
• (2,4), (2,8), and (2,10)
l Type-II contention (focus)
• Among all other links
• Distributed random access
• (2,4) and (3,10)
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Resolution of Type-I Contention
l Different channels experience gains of different
distributions
l Self-max-SNR scheduler
Distributionofcorresponding
channelgain
channelgain
1. Alllinksarescheduledwithequalprobability(fairnessassurance)
2. Alwaysschedulethelinkwiththebestinstantaneouschannelcondition
relativetoitsownchannelcondition(performanceassurance)
3.Sameasmax-SNRschedulerwhenalllinksarewithi.i.d.channeldistribution
E.g.:Rayleighfading
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Resolution of Type-II Contention
•1. Resolve contention in each CRS through a multi-stage channel-aware Aloha
(similar to DOMRA, use threshold to control contention performance and fairness)
•2. After one CRS, links with higher gains selected in a distributed way to continue
the contention in the following CRS (through distributed threshold control )
•3. Only one link with the best channel gain wins within each local area for data
transmission, all neighbors informed to keep silent
Users selected in Type-I contention resolution are involved.
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Three-Step Signal Exchange
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Complete Contention Resolution
CAD-MAC comparable to centralized schedulers
Theorem 1: With probability one, the contention
of any networks can be completely resolved by
CAD-MAC if sufficient CRSs are allowed.
Definition:Thecontentioninanetworkis
Completelyresolvedif
1. alllinksthathavewonthecontentioncan
transmitwithoutcollision;
2.ifanyadditionallinkthathasnotwonthe
contentiontransmits,itwillcollidewithat
leastonelinkthathaswonthecontention.
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l Performance loss compared to a centralized scheduler
• Due to CRSs used to resolve the contention
• Define the efficiency of CAD-MAC to be:
: average number of CRSs resolving contention
T
c
: CRS length; T
f
: frame length
Efficiency
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Necessary CRSs
Theorem2:For a network with N links, each interfering with all others,
where
Furthermore,
l A special case:
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Theorem3:For a network of any type and size,
Necessary CRSs
:transmissioncoexistencefactor,the average number of links that win the
contention in one frame slot
:contention coexistence factor,the average number of simultaneous
resolutions in each CRS
Examples:twocellularnetworksthatcoexist
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Efficiency of CAD-MAC
CRS length T
c
: round-trip time of signal propagation
Frame length T
f
: channel coherence time
Example:
l Cellular networks of 6km radius
round trip time: 50 [Leung 2002]
channel coherence time: tens of milliseconds with 900 MHz
carrier frequency and user speed 72 km/h [Kumar 2008]
Efficiency close to unity.
Proposition1:The efficiency of CAD-MAC satisfies,
For a network where each user interferer with all others,
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Robustness of CAD-MAC
l What if users have imperfect CSI because of non
-ideal channel estimation?
• Control medium access based on and
rather than the actual and
Theorem3:Theorems 1, 2, and 3 and Proposition 1 hold
when all users have imperfect channel knowledge and
CAD-MAC is robust to any channel uncertainty.
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Simulation Performance – CRSs Needed
for Complete Contention Resolution
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Simulation Performance
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5. CROSS-LAYER OPTIMIZATION
FOR ENERGY EFFICIENCY
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Energy Saving Energy Efficiency
l Complete Saving of Energy
• Shut down network completely to save the most energy
• Not desired!
l Purpose of energy-efficient wireless network design
• Not to save energy
• Make the best/efficient use of energy!
Energy saving
w/o
losing service quality
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l Essence of communications
• Use energy to move information from sender to
receiver
Energy and Communications
l What is energy?
The ability to move things
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Transmit Power Concern
l Information theorists studied energy-efficient communications for at
least two decades [Gallager88,Verdu90]
• Transceivers designed to maximize information bits per unit
energy
• Use infinite degrees of freedom per bit, e.g. infinite bandwidth or
time duration
• Example:
• Energy consumption per bit in AWGN channels:
• Minimized when t or W is infinite
E
min
= No ln 2 / g
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Energy Consumption Per Bit
UncodedMQAM
OptimalCoding
[Prabhakar01]
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Lazy Packet Scheduling [Prabhakar01]
l Minimize energy to transmit packets within a given
amount of time.
Packet arrival time
t
i
.
All packets are of the same size.
Question: what’s the transmission duration for each
packet so that the total energy transmitting all the
packets is minimized?
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Optimal Offline Scheduling
Intervalsbetweentwopckts
Transmissiontimeforeachpacket
Answer:divideavailable
transmissiontimeevenly
amongallpacketsandextend
thetransmissiontimeaslong
aspossible
Optimalonlinescheduling:
exploitthispropertywhile
considering
1.currentbuffersize
2.futurearrivals(statisticsof
arrivalprocess)
3.timeleft
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Energy Consumption in Practice
l RF transmit power:
• Consumed by PA for reliable delivery of data
l Circuit component power:
• Consumed by electronic circuits for reliable device
operations
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A Detailed Analysis of Energy Consumption
[Li,Bakkaloglu,and
Chakrabarti,07]
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Our Interest: Wireless Solution
Transmit mode critical for power consumption
• Radio interfaces account for more than 50% of overall system
energy budget for a smart cellular phone [Anand2007].
• Selection is critical
Our focus: transmit mode
Emphasize PHY and MAC to improve energy efficiency in transmit
states
Power consumption of 802.11 transceivers[Man05]
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Another Look at the Issues
• Circuit power Pc
• Usedbyallotherelectronics(filters,AD/DA)
• Devicedependent
• Afixedenergycostintransmitmode
• Transmit powerP
T
(R)
• Usedbypoweramplifierforreliable
bittransmission
• PowerforreliabletransmissionofR
• Dependonmodulation,codingandchannel
• SelectionofRdeterminesenergyefficiency
Needtobalanceconflictingdesignguidelinestooptimize
Transmitenergy:Extendtransmissiontimeaslongaspossible[Meshkati06],
[Prabhakar01]
Circuitenergy:UsehighestratesupportedandfinishtransmissionASAP
How to optimize the balance between transmit & circuit energy?
Across all subchannels?
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l Approach
• Allocate power, adapt modulation and coding based on
channel state information to
• reduce power consumption of point to point links;
• and balance the competing behaviors of multicell energy-
efficient communications
Energy-Efficient OFDM and MIMO
F1
F1
F1
F1
F1
F1
F1
OFDM and MIMO:
Key technologies in next-generation wireless communications
Few work done for energy-efficient OFDM and MIMO
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Energy Efficiency Metric
l Send as much data as possible given a certain amount of energy
l Given any small amount of energy consumed in a transmission
duration of , send a maximum amount of transmitted data
l Choose the optimal link adaptation, i.e. power and MCS (modulation
and coding scheme), to maximize
which is equivalent to maximize
called energy efficiency with a unit bits per Joule.
l Different from existing throughput maximization schemes: variation
of overall transmit power
Overalldatarateonallsubchannels
Vector,dataratesonallsubchannels
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Energy Efficiency in Flat Fading Channel
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Optimal Energy Efficient Link Adaptation in
Flat Fading Channels
l
Optimal energy efficient transmission rule [Miao07]
Mobile users always operate with optimal modulation mode
with optimal energy-efficient link adaptation
Energy Consumption to transmit 1 Mb of data
Optimal
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Properties of Energy-Efficient Link Adaptation
• To improve energy efficiency, we should
• Increase channel gain
• Reduce circuit power
• Increase the number of subchannels
• The data rate, determined by link adaptation, should
• Increase with channel gain
• Increase with circuit power
• Decrease with the number of subchannels
[Miao,TCom10]:Relationshipofenergyefficiency,distance,andrate
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Upper Bound
l Upper bound of energy efficiency
With Shannon capacity, it is g/(N
o
ln2) .
l How to achieve it?
• 1. Zero circuit power and transmit with infinite small
data rate
• 2. Infinite number of subchannels and transmit with
infinite small data rate.
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Conditions for Global Optimum
l Concept of Quasiconcavity:
l A local maximum is also a global maximum
x
1
x
2
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Optimal Energy-Efficient Transmission
l Energy efficiency
• strictly quasiconcave
l A unique global optimal link adaptation exists and is
characterized by
Measurehowgood
asubchanelis
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Optimal Energy-Efficient Transmission
l Example: Shannon capacity achieved on each
subchannel:
dynamic water-filling
l Classical water-filling
where is determined
by the peak power limit
Optimally balance circuit energy consumption and
transmit energy consumption on all OFDM subchannels
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Energy-Efficient Downlink Transmission
l A generic EE transmission theory
• Find
P
T
(R)
l Example: BS downlink OFDMA transmission [Xiong11]
• A special case of wireless transmission
• Except: before transmission, the BS needs to assign the
subcarriers to users according to certain rules.
• After scheduling: the base station needs to determine
transmission modes
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l Binary search assisted ascent (BSAA) for link
adaptation in frequency-selective channels
l A type of concave fractional programs
• Many standard methods for concave programs can be used
• E.g. Dinkelbach’s algorithm (converges superlinearly)
• Details in [Isheden 2011]
Algorithm Development-Frequency
Selective Channels
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Convergence of BSAA
Improvementofenergyefficiencywithiterations.
PDFof#ofiterationsforconvergence
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Performance Comparison
l Energy-efficient link adaptation always achieves the highest energy efficiency
l A tradeoff between energy efficiency and spectral efficiency exists
15dBm
20dBm
25dBm
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Energy Efficiency Metric
l Exponentially weighted moving average low-pass filters
• Average throughput at time t
• Average power consumption at time t
l Average energy efficiency metric:
circuit power
overalltransmitpoweronallsubchannels
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Low-Complexity Energy-Efficient Link Adaptation
l Note
l Dynamic water-filling power allocation to the level determined by
the previous energy efficiency.
Waterleveldeterminedby
previousenergyefficiency
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Multi-User Energy Aware Resource Allocation
l Max arithmetic average of energy efficiency (w/o
fairness)
l Max geometric average of energy efficiency (w/ fairness)
Energy aware resource allocation based on maximizing network
Energy aware resource allocation based on maximizing network
“throughput per joule”
“throughput per joule”
metric
metric
l In a multi-user system, all subchannels cannot be assigned to
one user
l How to assign subchannels?
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Resource Allocation w/o Fairness
l Allocation metric:
• For user n on subchannel k
• Assign subchannel k to the user with the highest metric
Closed-form resource allocation w/o fairness
• Circuit power >> transmit power (short distance commun.) and
circuit power the same for all users
Max-SINRscheduler
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Resource Allocation w/ Fairness
l Allocation metric:
• For user n on subchannel k
• Assign subchannel k to the user with the highest metric
Closed-form resource allocation w/ fairness
• Circuit power >> Transmit power (short distance commun.)
TraditionalProportional
Fairscheduler
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Performance—Link Adaptation
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Interference-Aware Energy Efficient Communication
l What will happen in a multi-cell
interference-limited scenario?
l For example: cell-edge users in
cellular network environment.
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Two-User Cooperation
l Consider two-user case to obtain insight
• Users 1 and 2 cooperates to determine their transmit powers
• Both have complete network knowledge
l Problem modeling
l Generally:
• Non concave
• Intractable
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Cooperative Power Optimization for Special Regimes
Transmitter condition:
• 1. circuit power >> transmit power
• 2. transmit power >> circuit power
Receiver condition:
• 1. noise power >> interference
• 2. interference >> noise
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Circuit Power Dominated Regime
l When circuit power dominates power consumption
• Equivalent to throughput optimization
• Binary power control [Anders 06]
Power of User 2
Power of User 1
Sum energy
efficiency
User 1 transmit max pwr
and User 2 is shut down
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Transmit Power Dominated Regime
l When the circuit power is negligible
l Users transmit with the lowest power and MCS for
maximum energy efficiency [Meshkati06]
Low Power
High Power
Low Power
High Power
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Noise Dominated Regime
l When noise power dominates interference plus noise
power
l Interference treated as noise
l Independent energy-efficient link adaptation
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Interference Dominated Regime
l Interference far larger than noise power
l On-Off energy-efficient power control, e.g. time sharing, is optimal
l Access protocol design is important
• Orthogonalize signals of different users
• SE protocols are also EE
• DOMRA, CIA-MAC, CAD-MAC
l Link adaptation is different from
l those for SE optimization
Energy-efficientpowersetting
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Intractability for General Cases
l What’s the situation for normal regimes?
• Non-concavity
• Multiple local maximums
• Behaviors of local maximums hard to predict
l Multiple subchannels and users further complicate the problem
l Impractical — complete network knowledge
l What can we do?
Non-cooperative
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Non-Cooperative Energy-Efficient Power Optimization
l User n chooses power to selfishly optimize energy efficiency
• Note: not appropriate for throughput, i.e. SE, maximization
• 1). Aggressive power control: selfish users increase transmit power
beyond what is reasonable [Goodman 2000]
• 2). Pricing is needed to regulate the aggressive behaviors
[Gesbert2007]
l Observation:
• A variation of traditional spectral-efficient power control with power pricing
• Socially favorable
SEoptimal
Powerpricing
Non-cooperativeenergy-efficientpowercontrolisdesirabletoreduce
interferenceandimprovethroughputinanon-cooperativesetting.
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Properties of Equilibrium
l Equilibrium:
• The condition that competing influences are balanced
• Its properties are important to network performance
l We analytically show:
• The equilibrium always exist;
• Necessary and sufficient conditions of the equilibrium
• With flat fading channels, the equilibrium is unique,
regardless of network conditions (channel gains, user
distribution)
• With frequency-selective channels, the number of
equilibrium depends on interference channel gains
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Equilibrium in Frequency-Selective Channels
l In frequency-selective channels,
• An example with at least two equilibria
• One equilibrium:
• Another due to symmetry of network assumptions
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Equilibrium in Frequency-Selective
Channels
l Sufficient conditions to assure a unique
equilibrium
where ||A|| is the Frobenius norm of
A.
• Not necessary: flat-fading channel
cases
onlydependon
interferencechannelgains
independentof
interferencechannelgains
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Equilibrium Power
l Define Network coupling factor
l Characterizes what level
different transmissions
interfere with each other
l Eqiulibrium power adapts to
interference strength: stronger
intf., lower pwr
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Performance in a 7-Cell Cellular Network
EEscheme
EEscheme
SEscheme
SEscheme
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6. Energy-Efficient Mobile Access
Networks: A Tradeoff Perspective
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Key Design Constraints
Tombaz,A.VästbergandJ.Zander,EnergyandCostEfficientUltraHighCapacityWirelessAccess",IEEEWireless
CommunicationMagazine,vol.18,no.5,pp.18-24,October2011.
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Tradeoffs in Cellular Network Design
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DE – EE
l DE: a measure of system throughput per unit of deployment cost
• An important network performance indicator for mobile
operators.
l DE consists of
• Capital expenditure (CapEx)
• Infrastructure costs (base station equipment, backhaul
transmission equipment, site installation, and radio
network controller equipment.)
• Operational expenditure (OpEx)
• electricity bill, site and backhaul lease, and operation and
maintenance cost
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Conflicting Design Rules
l DE:
• Large cell radius to save expenditure on site rental, base station
equipment, and maintenance, etc.
l EE:
• Smaller cell radius to save transmit power
• Example [Badic 2009]:
• By shrinking the cell radius from 1, 000 m to
250 m, the maximum EE of the HSDPA Network
will be increased from 0.11 Mbits/Joule to 1.92
Mbits/Joule, respectively
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Some Practical Considerations
l Previous DE-EE tradeoff assumes
• deployment cost scales continuously and proportionally with the
cell radius.
• only transmit power
l In reality
• the equipment cost does not scale proportionally with the target cell
size;
• the total network energy includes both transmit-dependent energy
(e.g. power consumed by radio amplifier) and transmit-independent
one (e.g. site cooling power consumption).
l The relation of DE and EE may deviate from the simple tradeoff curve
and become more complex when considering practical aspect
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Area Power Consumption and BS
Density
Tombaz,A.VästbergandJ.Zander,EnergyandCostEfficientUltraHighCapacityWirelessAccess",IEEEWireless
CommunicationMagazine,vol.18,no.5,pp.18-24,October2011.
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BW-PW
Convergeto
l Given a data transmission rate
Expansion of signal bandwidth
reduces transmit power and
achieves better energy
efficiency.
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BW-PW: In Practice
l Given a data transmission rate
Expansion of signal bandwidth
reduces transmit power and
achieves better energy
efficiency.
l In practice:
l the circuit power consumption,
such as filter loss, actually
increases with the system BW
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Circuit PW Scales with Bandwidth
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DL-PW
l According to Shannon:
Power needed for
reliable delivery of
one bit
Time needed for sending
one bit, i.e. delay
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DL-PW: In Practice
l Other device power needed to enable
operation:
127
Other device power
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DL-PW: One Step Further
l Traffic dynamics
• Delay include both the waiting time in the traffic
queue and the time for transmission
A(t)
m(P(t), H(t))
Avg. Power
Avg. Delay
Min.Avg.EnergyRequiredforStability
[BerryandGallager2002]
Open problem with practical considerations
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SE-EE
l In single-user scenario
• SE
• EE
• SE-EE relationship
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SE-EE In Practice
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SE-EE: In Practice
l
Reduced tradeoff between energy efficiency and spectral
efficiency w/ interference
Interference bounds SE
EE is sensitive to pwr,
but SE is not .
EE schemes are advantageous
in interference-limited scenarios
Tradeoff decreases with
intf.
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7. CONCLUSIONS AN REFERENCES
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Conclusion
l Cross-layer optimization for SE
• Spectral-efficient link adaptation
• Spectral-efficient centralized MAC
• Spectral-efficient distributed MAC
l Cross-layer optimization for EE
• Energy-efficient link adaptation
• Energy-efficient centralized MAC
• Energy-efficient distributed MAC
l Energy-Efficient Mobile Access Networks: A Tradeoff Perspective
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Light Analogy
134
Oillamp(first
massproduced)
(1G?)
Gaslights
(2G?)
Electricallamps
(3Gandbeyond?)
Brighterandbrighter…
(Higherandhigherlumencapacity)
And……
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Light Analogy
135
1780,oillamp
1794,gaslamp
From1800,electriclamp
1926, fluorescent lamp
(1/5 energy consumption, 5 years life)
1962, LED lamp (40% further energy
reduction, 32 years life)
1973, compact fluorescent lamp
145 years capacity improvement …
87 years of energy efficiency enhancement!
miao@FreeLinguist.com
Implications ….
l Where are we?
136
1G 2G 3G 4G2.5G
Faster?
(how faster
do we need)
Sustainable
design
1980s,
28KBPS
1990s,
100KBPS
2000s,
2MBPS
2010s,
100MBPS
miao@FreeLinguist.com
More Information
137
Cambridge University Press
miao@FreeLinguist.com
REVOLUTIONAL
THINKING
AHEAD
138
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Acknowledgement
139
Dr.GuocongSong
provideslidesrelatedtoutility-based
centralizedscheduling.
miao@FreeLinguist.com
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