Energy Efficient Wireless Communications

Energy Eﬃcient Wireless
Communications
Jingon Joung
ACT: Advanced Commun. Tech. Dept.
I2
R: Institute for Infocomm Research
A⋆
STAR: Agency for Science, Technology, And Research, Singapore
Tutorial 2: The 14th International Conference on Electronics, Information
and Communication (ICEIC’2015), Singapore
29 January 2015
Jingon Joung Tutorial 2: Energy Eﬃcient Wireless Communications 1 / 112

Index
1 Introduction and Background (35 min, 24 pages)
Green Wireless Communications
Efficiency in Communications
Theoretical Spectral Efficiency (SE) Energy Efficiency (EE) Tradeoff
Practical SE-EE Tradeoff
2 Technologies for Energy Efficiency (20 min, 11 pages)
Device-Level Approach
System-Level Approach
Network-Level Approach
3 Review (30 min, 60 pages)
PA Switching/Selection (PAS) Method
Large-scale Distributed-Antenna Systems (L-DAS)
4 Conclusion and QnA (5 min, 3 page)

Index

• Green
– to reduce energy consumption
– to reduce CO2 emission

• Green Information & Communications Tech. (ICT) [1]
– the 5th largest industry in power consumption
– energy consumption: 3% (2007’)
– CO2 emission: 2% (2007’) will increase X35, 4% (2020’)
– e.g., cellular networks:
ƒ energy: 77.5 TWh (2/60/3.5/10 TWh for 3b/4MM/20,000/etc.
subscribers/radio stations/radio controllers/others)
ƒ CO2: 35 Mt (1/20/2/5 Mt for “ )

• Wireless access communication networks consume
significant amount of energy to overcome [2]
– fading, distortion, degradation (path loss)
– obstacles, interferences
TX
wirelesschannel
RX

• The energy is mostly consumed at the
transmitter
• e.g., 80-85% power at base station (BS) in
cellular networks [2]
Base Band
Module
D
A
C
Informationbinarybits
Filter Filter
LO
LFTPA
Attn. control
DC control
Mixer
VGA
Synthethesizer

• 50–80% of transmitter’s power is
consumed at power amplifier (PA) [3]
PA
input
signal
output
signal
DC

References
[1]:
[McK, 2007]
[Fettweis and Zimmermann, 2008]
[Mancuso and Sara Alouf, 2011]
[Chatzipapas et al., 2011]
[2]:
[Richter et al., 2009]
[Baliga et al., 2011]
[Vereecken et al., 2011]
[Chatzipapas et al., 2011]
[Joung and Sun, 2012]
[3]:
[Gruber et al., 2009]
[Bogucka and Conti, 2011]
[Joung et al., 2012a]
[Joung et al., 2014c]
[Joung et al., 2013]

Green ICT Projects & Consortiums
C2Power: Project, EU, Jan. 2010–Dec. 2012
- cognitive radio and cooperative strategies
- power saving in multi-standard wireless devices
eWIN, Project, KTH, Sweden
ECOnet: low Energy COnsumption NETworks, EU, Oct. 2010–Sept. 2013
- dynamic adaptive technologies
G-MC2: Project, Green Multiuser-MIMO Cooperative Communications,
I2
R, Singapore, Jan. 2012–Dec. 2014
GREENET: Consortium, green wireless networks, EU
GREENT: Consortium, green terminals for next generation wireless
systems, EU
CO2GREEN Project: Spain
- cooperative and cognitive techniques
- green wireless communications
GreenTouch: Consortium, architecture, speciﬁcations and roadmap, since
2010
earth: Project, Energy Aware Radio and neTwork tecHnologies
Green Radio: mobile virtual center of excellence (VCE) and personal
communications, UK

Index

What’s Efficiency?
Efficiency: has widely varying meanings in different disciplines
“refers to the use of resources so as to maximize the production of goods
and services” – economics
“is an important factor in determination of productivity” – business
“describes the extent to which time, effort or cost is well used for the
intended task or purpose” – wikipedia
“is the ratio of the energy developed by a machine, engine, etc., to the
energy supplied to it” – dictionary
Efficiency in general
η
valuable resource produced
valuable resource consumed
Efficiency in communications?

valuable resource produced in comm: information, coverage, subscribers, ...
valuable resource consumed in comm: frequency, space, time, power, ...
Spectral Efficiency (SE) and Energy Efficiency (EE)
SE, b/s/Hz: number of reliably decoded bits per channel use
[Cover and Thomas, 2006, Verdú, 2002, Chen et al., 2011]
- Shannon (channel) capacity, data rate (throughput), goodput
EE, b/J: number of reliably decoded bits per energy
- various EEs in communications

Various EEs in Comms.
Table 1: Various EEs [Richter et al., 2009, Tombaz et al., 2011, Hasan et al., 2011,
Zhou et al., 2013, Joung et al., ].
Metric Type Units
power usage efficiency facility-level ratio (≥ 1)
data center efficiency facility-level %
telecommun. energy effi. ratio equipment-level Gbps/W
telecommun. equipment energy effi. rating equipment-level − log(Gbps/W)
energy consumption rating equipment-level W/Gbps
area power consumption network-level Km2
/W
user power consumption network-level users/W
network energy effi. with delay effect network-level dB/J
pecuniary efficiency network-level bits/$

Index

Theoretical SE-EE Tradeoff
SE, b/s/Hz
EE,b/J
Figure 1: Theoretical SE-EE tradeoff. Figure 2: Ideal power consumption.
SE = log2(1 + Pout/σ2
): Gaussian signalling, perfectly linear PA
[Cover and Thomas, 2006]
EE = Ω SE
Pc
: ideal power consumption model in Fig. 2
[Verdú, 2002, Chen et al., 2011]
[Pout: transmit power; σ2: noise power; Ω: total bandwidth]
Convex and wide (good) tradeoff

SE, b/s/Hz
EE,b/J
Figure 1: Theoretical SE-EE tradeoﬀ.
Pc PPA Pout==
Figure 2: Ideal power consumption.
EE = Ω SE
Pc
[Verd´u, 2002, Chen et al., 2011]

SE, b/s/Hz
EE,b/J
1
σ2 ln 2
Figure 1: Theoretical SE-EE tradeoﬀ.
Pc PPA Pout==
Figure 2: Ideal power consumption.
EE = Ω SE
Pc
[Verd´u, 2002, Chen et al., 2011]

Index

Practical PA Package
(a) SM2122-44L (b) SM0822-39
Figure 3: (a) High power PA with Pmax
out = 25 W(44 dBm) and g = 55 dB. Frequency
band 2.1 GHz −2.2 GHz. (b) Low power PA with Pmax
out = 8 W and g = 45 dB.
PA: a type of electronic ampliﬁer
- converts a low-power RF signal into a larger signal of signiﬁcant power
- typically, for driving the antenna of a transmitter

Basic Circuit Model of PA
ACRF-drive,Pin DC input, PDC
PAoutput,Pout
Power Amplifier (PA)
Figure 4: Power amplifier model with field-effect transistor (FET)
[Cripps, 2006, Kazimierczuk, 2008].
Transistor is a core semiconductor device:
amplifies and switches electronic signals and electrical power
changes a voltage or current: terminals (G,D) to terminals (S,D)

Basic Circuit Model of PA
ACRF-drive,Pin
DC input, PDC
PAoutput,Pout
gate
drainsource
transistor
RFC
DC blocking capacitor
inputnetwork
outputnetwork
resistor
Figure 4: Power amplifier model with field-effect transistor (FET)
[Cripps, 2006, Kazimierczuk, 2008].
Transistor is a core semiconductor device:
amplifies and switches electronic signals and electrical power
changes a voltage or current: terminals (G,D) to terminals (S,D)

Ideal PA Linearity
0 0.5 1 1.5 2
0
0.2
0.4
0.6
0.8
1.0
1.2
normalized input signal amplitude, |vin|
normalizedoutputsignalamplitude,|vout|
(a)
−30 −20 −10 0 10
−1
0
1
input power, dBm
normalizedgain,dB
(b)
−30 −20 −10 0 10
−1
0
1
input power, dBm
phaseshift,degree
(c)
Figure 5: (a) and (b): Dynamic amplitude-to-amplitude modulation (AM/AM)
distortion characteristics. (c) amplitude-to-phase modulation (AM/PM) characteristic.
between RF input and output signals, i.e., Pin and Pout

Ideal PA Eﬃciency
5 10 15 20 25 30 35 40 45 50 55
5
10
15
20
25
30
35
40
45
50
55
PDC dBm
Pout(Pmax)dBm
Figure 6: Maximum output power at linear region versus PA power consumption.
between input and output signals, i.e., Pout/PDC.

Linearity Models [Teikari, 2008]
Transistor-level
System-level
accurate yet difficult to obtain, generalize or analyze
a few parameters obtained from measurements,
tractable, and reasonably accurate
PA
Linearity
Models
[23][Vuolevi et al., 2000], [24][Boumaiza and Ghannouchi, 2003], [25][Gadringer et al., 2005],
[26][Morgan et al., 2006], [27][Kim and Konstantinou, 2001], [28][Jeruchim et al., 2000]

Transistor-level
System-level
Memory Memoryless
a frequency-domain fluctuation due to
the capacitance and Inductance in the
circuits and the thermal fluctuation of
the PAs, i.e., an electrical and thermal
memory effects, respectively [23,24]
Volterra series model [25]
Wiener, Hammerstein models [26]
Memory polynomial [27]
previous PA output signal
does not affect the current
PA output signal
PA
Linearity
Models

Transistor-level
System-level
Memory Memoryless Passband
Baseband
a frequency-domain fluctuation due to
the capacitance and Inductance in the
circuits and the thermal fluctuation of
the PAs, i.e., an electrical and thermal
memory effects, respectively [23,24]
Volterra series model [25]
Wiener, Hammerstein models [26]
Memory polynomial [27]
previous PA output signal
does not affect the current
PA output signal
difficult to do simulation
and computation
Nonlinearity of complex
baseband frequency
approximation is
captured simply [28]
PA
Linearity
Models

Memoryless Baseband PA Models
Generic model: simpliﬁed baseband model for tractable analysis
Ideal model for analysis:
y = gx,
where x and y are PA input and output signals; and g > 0 is a linear gain.
Linear model for analysis [Minkoﬀ, 1985]:
y = gx + n,
where n is the nonlinear distortion that is independent of x and modeled as
Gaussian noise based on Bussgang’s theorem [Bussgang, 1952].
Soft limiter model for analysis [Tellado et al., 2003]: y = γ (|x|) e2πjΦ(|x|)
- γ(·): amplitude-dependent gain function
- Φ(·): phase shift function
γ (|x|)
|x|, if |x| < vsat,
vsat, otherwise,
Φ(|x|) 0
where vsat is a saturation output amplitude.

Memoryless Baseband PA Models
PA-specific model: accurate baseband model specified to PA type
Saleh model for traveling wave tube amplifier [Saleh, 1981]:
γ (|x|)
a1|x|
1 + a2|x|2
; Φ(|x|)
b1|x|2
1 + b2|x|2
where ai and bi are the distortion coefficients
Ghorbani model for FET PA and for low amplitude nonlinearity
[Ghorbani and Sheikhan, 1991]:
γ (|x|)
a1|xa2
|
1 + a3|xa2 |
+ a4|x|; Φ(|x|)
b1|xb2
|
1 + b3|xb2 |
+ b4|x|
Rapp model for envelope characteristic of solid state PA (especially,
class-AB) [Rapp, 1991, Falconer et al., 2000]:
γ (|x|) |x| 1 +
|x|
vsat
2p − 1
2p
; Φ(|x|) 0
where p is a smoothness factor.

PA Linearity = Nonlinear
0 0.2 0.4 0.6 0.8
0
0.2
0.4
0.6
0.8
1.0
1.2
Ideal model
Linearized model (35dB)
Soft limiter model
Rapp model (p=10)
Rapp model (p=2)
Saleh model
Ghorbani model
p=10
p=2
normalized input signal amplitude, |vin|
normalizedoutputsignalamplitude,|vout|
Figure 7: AM/AM distortion characteristics for various baseband PA models.

Efficiency Models
Efficiencies of PA [Kazimierczuk, 2008, Raab et al., 2002]
1 Drain efficiency: ηD
Pout
PDC
2 Power-added efficiency (PAE): ηPAE
Pout−Pin
PDC
= ηD 1 − 1
g
3 Overall efficiency: ηO
Pout
PDC+Pin
Typically η ηD ≃ ηPAE ≃ ηO, ∵ Pin ≪ PDC, Pin ≪ Pout
Two important factors

PA Eﬃciency < 100%
5 10 15 20 25 30 35 40 45 50 55
5
10
15
20
25
30
35
40
45
50
55
20% drain efficiency line
PAs: 0−1 GHz
PAs: 1−2 GHz
PAs: 2−3 GHz
PAs: 3−4 GHz
PAs: 4−5 GHz
PDC dBm
Pout(Pmax)dBm
Figure 8: Maximum output power (at the linear region) versus PDC.
η < 100% [Raab et al., 2003a]
Typically between 20% and 30% [Joung et al., 2012a]

PA Eﬃciency Drop
0 5 10 15 20 25 28 30 34 40 45 50
0
10
20
30
40
50
60
70
Power-addedeﬃciency(PAE),ǫ%
Pout dBm
SKY65162-70LF
optimal matching
Doherty
class-AB
MAX2840
RF2132
RF2146
SST13LP01
AN10923
Doherty 21180
Figure 9: PAE versus Pout.
η usually drops rapidly as the output power decreases [Shirvani et al., 2002]
a peak η is achieved at the peak envelope power (PEP)

PA Classes
Table 2: Examples of PA Classes
[Raab et al., 2002, Raab et al., 2003a, Raab et al., 2003b].
Class Linearity Efficiency Utilization Factor Main Applications
A high ≤ 50% 0.125
millimeter wave (30-100GHz)
4W/30%/Ka-band
250mW/25%/Q-band
200mW/10%/W-band
B high ≤ 78.5% 0.125 broadband at HF and VHF
C ≤ 85% - high-power vacuum-tube Tx
D low ≤ 100% 0.159 100W to 1kW HF
E low ≤ 100% 0.098
high-efficiency at K-band
16W with 80% efficiency at UHF
100mW with 60% efficiency at 10GHz
F low very high ≥ 50% from 0.125 to 0.159 UHF and microwave
(operation) class: amplification method
power-output capability (transistor utilization factor):
output power
(# of transistor)×(peak drain voltage)×(peak drain current)

Applications of PAs
HF VHF UHF L S C X Ku K Ka V W G THF
3M 30M 300M 1G 2G 12G 18G4G 8G 26.5G 40G 110G 300G 3T3G 75G
(1-300M)
Comm. Submarines (3-30K)
AM broadcasts (153K-26M)
Navigation
FM (87.5-108M)
TV (54M-890M, VHF, UHF)
Weather radio (162M)
Aircraft comm.
Land/maritime mobile comm.
RFID (ISM band)
Shortwave Broadcasts
Citizens' band radio
Over-the-horizon radar/comm.
Amateur radio
(3G-30G)
WLAN 802.11a/ac/ad/n (5G)
DBS (10.7G-12.75G)
Satellite comm./TV (C,Ku,Ka)
Microwave devices/communications
Modern radars
Radio astronomy
Amateur radio
(30G-300G)
LMDS (26G-29G, 31G-31.3G)
WLAN 802.11ad (60G)
HIgh-frequency microwave radio relay
MIcrowave remote sensing
DIrected-energy weapon
MIllimeter wave scanner
Radio astronomy
Amateur radio
(1T~)
Terahertz imaging
Ultrafast molecular dynamics
Condensed-matter physics
Terahertz time-domain spectroscopy
Terahertz computing/communications
Sub-mm remote sensing
Amateur radio
(300M-3G)
FRS/GMRS (462M-467M)
ZigBee (ISM: 868M,915M,2.4G)
Z-Wave (900M)
DECT/Cordless telephone (900M)
GPS (1.2,1.5G)
GSM (900M,1.8G)
UMTS (2.1G)
LTE (698M-3.8G)
WLAN (802.11b/g/n/ad 2.4G)
Bluetooth (2.4-2.483.5MHz)
BAN/WBAN/BSN (2.36G-2.4G)
UWB (1.6G-10.5G)
Modern Cordless telephone (1.9G,2.4G,5.8G)
Si LDMOS
GaN-HEMT
Si RF CMOS
SiGe-HBT, SiGe-BiCMOS
GaAs-HBT, GaAs-pHEMT
InP-HBT InP-HEMT, GaAs-mEHMT
SiC-MESFET
0
Figure 10: Power amplifier applications over IEEE bands [Joung et al., 2014b].
FET: field effect transistor
MOSFET: metal oxide semiconductor FET
MESFET: metal epitaxial semiconductor FET
DMOS: diffued metal oxide semiconductor
LDMOS: laterally DMOS
CMOS: complementary metal oxide semicond.
BiCMOS: bipolar CMOS
HBT: heterojunction bipolar transistor
HEMT: high electron mobility transistor
mHEMT: metamorphic HEMT
pHEMT: pseudomorphic HEMT
SiGe: silicon Germanium
GaAs: Gallium Arsenide
GaN: Gallium Nitride
InP: Indium Phosphide
ISM: industrial, scientific and medical
WPAN: wireless personal area network
WLAN: wireless local area network
LTE: long term evolution
GSM: Global system for mobile communications
UMTS: universal mobile telecommun. system
UWB: ultra-wide band
GPS: global positioning system
FRS: family radio service
GMRS: general mobile radio service
BSN: body sensor network
BAN: body area network
WBAN: wireless body area network
DBS: direct-broadcast satellite
LMDS: local multipoint distribution service
DECT: digital enhanced cordless telecommun.

PA in Wireless Communications
The LDMOS PA are employed for many communication equipments.
The international technology roadmap for semiconductors (ITRS) reports
that 48 volt LDMOS transistor is widely used in cellular infrastructure
market in 2011 [ITRS, 2011].
∵ The LDMOS can support high output power required for a base station
(BS) in cellular networks.
Table 3: Transmit Power of BSs [Auer et al., 2011, earth, 2015]
Type Pmax
out back-oﬀ average max output power ISD [m]
Macro 54 dBm (250 W) 8 dB 43 dBm (20 W) 1 Km −5 Km
Micro 46 dBm (40 W) 8 dB 38 dBm (6.3 W) 100 m −500 m
Pico 33 dBm (2 W) 12 dB 21 dBm (125 mW) 10 m −80 m
Femto 29 dBm (800 mW) 12 dB 17 dBm (50 mW) indoor
To compensate poor eﬃciency of LDMOS devices in the low power regime,
envelope tracking architecture or Doherty technique is used for 3G and 4G
networks that employs high PAPR waveform signals.

Previous Studies on the Practical
SE-EE Tradeoff
Practical SE analysis with PA nonlinearity for multicarrier systems
[Tellado et al., 2003, Zillmann and Fettweis, 2005,
Gutman and Wulich, 2012]
Practical SE-EE tradeoff conjecture [Chen et al., 2011]
Practical SE-EE tradeoff analysis with practical power consumption model
[Héliot et al., 2012, Onireti et al., 2012]
Analytical and numerical quantification of SE-EE tradeoff with BOTH
nonlinearity and imperfect efficiency of PA
[Joung et al., 2012a, Joung et al., 2014c]

SE, b/s/Hz
EE,b/J
?
Figure 11: Practical SE-EE tradeoff.
Pc PPA Pout≫≫
Figure 12: Practical Power Consumption.
Practical system’s power consumption model in Fig. 12
efficiency < 100% and PA nonlinearity
SE is not a log function anymore [Joung et al., 2012a, Joung et al., 2014c]
Concave, narrow (bad), and biased tradeoff

SE, b/s/Hz
EE,b/J
Theoretical tradeoff in Fig. 1
Figure 11: Practical SE-EE tradeoff.
Pc PPA Pout≫≫
Figure 12: Practical Power Consumption.
Practical system’s power consumption model in Fig. 12
efficiency < 100% and PA nonlinearity
SE is not a log function anymore [Joung et al., 2012a, Joung et al., 2014c]
Concave, narrow (bad), and biased tradeoff

1
σ2 ln 2
SE, b/s/Hz
EE,b/J
PA inefficiency and SE degradation
resulting in drop of EE
PA nonlinearity resulting in drop of SE
Theoretical SE-EE tradeoff
Practical SE-EE tradeoff
Figure 13: Illustration of SE-EE tradeoff.
SE maximization or EE maximization? EE for a single antenna (PA)

Index

Energy Eﬃcient Technologies
• 50–80% of transmitter’s power is consumed
at power amplifier (PA)

Transmitter architecture
PA package structures

Transceiver signal processing
IBO/PAPR/DPD/…

Transceiver signal processing
IBO/PAPR/DPD/…
Network processing
SC/HetNet/CZ/CoMP/CoNap/DTX/…

EE Tech. [Joung et al., 2014b]
Approaches Methods Improvement Challenges
1. PA Design
linear architecture Linearity (L)
high cost,
parallel architecture Efficiency (E)
large form factor
switching architecture (PAS) E
envelope tracking (ET) architecture E
PA circuit architecture
envelope elimination & restoration (EER/Kahn) L, E
outphasing technique (LINK) L, E
Doherty technique E
2. Signal Design
PAPR reduction
clipping
L, E
out-of-band emission
coding
additional resource,partial transmit sequence (PTS)
latency,
PA input/output
selective mapping (SLM)
complexity
signal processing
tone reservation (TR)
tone insertion (TI)
Linearlization
feed forward sensitive to PA,
feedback additional circuit,
digital predistortion (DPD) complexity
3. Network Design
Network densification
E
infrastructure,
in/out band small cells
overhead signalling,
distributed antenna system (DAS)
scalability,
Efficient network
cooperative communications (relay)
handover,
topology & protocol
Network protocol
interference
design by
cell zooming
deploying low power PA
coordinated multipoint (CoMP)
or using PA on/off
cell discontinuous TX/RX (DTX/DRX)
coordinated sleep and napping (CoNap)

Index

1. PA Design
Architecture: a building block of various circuits (e.g., multiple PAs,
oscillators, mixers, ﬁlters, matching networks, combiners, and circulators)
Transmitter Architecture: direct approach, PA package structures
1 Linear architecture [Raab et al., 2003c]
2 Corporate architecture [Cripps, 2006]
3 Parallel architecture [Shirvani et al., 2002, Alc, 2008]
4 Stage bypassing and gate switching [Raab et al., 2003c, Staudinger, 2000]
5 Envelope elimination and restoration (EER) technique (or Kahn)
[Raab et al., 2003c, Kahn, 1952]
6 Envelope tracking architecture [Raab et al., 2003c] F
7 Outphasing using linear ampliﬁcation using nonlinear components (LINK)
[Raab et al., 2003c, Cox, 1974]
8 Doherty technique [Cha et al., 2004, Doherty, 1936, Correia et al., 2010,
Auer et al., 2011, Arnold et al., 2010] F
9 Combined complex architecture, etc.

Transmit Architectures
DC
A,P gA,P
DC
A,P gA,P
DC
+
A,P gA,P
DC
A,P gA,P
A,P gA,P
E-Det. A
A,P A,P gA,P
E-Det. A
P
DC
+
A,P gA,P
P1
P2
DC
+
A,P gA,P
DC : DC power supply : PA A: amplitude information P: phase information g: gain: RF-drive input : RF output : delay+ : combiner
(a) (b1)
(d) (e)
(b2) (c)
(f) (g)
SCS
limitter
DC
DC/DC
Figure 14: Various transmit architectures: a) Linear architecture. b) Parallel
architecture. c) Switching architecture. d) Envelope tracking (ET) architecture. e)
EER technique (or Kahn). f) Outphasing technique. g) Doherty technique (DT).
ET: DC power is controlled by the envelope of the RF input signal
DT: Class-B (carrier or main PA) + Class-C (peaking or auxiliary PA)

Key Points
Table 4: Efficiency, costs and environmental impact of a 20,000-base-station network
with different power amplifier technologies [Mancuso and Sara Alouf, 2011].
Traditional technology Doherty technology Envelope tracking
PA efficiency 15 % 25 % 45 %
Power consumption 51.7 MW 27.2 MW 16.1 MW
CO2 emission 194,600 tons 102,400 tons 60,800 tons
Power cost 54.3 MM$ 28.6 MM$ 17.0 MM$
Challenges:
Nonlinearity and inefficiency are fundamental and inevitable.
mathematical model: modeling and empirical evaluation, i.e.,
simulated-annealing-based custom computer-aided design
high manufacturing cost
large form factor
wide dynamic range with high efficiency
advanced materials and metamaterials

Index

2. Signal Design
Transceiver Signal Processing: knowledge of the signal properties
1 Input backoff (IBO) [Kowlgi and Berland, 2011]
- Reducing the transmit power to sufficiently below its peak output power
- Broadband signal having a high peak-to-average power ratio (PAPR), e.g., 8
to 13 dB PAPR for ODFM signals [Kowlgi and Berland, 2011], requires high
IBO
- High IBO reduces the PA efficiency
2 PAPR reduction methods [Jiang and Wu, 2008]
Clipping/ coding/ partial transmission sequence and selective mapping/
nonlinear companding transform/ tone reservation and tone injection
3 Linearization [Pothecary, 1999, Kenington, 2000]
To mitigate the nonlinearity of PAs in the low power regime, various
linearization methods such as
feedforward/ feedback/ predistortion
4 Active antenna systems (reducing feeder loss)
5 SE improving system design: large (massive) MIMO, 3D MIMO, etc.
6 Combined complex architecture

Key Points
Impact: avoidance/compensation of the adverse impact of PA nonlinearity
Cost:
- PA efficiency reduction (high power consumption) for input back off
- out-of-band radiation increase for signal clipping
- SE reduction for side information
- additional computational complexity (see e.g., parameter optimization and
multiple candidate sequence generation [Rahmatallah and Mohan, 2013])
- additional analog circuits
Challenges:
hardware capability limitation
high sensitivity to PA status
additional resource requirement
additional circuits
tradeoff between: linearity and efficiency; SE and EE

Index

3. Network Design
Network Processing: using small power PA/reducing PA activation time
1 Network densiﬁcation
- in/out band small cell, inter-cell interference coordination (ICIC),
heterogeneous network (HetNet) [Chen et al., 2011, Richter et al., 2009]
- DAS [Choi and Andrews, 2007, Joung and Sun, 2013, Joung et al., 2014a]
- relay [Joung and Sun, 2012, Yang et al., 2009]
Figure 15: Example of network densiﬁcation.

3. Network Design
Network Processing: using small power PA/reducing PA activation time
- in/out band small cell, inter-cell interference coordination (ICIC),
heterogeneous network (HetNet) [Chen et al., 2011, Richter et al., 2009]
- DAS [Choi and Andrews, 2007, Joung and Sun, 2013, Joung et al., 2014a]
- relay [Joung and Sun, 2012, Yang et al., 2009]
carrier frequency 1 carrier frequency 2
joint processing
cross-tier
interference intra-tier
interference
in-band small cell coverage
out-band small cell coverage
cooperative communication region Macro BS coverage
usermacro BS
small BS
relay AP/RRH
Figure 15: Example of network densiﬁcation.

Network Design
2 Network protocol design
- Cell zooming [Niu et al., 2010]
- CoMP [Han et al., 2011]
- DTX/CoNap [Frenger et al., 2011, 3GPP, TR 25.927 V11.0.0, 2012,
Adachi et al., 2012, Adachi et al., 2013, Shirvani et al., 2002]

Network Design
deactivated BS deactivated BSactive BS

Network Design
deactivated BS active BSactive BS

Network Design
deactivated BS active BSactive BS
fre q u e n c y
P A c a n b e tu rn e d o ff
P A is o p e ra tin g in h ig h e ffic ie n c y
T ra n s m it
p o w e r

Key Points
Requirements for cell densification: cross-tier interference management
- cell range expansion [Damnjanovic et al., 2011] and eICIC
[Kosta et al., 2013] in 3GPP
- optimization of beamforming and radio resource management
[Joung et al., 2012b, Joung et al., 2012c]
Requirements for network protocol design: cooperation or coordination
among different nodes
- decide how often the cooperation/coordination performs PA on and off to
balance the system performance and the power saving
- optimization of the radio resource allocation
Challenges:
high infrastructure cost
overhead signalling and frequent handover
network (backbone) overhead
scalability to already-deployed networks

Index

System Model
x y
IDFTF
addCP→P/S→DAC
ADC→S/P→removeCP
DFTFH
x
PA
LPA(·)
{h0, · · · , hL−1}
w
z
y
Figure 16: An OFDM system with a nonlinear memoryless PA [Joung et al., 2014c].
x = [x0, · · · , xN−1]
T
∼ CN(0, Pin): OFDM symbol consisting of N
complex-valued data symbols
x = [x0, · · · , xN−1]T
= F x: time domain signal vector

Signal Model
F : N-by-N unitary IDFT matrix
wt = LPA(xt): PA output signal, w = [w0, · · · , wN+NCP−1]T
xt = atejθt
: at |xt|, θt is the phase of xt (0 ≤ θt < 2π)
wt = btejθt
: bt |wt|
{h0, h1, · · · , hL−1}: L-tap multipath channel
Yt = ht ⊗ wt + zt: received signal
- perfect timing synchronization
- the CP is removed
- indices are shifted to start from 0
- zt ∼ CN(0, σ2
z ): AWGN
- yt rtejφ
: rt and φt represent the amplitude and phase of Yt
- y = [Y0, · · · , YN−1]T
: vector representation
y = [y0, · · · , yN−1]T
= F H
y: frequency domain signal vector after DFT of
y

Assumptions
A1: {xn} are i.i.d. with distribution x ∼ CN(0, Pin)
A2: Soft limiter model for PA input/ouput power:
LPA(at) =
√
gat, if at < amax
bmax, if at ≥ amax
- amax Pmax
in and bmax Pmax
out
- Linearity in low power regime can be obtained with linearization techniques
(e.g., feedforward, feedback, and predistortion)

Mutual Information (MI) in Flat
Fading Channels (L = 1)
Achievable rate averaged over N transmissions is given by
I(X; Y )/N
(a)
= I(X; Y )/N (a) Unitary transform (IDFT)
(b)
=
N−1
t=0 I(Xt; Yt)/N (b) independency in time domain
(c)
= I(X; Y ) = H(Y ) − H(Y |X) (c) identical MI over t
(d)
= H(Y ) − log2 πeσ2
z [b/s] (d) H(Y |X) = H(N) = log2 πeσ2
z
The entropy of Y is given by [Cover and Thomas, 2006]
H(Y ) = −
y
fY (y) log2 fY (y)dy

Entropy H(y) in Flat Fading Ch.
Define S as a binary random variable denoting the clipping at the PA:
S =
0, if A ≤ amax
1, otherwise
The pdf of y can be rewritten as
fY (y) = fY (y, S = 0) + fY (y, S = 1)
A closed-form expression of fY (y, S = 0) and fY (y, S = 1) (see
[Joung et al., 2014c] for the proof):
fY (y, S = 0) = N0(y) 1 − Q1 µ(y),
√
ρmax
fY (y, S = 1) = N1(y) exp −
a2
max
Pin
−
2bmaxyRe
σ2
z
I0
2bmax|y|
σ2
z
- N0(y): pdf of CN 0, gPin + σ2
z , N1(y): pdf of CN bmax, σ2
z
- Q1(·, ·): Marcum-Q-function [Papoulis and Pillai, 2002] with parameters
ρmax
2(gPin+σ2
z)
gPin
√
bmax and µ(y)
8gPin(gPin+σ2
z)
σ4
z
|y|2
- yRe: real part of y
- I0(·): modified Bessel function of first kind

Analytical Results on SE
SE(ξ) = H(Y ) − log2 πeσ2
z
= −
y s=0,1
fY (y, S = s) log2
s=0,1
fY (y, S = s) dy − log2 πeσ2
z
If the PA is perfectly linear, i.e., bmax → ∞ and thus amax → ∞
fY (y, S = 0) = N0(y) 1 − Q1 µ(y),
√
ρmax
= N0(y)
fY (y, S = 1) = N1(y) exp −
a2
max
Pin
−
2bmaxyRe
σ2
z
I0
2bmax|y|
σ2
z
= 0
SEideal
(ξ) = log2 (1 + γξ)

If PA input power is low, i.e., ξ ≪ 1
fY (y, S = 0) = N0(y) 1 − Q1 µ(y),
√
ρmax
≈ N0(y)
fY (y, S = 1) = N1(y) exp −
a2
max
Pin
−
2bmaxyRe
σ2
z
I0
2bmax|y|
σ2
z
≈ N1(y)
SEIBO
(ξ) ≈ log2 (1 + γξ) + e− 1
ξ log2 πσ2
z e1− 1
ξ − log2 σ2
z

Theorem 1. The approximated SE, SEIBO
(ξ), is a concave function over
max 0, − 1
ln πσ2
z
< ξ ≤ 1
2 .
Theorem 2. The SE-aware optimal power loading factor ξ⋆
SE which maximizes
SEIBO
(ξ) is obtained by the solution of the following equality:
γ
1 + γξ
= e−ξ−1
ξ−2
−ξ−1
+ 1 − ln πeσ2
z
Proposition 3. A closed form approximation of ξ⋆
SE is given by
ξ⋆
SE ≈ ξ⋆
SE
−1
W 1
ln(πeσ2
z )
where W(·) denotes the Lambert W function
[Chapeau-Blondeau and Monir, 2002]

Numerical Results on SE
Bandwidth: Ω = 10 MHz
Channel attenuation:
G − 128 + 10 log10 (d−α
)
[LTE, 2011]
- G = 5 dB: TRx feeder
loss + antenna gains
- d = 200 m: distance
between Tx and Rx
- α = 3.76: path loss
exponent
AWGN:
σ2
z = −174 dBm / Hz
PA: SM2122-44L
Pmax
out = 25 W
g = 55 dB
0 0.2 0.4 0.6 0.8 1
0
2
4
6
8
10
12
14
16
18
Power loading factor, ξ
SE(ξ),b/s/Hz
SEideal
(ξ)
SE(ξ)
SEIBO
(ξ)
SE(ξ⋆
SE)
: ideal SE with an ideal PA
: practical SE with a practical PA
: approximated SE
: optimal SE
Figure 17: SE evaluation results.

Power Consumption And Losses
Power
Amplifier (PA)
Module
DC
Power
Supply
(PS)
Module
Base Band
(BB) Module
Radio
Frequency
(RF) Module
(except PA)
Active Cooler and Battery Back up (CB) Module (if present)
PBB PRF PPA
PCB
Pin Pout
loss
loss
loss
loss
Figure 18: Block diagram for system power consumption
[Auer et al., 2011, Arnold et al., 2010, Deruyck et al., 2010, Deruyck et al., 2011,
Kumar and Gurugubelli, 2011].
Pfix (1 + CPS)(1 + CCB)(PBB + PRF)
CPS: power supply coefficient between 0.1 and 0.15
CCB: active cooler and battery coefficient less than 0.4

PA-Dependent Nonlinear Power
Consumption Model
PA-dependent nonlinear power consumption model (0 < ξ ≤ 1):
Pc(ξ) = Pfix + c c1 + c2 ξ Pmax
out
- c: power loading-dependent scaling coefficient
- ℓ-way Doherty PA [Raab, 1987] (for class-A and B, ℓ = 1)
(c1, c2) =
4
ℓπ
×



(2, 0) , 0 < ξ ≤ 1,
(0, 1) , 0 < ξ ≤ 1
ℓ2 ,
(−1, ℓ + 1) , 1
ℓ2 < ξ ≤ 1.
Ideal power consumption model with ideal PA (100% efficiency):
Pideal
c (ξ) = Pfix + c 1 − g−1
ξPmax
out

Power Consumption
0 0.2 0.4 0.6 0.8 1
60
90
120
150
180
210
240
Powerconsumption,Pc(ξ),W
idle mode
linear model
nonlinear model with class B PA
nonlinear model with 2-way Doherty PA
model with ideal PA
Figure 19: Power consumption of microcell base station with Pﬁx = 130 W and
c = 4.7π
4
.

Analytical Results on EE
Practical EE
EE(ξ) =
ΩSE(ξ)
Pc(ξ)
Ideal EE with a perfectly linear and eﬃcient PA
EEideal
(ξ)
ΩSEideal
(ξ)
Pideal
c (ξ)
PA-dependent EE with a perfectly linear PA
EElinear
(ξ)
ΩSEideal
(ξ)
Pc(ξ)

Analytical Results on EE
Theorem 4. EElinear
(ξ) is a piecewise quasi-concave function over
ξ ≥ ζ v +
√
1 + v2
2
/γ2
, where v = Pmax
out c0c2/(P0 + Pmax
out c0c1).
Speciﬁcally, EElinear
(ξ) is quasi-concave over ζ ≤ ξ ≤ 1/ℓ2
and also over
1/ℓ2
< ξ ≤ 1.
Theorem 5. Assuming ξ⋆
EE ≥ ζ, ξ⋆
EE equals either [ξ⋆
1 ]
1/ℓ2
ζ or [ξ⋆
2 ]1
1/ℓ2,, where ξ⋆
1
and ξ⋆
2 are the solutions of ∂EElinear
(ξ)
∂ξ = 0 with given (c1, c2).
Proposition 6. A closed form approximation of ξ⋆
EE, denoted by ξ⋆
EE, equals
either [ξ⋆
1 ]
1/ℓ2
ζ or [ξ⋆
2 ]1
1/ℓ2,, where ξ⋆
i (i ∈ {1, 2}) approximates ξ⋆
i and is given by
ξ⋆
EE = ξ⋆
i ≈ ξ⋆
i =
1
γ
exp 2 + 2W
√
γ
ev
, i = 1 or 2.
where W(·) > 0 as
√
γ
ev > 0, so that W(·) is unique.

Numerical Results on EE
0 0.2 0.4 0.6 0.8 1
0
0.5
1.0
1.5
2.0
2.5
EE(ξ),Mb/J
EElinear
(ξ⋆
EE);× EE(ξ⋆
EE); ξ⋆
EE in Prop. 6
EElinear
(ξ)
EEideal
(ξ)
EE(ξ): 2-way Doherty PA
EE(ξ): class-B PA
EE(ξ): class-A PA
Figure 20: EE evaluation with Pﬁx = 130 W and c = 4.7.

0 2 4 6 8 10 12 14 16 18
0
0.5
1.0
1.5
2.0
2.5
SE(ξ), b/s/Hz
EE(ξ),Mb/J
SE-EE tradeoff with PAlow
SE-EE tradeoff with PAhigh
◦
×
: (SE(ξ⋆
SE), EE(ξ⋆
SE))
: (SE(ξ⋆
EE), EE(ξ⋆
EE))
Fig. 24
Figure 21: SE-EE tradeoff with 2-way Doherty PAs. PAlow
with Pmax
out = 25 W and
g = 55 dB, and PAhigh
with Pmax
out = 100 W and g = 50 dB.

PA Switching (PAS) Method
SE
EE Low power PA, PA-1
SE
EE High power PA, PA-2
Figure 22: Illustration of PAS Concept.

PA Switching (PAS) Method
SE
EE Low power PA, PA-1
SE
EE High power PA, PA-2
SE
EE PA switching
Figure 22: Illustration of PAS Concept.

PAS for FDD/TDD Frame
switching time
ǫ
frame k − 1, PA-1 frame k, PA-1 frame k + 1, PA-2
time
· · ·· · ·
(a) FDD Systems
switching time
ǫ
DL frame, PA-1 UL frame DL frame, PA-2
UL frame length + TTG + RTG > ǫ
time
· · ·· · · TTG RTG
(b) TDD Systems
Figure 23: Illustration of PA switching between PA-1 and PA-2.
K: frame number; T: frame length; ǫ: switching time

SE and EE of PAS
SEs(ξ, κ) =
kT SE′
1(ξ) + (K − k)T SE′
2(ξ)
KT + ǫ
EEs(ξ, κ) =
KT ΩSEs(ξ, κ)
kT P1
c (ξ) + (K − k)T P2
c (ξ)
κ = k
K is PA time sharing factor.
Switch power consumption is ignored as it is relatively negligible compared
to Pi
c (ξ).
FDD: ǫ > 0 unless there is switching.
TDD: ǫ < 1 ms and UL frame length (10 ms) [LTE, 2011]; PAs can be
switched between consecutive DL frames while receiving UL frame, without
consuming any switching time overhead, i.e., ǫ = 0.

SE-EE Tradeoff of PAS
ǫ = 10 µs, LS = 1 dB,
T = 10 ms, and
K = 20
A → B(D) :
EE ↑ 210%(41%)
SE ↓ 12%
A → C(E) :
EE ↑ 323%(69%)
SE ↓ 15%
14.0 14.5 15.0 15.5 16.0 16.5 17.0 17.5
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
SE(ξ), b/s/Hz
EE(ξ),Mb/J
GS = 0 dB, ǫ = 0 µs, ideal
GS = 1 dB, ǫ = 0 µs, TDD
GS = 1 dB, ǫ = 10 µs, FDD
GS = 1 dB, ǫ = 1 ms, FDD
A
DE
B
C
SE-EE tradeoff with PAhigh
Figure 24: SE-EE tradeoff.

TAS-MRC Syst. with Partial CSIT
x
z1
z2
y1
MRC
ˆx
y2
√
Ah2
√
Ah1S1
M +1
feedback for S1 and PC
PC
power contorl
1
2
: high-power main power ampliﬁers
Figure 25: A transmit antenna selection with maximum ratio combining system.
Switch S1 selects a transmit antenna
Switch S2 selects a PA [Joung et al., 2013]
Pmax
out : maximum output power of TX subject to regulatory constraints
µmPmax
out : Maximum output power of PA m
0 < µ1 < µ2 < · · · < µM+1 = 1

TAS-MRC Syst. with Partial CSIT
x
z1
z2
y1
MRC
ˆx
y2
√
Ah2
√
Ah1S1S2
oﬀ − mode
M +1
M
1
... feedback for S1 and S2
0
M + 1
M
1
1
2
: low-power auxiliary power ampliﬁers
Figure 25: A transmit antenna selection with maximum ratio combining system.
Switch S1 selects a transmit antenna
Switch S2 selects a PA [Joung et al., 2013]
Pmax
µmPmax
out : Maximum output power of PA m
0 < µ1 < µ2 < · · · < µM+1 = 1

PA Switching Probability
A switching probability for given target rate R b/s/Hz
fm = Pr(Rm−1 < R ≤ Rm)
= (Υm−1 − Υm) (Υm−1 + Υm − 2)
by using order statistics [David, 1970, Proakis and Salehi, 2007]
- Υm 2R
− 1 σ2
m + 1 e−(2R
−1)σ2
m
- σ2
m
σ2
z
AµmP max
out
, where σ2
z is variance of AWGN
- Υ0 = 0 and ΥM+2 = 1
EE of the proposed TAS-MRC systems for the given R
EETAS
MRC =
ΩR(1 − fM+2)
M+2
m=1 PTx,mfm
- PTx,m
100cµmP max
out
ǫ(µm)
+ Pfix, if m = 1, . . . , M + 1,
Pfix, if m = M + 2 (off-mode)

PA Output Power (Pout W) and
Efficiency (ǫ%)
Table 5: Proposed PAS TAS-MRC with M = 2.
PAS m PA Pout W ǫ% FB s1 s2 fm
Auxiliary PA
1 PA1 0.63 W 55% 110/1 1/2 1 f1
2 PA2 2.5 W 43% 010/1 1/2 2 f2
Main PA 3 PA3 10 W 60% 100/1 1/2 3 f3
off-mode 4 off-mode 0 0 000 – 0 f4
Table 6: Conventional TAS-MRC System with Power Control.
Level l PA Pout W ǫ% feedback s1 fpow
l
P1 1 PA3 0.63 W 9% 110/1 1/2 fpow
1
P2 2 PA3 2.5 W 32% 010/1 1/2 fpow
2
P3 3 PA3 10 W 60% 100/1 1/2 fpow
3
P4 4 off-mode 0 0 000 – fpow
4

Mode Switching Probability
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
R b/s/Hz
Probability,fm
f1:PA1
Figure 26: Switching probabilities.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
R b/s/Hz
Probability,fm
f2:PA2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
R b/s/Hz
Probability,fm
f3:PA3

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
R b/s/Hz
Probability,fm
f4:PA4, oﬀ-mode

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
R b/s/Hz
Probability,fm
f1:PA1
f2:PA2
f3:PA3
f4:PA4, oﬀ-mode

EE Comparison
Simulation
Environments
PA1(0.63 W, 55%),
PA2(2.5 W, 43%),
PA3(10 W, 60%)
8 dB IBO
Switch insertion loss:
GS = 0 dB for S1,
GS = 1 dB for S2
A dB = G − 128 +
10 log10(d−α)
[LTE, 2011]
G = 5 dB,
d = 0.6 km, α = 3.76
σ2 = −174 dBm / Hz,
Ω = 10 MHz,
Pﬁx = 40 W and
c = 4.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
1.2
R b/s/Hz
Energyeﬃciency(EE)Mb/J
no power control
Figure 27: EE comparison.

EE Comparison
Simulation
Environments
PA1(0.63 W, 55%),
PA2(2.5 W, 43%),
PA3(10 W, 60%)
8 dB IBO
GS = 0 dB for S1,
GS = 1 dB for S2
A dB = G − 128 +
10 log10(d−α)
[LTE, 2011]
G = 5 dB,
d = 0.6 km, α = 3.76
σ2 = −174 dBm / Hz,
Ω = 10 MHz,
Pfix = 40 W and
c = 4.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
1.2
R b/s/Hz
1-bit FB for off-mode
off-mode
gain

EE Comparison
Simulation
Environments
PA1(0.63 W, 55%),
PA2(2.5 W, 43%),
PA3(10 W, 60%)
8 dB IBO
GS = 0 dB for S1,
GS = 1 dB for S2
A dB = G − 128 +
10 log10(d−α)
[LTE, 2011]
G = 5 dB,
d = 0.6 km, α = 3.76
σ2 = −174 dBm / Hz,
Ω = 10 MHz,
Pﬁx = 40 W and
c = 4.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
1.2
R b/s/Hz
2-bit FB for Pow Ctrl
oﬀ-mode
power
control
gain
gain

EE Comparison
Simulation
Environments
PA1(0.63 W, 55%),
PA2(2.5 W, 43%),
PA3(10 W, 60%)
8 dB IBO
GS = 0 dB for S1,
GS = 1 dB for S2
A dB = G − 128 +
10 log10(d−α)
[LTE, 2011]
G = 5 dB,
d = 0.6 km, α = 3.76
σ2 = −174 dBm / Hz,
Ω = 10 MHz,
Pﬁx = 40 W and
c = 4.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
0.2
0.4
0.6
0.8
1.0
1.2
R b/s/Hz
2-bit FB for Pow Ctrl
2-bit FB for PAS

MIMO Syst. with Partial CSIT
x2
x1
z2
z1
y2
y1
MRC/MIMO
ˆx2
ˆx1
√
Ah2
√
Ah1
M +1
M +1 Ant.2
Ant.1
PC
PC
power contorl
feedback for PC
Figure 28: PAS MIMO system model with two main PA and M auxiliary PAs.
Switch S0 selects a communication mode
Switch S1 selects a PA [Joung et al., 2014d]
µmPmax
out : Maximum output power of PA m ∈ {1, · · · , M + 1}
0 < µ1 < · · · < µM < µM+1 = 1
2Pmax

MIMO Syst. with Partial CSIT
x2
x1
z2
z1
y2
y1
MRC/MIMO
ˆx2
ˆx1
√
Ah2
√
Ah1S0
S1
M +1
M +1
M
1
Ant.2
Ant.1
0
0
M + 1
M
1
1
: low-power auxiliary power ampliﬁers
feedback for S1 and S2
...
oﬀ-mode
Figure 28: PAS MIMO system model with two main PA and M auxiliary PAs.
Switch S0 selects a communication mode
Switch S1 selects a PA [Joung et al., 2014d]
µmPmax
out : Maximum output power of PA m ∈ {1, · · · , M + 1}
0 < µ1 < · · · < µM < µM+1 = 1
2Pmax

Commun. Mode Selection Prob.
A switching probability for given target rate R b/s/Hz
- PAS-MRC mode:
fm = fU (2R
− 1)σ2
m ≤ u < (2R
− 1)σ2
m−1 , m = {1, · · · , M}
= Υm − Υm−1
where Υm 1 + 2R
− 1 σ2
m e−(2R
−1)σ2
m .
- Oﬀ-mode: fM+3 fC(x < R|σ2
M+1) = FC(R|σ2
M+1)
FC(x|σ2
M+1)=
∞
−∞
∞
0
e−x
1+
x
2σ2
M+1
j2πu
ln 2
dx
∞
0
x2
e−x
1+
x
2σ2
M+1
j2πu
ln 2
dx
−
∞
0
xe−x
1+
x
2σ2
M+1
j2πu
ln 2
dx
2
1 − ej2πux
j2πu
du.
- MIMO mode: fM+2 = 1 −
M+1
m=1
fm − fM+3

EE Analysis
EE can be derived as EEPAS
MIMO = ΩR(1−fM+3)
M+3
m=1
PTx,mfm
.
- Numerator represents system throughput over bandwidth Ω Hz and given
target rate R
- Denominator is total power consumption with model defined as
[Joung et al., 2013, Héliot et al., 2012]:
PTx,m =



Pant,m + Psp1Ω + Pfix, if m = 1, · · · , M + 1,
2Pant,m + Psp1Ω + Pfix, if m = M + 2,
Pfix, if m = M + 3
+ Pant,m: power consumption that is proportional to the number of transmit
antennas NT [Xu et al., 2011], as Pant,m =
cµmP max
out
ǫ(µm)
+ Pcc + Psp2Ω
+ c: a system dependent power loss coefficient which can be empirically
measured and obtained
+ ǫ(µm): a PA efficiency that depends on the input power (or equivalently PA
output power)
+ Pcc and Psp2 are the RF circuit and signal processing related power
consumptions per bandwidth, respectively, which are proportional to NT
+ µ4 = µ3
+ Psp1: a sig. proc.g related power consumption which is independent of NT
+ Pfix: a fixed power consumption, for power supply and cooling system, which
is independent of P max
out , NT , and Ω

PA Output Power (Pout W) and
Efficiency (ǫ%)
Table 7: Proposed PAS MIMO with M = 2.
Comm. Mode m PA Pout W ǫ% FB s0 s1 fm
PAS-MRC
1 PA1 0.63 W 55% 00 0 1 f1
2 PA2 2.5 W 43% 01 0 2 f2
3 PA3 10 W 60% 10 0 3 f3
MIMO 4 two PA3’s 2 × 10 W 60% 11 1 3 f4
off-mode 5 PA4 0 0 null 0 0 f5
Table 8: Conventional MIMO System with Power Control.
Level l PA Ptotal
out W ǫ% feedback fpow
l
P1 1 PAa
3, PAb
3 2 × 0.315 W 5% 00 fpow
1
P2 2 PAa
3, PAb
3 2 × 1.25 W 15.5% 01 fpow
2
P3 3 PAa
3, PAb
3 2 × 5 W 32% 10 fpow
3
P4 4 PAa
3, PAb
3 2 × 10 W 60% 11 fpow
4
P5 5 off 0 0 null fpow
5

0 2 4 6 8 10 12 14 16 18 20 22 24
0
0.2
0.4
0.6
0.8
1.0
Target rate, R b/s/Hz
Probability,fm
m = 1, PA1: 0.63 W
m = 2
m = 3
m = 4
PA2
2.5 W
PA3
10 W
PA3+PA3
2 × 10 W
m = 5, PA4: oﬀ-mode
(a)
0 2 4 6 8 10 12 14 16 18 20 22 24
0
0.2
0.4
0.6
0.8
1.0
Target rate, R b/s/HzProbability,fpow
l
l = 5, P5: oﬀ-model = 1, P1: 2 × 0.315 W
l = 2, P2
2 × 1.25 W
l = 3, P3
2 × 5 W
l = 4, P4
2 × 10 W
(b)
Figure 29: Probabilities. (a) fm of PAS. (b) fpow
l of power control.

EE Comparison
Simulation Environments
GS = 0 dB for S0,
GS = 0 dB for S1
σ2
= −174 dBm/ Hz,
Ω = 5 MHz, with 512
FFT size (300
subcarriers)
Pfix = 36.4 W,
c = 2.63,
Pcc = 66.4 W,
Pfix = 36.4 W,
Psp1 = 1.28 µW / Hz,
Psp2 = 3.32 µW / Hz
0 2 4 6 8 10 12 14 16 18 20 22 24
0
0.05
0.10
0.15
0.20
0.25
Energyefficiency(EE)Mb/J Conventional MIMO w/o Pow Ctrl.
Figure 30: EE comparison of the proposed
MIMO-MRC with PAS and the
conventional MIMO w/ or w/o Pow Ctrl.

EE Comparison
GS = 0 dB for S0,
GS = 0 dB for S1
σ2
= −174 dBm/ Hz,
FFT size (300
subcarriers)
Pfix = 36.4 W,
c = 2.63,
Pcc = 66.4 W,
Pfix = 36.4 W,
Psp1 = 1.28 µW / Hz,
Psp2 = 3.32 µW / Hz
0 2 4 6 8 10 12 14 16 18 20 22 24
0
0.05
0.10
0.15
0.20
0.25
Energyefficiency(EE)Mb/J Conventional MIMO w/ Pow Ctrl.

EE Comparison
GS = 0 dB for S0,
GS = 0 dB for S1
σ2
= −174 dBm/ Hz,
FFT size (300
subcarriers)
Pﬁx = 36.4 W,
c = 2.63,
Pcc = 66.4 W,
Pﬁx = 36.4 W,
Psp1 = 1.28 µW / Hz,
Psp2 = 3.32 µW / Hz
0 2 4 6 8 10 12 14 16 18 20 22 24
0
0.05
0.10
0.15
0.20
0.25
Conventional MIMO w/ Pow Ctrl.
Proposed MIMO w/ PAS

Index

System Model [Joung et al., 2014a]
BBU
deactivated DA
user equipment
SISO MISO MU-MIMO
activated DA
optical fibre
large-scale
distributed antennas
(DAs)
cellular
communication
networks

EE Model of L-DAS
EE (S, W, P )
System throughput per unit time
Total power consumption
System throughput: R(S, W, P ) = u∈U Ω log2 (1 + SINRu(S, W, P ))
SINRu(S, W, P ) =
|hr
u(sc
u◦wc
u)|2
puu
U
u′=1,u′=u
hr
u sc
u′ ◦wc
u′
2
pu′u′ +σ2
Total power consumption: C(S, W, P ) = f(S, W, P ) + g(S, W )
f(·): TPD (transmit power dependent) term
g(·): TPI (transmit power independent) term

Power Consumption Model: TPD
ASIC, FPGA, DSP
Examples:
-digital up converter
-digital predistorter
-scrambling
-CRC check
-conv. encoder
-interleaver
-modulation
-IFFT
-CP insertion
-parallel-to-serial
eRF module oRF module oRF module eRF module
Examples:
-O/E
converter
Examples:
-E/O converter
-laser
-driver
-modulator
Examples:
-D/A converter
-filters
-synthesizer
Examples:
-VGA
-driver
-PA
Examples:
-AC/DC
-DC/DC
-active
cooler
...
... ...
...
...
...
baseband module
mth RF module at BBU
baseband unit (BBU)
mth distributed antenna (DA) port
1st Ant.
mth Ant.
Mth Ant.
1
m
M
ﬁber
Psp1, Psp2, Psig (TPI)
Pcc1,m (TPI) Pcc2,m (TPI)Pcc2,m (TPI)
Pﬁx
(TPI)
Ptx,m (TPD)
TPD term
f(S, W, P ) =
m∈M
c
ηm
(S ◦ W )P (S ◦ W )H
mm
eRF (electric RF)
oRF (optical RF)

Power Consumption Model: TPI
TPI term
g(S, W ) = grf(S) + gbb(W ) + gnet(M) + Pfix
grf(S) = m∈M
Pcc1,m + Pcc2,m u∈U
Ru maxu smu
gbb(W ) = ΩPsp1 [dim(W )]β+1
+ ΩPsp2
gnet(M) = MΩPsig
Pcc1: eRF
Pcc2: per unit-bit-and-second of oRF
Ru: target rate of user u
β ≥ 0: implies overhead power consumption of MU processing compared to
SU-MIMO
Pfix: fixed power consumption (e.g., pow supply, AC/DC, DC/DC, and
cooling system)

EE of L-DAS
EE (S, W, P ) =
u∈U
Ω log2 1 +
|hr
u (sc
u ◦ wc
u)|
2
puu
U
u′=1,u′=u |hr
u (sc
u′ ◦ wc
u′ )|2
pu′u′ + σ2
m∈M
c
ηm
(S ◦ W )P (S ◦ W )H
mm
+
m∈M
Pcc1,m + Pcc2,m
u∈U
Ru max
u
smu
+ ΩPsp1 [dim(W )]
β+1
+ ΩPsp2
+ MΩPsig

Original Problem Formulation
Po: original problem
max
{S,W,P }
EE (S, W, P )
s.t. (S ◦ W )P (S ◦ W )H
mm
≤ Pm, ∀m ∈ M,
Ru(S, W, P ) ≥ Ru, ∀u ∈ U,
pu1u2 = 0, ∀u1 = u2 ∈ U,
smu ∈ {0, 1}, ∀m ∈ M, ∀u ∈ U
objective function: EE
per-ant pow constraints with max-output pow Pm
per-user rate constraints with a target rate Ru
diagonal structure of P
DA selection

Problem Decomposition
Issues to solve the original problem Po:
Non-convex objective function
Integer variables {smu} in the constraints
Signaling overhead
Computational complexity
Suboptimal decomposition approach
Step 1: DA selection: S
Step 2: DA clustering
Step 3: Cluster-based optimization (cluster index ℓ)
Step 3-1: Precoding, Wℓ
Step 3-2: Power allocation, Pℓ

Step 1: DA Selection
20 UEs
activated DA: Mu = 1
non-activated DA: M = 400
IAD
BBU
Channel-gain-based greedy AS: received signal strength indicator (RSSI)
Min-dis-based greedy AS: localization information

Step 2: Selected-DA Clustering
SINR-threshold γ based clustering
If UE u′
’s distance from cluster ℓ is shorter than γ, UE u′
is included to the
cluster ℓ as follows:
Uℓ = Uℓ ∪ {u′
}, if D(Uℓ, u′
) ≤ γ
Distance metric between two clusters
D(Uℓ, Uℓ′ ) min
u∈Uℓ,u′∈Uℓ′
d(u, u′
).
d(u, u′
) min
m∈Mu
|hum|2Pm
σ2 + m′∈Mu′
|hum′ |2Pm′
,
m′∈Mu′
|hu′m′ |2Pm′
σ2 + m∈Mu
|hu′m|2Pm

DA Clustering Example
cluster with a single UE
cluster with four UEs
(a) γ = 25 dB, L = 11 (b) γ = 32 dB, L = 6
γ ↑ =⇒ cluster size ↑, number of clusters ↓, inter-cluster interference ↓
γ ↓ =⇒ cluster size ↓, number of clusters ↑, inter-cluster interference ↑

DA Clustering Example
cluster with a single UE
cluster with four UEs
(a) γ = 25 dB, L = 11 (b) γ = 32 dB, L = 6
SINR between users in diﬀerent clusters > γ
=⇒ cluster-based optimization
SINR between users within a cluster ≤ γ
=⇒ MU-MIMO precoding

Step 3: Cluster-based Optimization
Problem
Pℓ: cluster-ℓ problem for given S∗
ℓ , ℓ ∈ L
max
{Wℓ,Pℓ}
EE (S∗
ℓ , Wℓ, Pℓ)
s.t. (S∗
ℓ ◦Wℓ)Pℓ(S∗
ℓ ◦Wℓ)H
mm
≤ Pm,∀m∈Mℓ,
Ru(S∗
ℓ , Wℓ, Pℓ) ≥ Ru, ∀u ∈ Uℓ,
pu1u2 = 0, ∀u1 = u2 ∈ Uℓ
Maximization of EE upper bound
Computational complexity reduction
CSI and signaling overhead reduction:
e.g., 8000 ⇒ 48 and 160 in (a) and (b)

Pℓ,1: Precoding matrix W ∗
ℓ for ﬁxed S∗
ℓ and P ′
ℓ
max
{Wℓ,Pℓ}
EE (Wℓ, P ′
ℓ)
s.t. (S∗
ℓ ◦W ∗
ℓ )Pℓ(S∗
ℓ ◦W ∗
ℓ )H
mm
≤ Pm, ∀m ∈ Mℓ
Ru(S∗
ℓ , W ∗
ℓ , Pℓ) ≥ Ru, ∀u ∈ Uℓ
pu1u2 = 0, ∀u1 = u2 ∈ Uℓ

Pℓ,2: Power control matrix P ∗
ℓ for ﬁxed S∗
ℓ and W ∗
ℓ
max
{Wℓ,Pℓ}
EE (W ∗
ℓ , Pℓ)
s.t. (S∗
ℓ ◦W ∗
ℓ )Pℓ(S∗
ℓ ◦W ∗
ℓ )H
mm
≤ Pm, ∀m ∈ Mℓ
Ru(S∗
ℓ , W ∗
ℓ , Pℓ) ≥ Ru, ∀u ∈ Uℓ
pu1u2 = 0, ∀u1 = u2 ∈ Uℓ

ZF-MU-MIMO Precoding Design
ℓ for ﬁxed S∗
ℓ and P ′
ℓ
W ∗
ℓ = max
Wℓ
EE(S∗
ℓ , Wℓ, P ′
ℓ )
(a)
≡ min
Wℓ
C(S∗
ℓ , Wℓ, P ′
ℓ )
(b)
≡ min
Wℓ
c
m∈Mℓ
1
ηm
(S∗
ℓ ◦ Wℓ)P ′
ℓ (S∗
ℓ ◦ Wℓ)H
mm
(c)
≡ min
Wℓ
m∈Mℓ
(S∗
ℓ ◦ Wℓ)P ′
ℓ (S∗
ℓ ◦ Wℓ)H
mm
(a) rate does not depend on Wℓ due to a ZF property
(b) Wℓ aﬀects only on TPD term for given S∗
ℓ
and P ′
ℓ
(c) equal capability of PA is preferred for high EE [Joung and Sun, 2013],
c/ηm is assumed to be a constant

Cont.: Reformulation & Solution
ℓ for ﬁxed S∗
ℓ and P ′
ℓ
W ∗
ℓ = min
Wℓ
m∈Mℓ
(S∗
ℓ ◦ Wℓ)P ′
ℓ (S∗
ℓ ◦ Wℓ)H
mm
= min
Wℓ
Sd
ℓ Wℓ P ′
ℓ
2
F
Sd
ℓ
= diag(sc,∗
ℓ
) ∈ RM×M
sc,∗
ℓ
= u∈Uℓ
sc,∗
u
ZF-MU-MIMO precoding matrix as
Wℓ = (HℓSd
ℓ )†
+ null(HℓSd
ℓ )Aℓ
Hℓ ∈ CUℓ×M : a channel matrix of cluster ℓ that consists of row vectors hr
u, u ∈ Uℓ
Aℓ ∈ CUℓ×Uℓ is a Uℓ-dimensional arbitrary matrix

Cont.: Reformulation & Solution
ℓ for ﬁxed S∗
ℓ and P ′
ℓ
W ∗
ℓ = min
Wℓ
m∈Mℓ
(S∗
ℓ ◦ Wℓ)P ′
ℓ (S∗
ℓ ◦ Wℓ)H
mm
= min
Wℓ
Sd
ℓ Wℓ P ′
ℓ
2
F
Sd
ℓ
= diag(sc,∗
ℓ
) ∈ RM×M
sc,∗
ℓ
= u∈Uℓ
sc,∗
u
ZF-MU-MIMO precoding matrix as
W ∗
ℓ = (HℓSd
ℓ )†
+ null(HℓSd
ℓ )Aℓ
Hℓ ∈ CUℓ×M : a channel matrix of cluster ℓ that consists of row vectors hr
u, u ∈ Uℓ
Aℓ ∈ CUℓ×Uℓ is a Uℓ-dimensional arbitrary matrix

Power Control
ℓ for ﬁxed S∗
ℓ and W ∗
ℓ
P ∗
ℓ = max
Pℓ
EE(S∗
ℓ , W ∗
ℓ , Pℓ)
s.t. (Sd
ℓ W ∗
ℓ )Pℓ(Sd
ℓ W ∗
ℓ )H
mm
≤ Pm, ∀m ∈ Mℓ
Ru(Pℓ) ≥ Ru, ∀u ∈ Uℓ
pu1u2 = 0, ∀u1 = u2 ∈ Uℓ.
u∈Uℓ
Ru(Pℓ) ≥ ξC(S∗
ℓ , W ∗
ℓ , Pℓ)
Using an additional variable ξ
Convex feasibility problem
Bisection search to ﬁnd the optimal ξ

Power Control
ℓ for ﬁxed S∗
ℓ and W ∗
ℓ
P ∗
ℓ = max
Pℓ
ξ
s.t. (Sd
ℓ W ∗
ℓ )Pℓ(Sd
ℓ W ∗
ℓ )H
mm
≤ Pm, ∀m ∈ Mℓ
Ru(Pℓ) ≥ Ru, ∀u ∈ Uℓ
pu1u2 = 0, ∀u1 = u2 ∈ Uℓ.
u∈Uℓ
Ru(Pℓ) ≥ ξC(S∗
ℓ , W ∗
ℓ , Pℓ)
Using an additional variable ξ
Convex feasibility problem
Bisection search to ﬁnd the optimal ξ

Cont.: Heuristic Method
Minimum required power to satisfy Ru with ZF-MU-MIMO precoding
pu,ℓ = σ2
2
Ru
Ω − 1
pu,ℓ is a ratio based on minimum required power for target rate
[Joung and Sun, 2013] s.t.
pu,ℓ =
pu,ℓ
k∈Uℓ
pk,ℓ
, ∀u ∈ Uℓ
pu,ℓ = αℓpu,ℓ
αℓ: common power scaling factor for power limit of PAs and target rate of
UEs in cluster ℓ
Maximize EE lower bound

Cont.: Heuristic Method
§
¦
¤
¥
P′
ℓ,2 : α∗
ℓ = arg max
αℓ
ζ(αℓ), s.t. αLB,ℓ ≤ αℓ ≤ αUB,ℓ
ζ(αℓ) =
ΩUℓ log2(1+c1,ℓαℓ)
c2,ℓαℓ+c3,ℓ
: concave function
c1,ℓ minu{pu,ℓ}σ−2
c2,ℓ c m∈Mℓ
η−1
m [Sd
ℓ W ∗
ℓ P ℓ(Sd
ℓ W ∗H
ℓ )H
]mm
c3,ℓ = m∈Mℓ
(Pcc1,m + Pcc2,m u∈Uℓ
Ru) maxu∈Uℓ
smu +
ΩPsp1[dim(Wℓ)]β+1
+ MℓΩPsig + ΩPsp2/L + Pﬁx/L
αLB,ℓ = σ2
2
Ru
Ω − 1 pu,ℓ from QoS constraint
αUB,ℓ = minm∈Mℓ
Pm Sd
ℓ W ∗
ℓ P ℓ(Sd
ℓ W ∗
ℓ )H
mm
from power constraint

Cont.: Solution
Closed form solution:
αo =
1
c1
exp 1 + W −
1
exp(1)
+
c1c3
c2exp(1)
− 1
α∗
= [αo]
αUB
αLB
P ∗
heuristic = α∗
P

Algorithms
Algorithm 1 : Clustering threshold γ adaptation
1. Initial setup: γ, δ > 0, stop= 0, q = 0, Mu, and QC ≥ 0
2. compute EEp with γ: Mu Adaptation Algorithm 2
3. compute EEc with γ = γ + δ: Mu Adaptation Algorithm 2
4. if EEc > EEp then
5. EEp = EEc and ξ = 1
6. else
7. compute EEc with γ = γ − 2δ: Mu Adaptation Algorithm 2
8. if EEc > EEp then EEp = EEc and ξ = −1
9. else stop= 1 end if
10. end if
11. while stop= 0 & q < QC do
12. compute EEc with γ = γ + ξδ: Mu Adaptation Algorithm 2
13. if EEc > EEp then EEp = EEc
14. else stop= 1 end if
15. q = q + 1
16. end while

Algorithms
Algorithm 2 : Mu adaptation algorithm for AS
1. Initial setup: Mu = 1, ∀u ∈ U, q = 0, γ, and QAS ≥ 0
2. while feasibility= 0 & q < QAS do
3. DA Selection Algorithm 3
4. DA Clustering Algorithm 4 with a threshold γ
5. feasibility= 1
6. for cluster ℓ = 1, . . . , L do
7. precoding: Wℓ = (HℓSd
ℓ
)†
8. power control: Bisection Search Algorithm 5 or P ∗
heuristic
= α∗
9. if power control is infeasible & u∈U
Mu < M
10. then add one additional DA to UE u ∈ Uℓ who has the weakest channel gain, i.e.,
Mu = Mu + 1 where u ∈ Uℓ s.t., u = arg min
u∈Uℓ
|hum|
11. feasibility= 0 end if
12. end for
13. q = q + 1
14. end while

Algorithms
Algorithm 3 : CGB/MDB-Greedy DA selection algorithm
1. Initial setup: U = {1, . . ., U}, M = {1, . . ., M}, smn = 0, ∀m ∈ M, ∀u ∈
U, Mu = ∅, ∀u ∈ U, and given Mu’s
2. while U = ∅ do
3. ﬁnd {m∗
, u∗
} = arg metric
m∈M,u∈U
‘metric’ = max |hum| for CGB, min dum for MDB
4. set sm∗u∗ = 1, M = M m∗
and Mu∗ = M ∪ m∗
5. if |Mu∗ | = Mu∗ then
6. U = U u∗
7. end if
8. end while

Algorithms
Algorithm 4 : DA clustering algorithm
1. Initial setup: distance do = 0, clusters Uℓ = {ℓ} where ℓ ∈ L = {1, . . ., U},
and given γ
2. while do < γ do
3. ﬁnd the distance of most closest pair of clusters Uℓ and Uℓ′ , i.e., do =
minℓ,ℓ′∈L,ℓ=ℓ′ D(Uℓ, Uℓ′ )
4. if do < γ then
5. merge clusters as Uℓ = Uℓ ∪ Uℓ′
6. update L
7. end if
8. end while

Algorithms back
Algorithm 5 : Bisection search algorithm: Per-cluster optimal power control for
MU
1. setup: ξLB = 0, ξUB ≃ ∞, and a tolerance value, δ > 0
2. while ξUB − ξLB > δ do
3. ξ ← (ξUB − ξLB)/2
4. Solve convex feasibility problem and ﬁnd (update) P ∗
ℓ
5. if infeasible then ξUB ← ξ
6. else ξLB ← ξ end if
7. end while
8. P ∗
optimal,ℓ = P ∗
ℓ

Simulation Parameters
Cell model two square grid cells (1km2
)
Number of DAs/CAs 25 ≤ M ≤ 900
Intra-antenna distance (IAD) from 33 m to 200m
Number of UEs 2 ≤ U ≤ 20
UE distribution Uniform (104
−realization)
Path loss model (fc = 2GHz) Aum = g − 128 + 10 log10(d−µ
u,m)
feeder loss and antenna gain g = 5dB
Path loss exponent µ = 3.76
Small scale fading hum ∼ CN(0, 1)
Bandwidth Ω = 10MHz
Target rate Ru = 10Mb
Maximum Tx power Pm = 17dBm
AWGN standard deviation σ2
= −174dBm/Hz
Power loss coefficient c = 2.63
eRF circuit pow.cons. Pcc1,m = 5.7W
oRF circuit pow.cons. Pcc2,m = 0.5/0pW/bit/s
Fixed pow.cons. Pfix = 34 W
Signal processing pow.cons. Psp1 = 0.94 × 1/1.1µW/Hz
Psp2 = 0.54 × 1/1.1µW/Hz
Signaling pow.cons./antenna 5 ≤ Psig ≤ 500nW/Hz
preprocessing pow.cons. ratio 0 ≤ β ≤ 2
PA efficiency ηm = 0.08/0.6
Clustering threshold −∞ ≤ γ ≤ ∞dB

Simulation Visualization
(outage: cell boundary)

Simulation Visualization
(no outage)

M =400, U =20, Psig =50nW/Hz
−10 0 10 20 30 40 50
0
1
2
3
4
5
6
Clustering threshold γdB
AverageenergyeﬃciencyMb/J
optimal power control
heuristic power control
L-CAS with β = 0.2
β = 0.2
β = 0.5
β = 1
−∞ ∞
fullSU,Uclusters
fullMU,singlecluster

M =400, β =0.5, Psig =50nW/Hz
2 4 6 8 10 12 14 16 18 20
0
1
2
3
4
Number of UEs, U
AverageenergyeﬃciencyMb/J
optimal pow ctrl with adaptation
heuristic pow ctrl with adaptation
γ = ∞dB (full MU, single cluster)
γ = −∞dB (full SU, U clusters)
γ = 22dB
L-CAS with β = 0.5

U = 20, β = 0.5, γ = 22 dB
0 100 200 300 400 500 600 700 800 900
0
1
2
3
4
5
6
Number of transmit DAs, M
AverageenergyeﬃciencyMb/J optimal power control
heuristic power control
Psig = 5nW/Hz
Psig = 50nW/Hz
Psig = 500nW/Hz
L-CAS with Psig = 5nW/Hz

Index

Summary
ICT consumes/emits 3%/2% of world-wide energy/CO2
Efficiency: valuable resources produced / valuable resources consumed
Energy efficiency (EE) in wireless communications: bits/Joule
Power amplifier (PA): LDMOS + Doherty/envelope tracking
Two fundamental characteristics of PA and the models:
linearity and efficiency
SE-EE tradeoff: EE maximization rather than SE maximization
EE-aware technologies based on three design perspectives
i) PA, ii) signal, iii) network
PAS and large-scale DAS
Further Work
- Multiuser communication systems
- Memory effects of PA for wideband systems
- Manufacturing cost and form factor of a PA bank
- Multi-band systems application, network-level approach
- ...

CTN Issue: 28 January 2015

Top 10 Trends in 2015, IEEE
ComSoc
1 5G (energy saving 1/1000)
2 FIBER EVERYWHERE
3 VIRTUALIZATION, SDN & NFV
4 EVERYWHERE CONNECTIVITY FOR IoT & IoE
5 COGNITIVE NETWORKS, BIG DATA
6 CYBERSECURITY
7 GREEN COMMUNICATIONS
- environmentally friendly batteries
- renewable energy sources
- intelligent management of the power systems
- total energy usage
- compute-to-consumption ratios
- performance KPIs for best in class green operations
8 SMARTER SMARTPHONES, CONNECTED SENSORS
9 NETWORK NEUTRALITY, INTERNET GOVERNANCE
10 MOLECULAR COMMUNICATIONS

Thank You
http://www1.i2r.a-star.edu.sg/∼jgjoung
jgjoung@i2r.a-star.edu.sg

Envelope Tracking Architecture B
the envelope tracking RF PA’s supply voltage is controlled by the envelope
of the RF-drive.
eﬃciency between the linear RF PA and the PAs using the Kahn technique.
Figure 31: Envelope tracking architecture [Raab et al., 2003c].

Doherty Technique B
Class-B (carrier PA or main PA) and class-C (peaking PA or auxiliary PA)
PAs are combined to improve eﬃciency
The class-B PA is activated if the signal amplitude is half or less than the
PEP
Both PAs are activated if the signal amplitude is larger than half of the
PEP [Doherty, 1936]
Due to the high eﬃciency and linearity over wide frequency band and
power level, Doherty technique is widely used for the modern cellular
communications [Correia et al., 2010, Cha et al., 2004, Auer et al., 2011,
Arnold et al., 2010].
RF-drive input output
main PA
auxiliary PA
Figure 32: Doherty transmitter.

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