Tutorial at IEEE ICC2014, Sydney Revisit the Cellularicc2014.ieee-icc.org/2014/private/Tutorial13.pdf · IEEE ICC2014 Tutorial "Revisit the Cellular" 2014/06/14 ... 1G (’80s) 2G

IEEE ICC2014 Tutorial "Revisit the Cellular" 2014/06/14

Zhisheng Niu @ Tsinghua University 1

Revisit the Cellular: A Hyper-cellular Framework for Green and

Smart (5G) Mobile Communication Systems

Electronic Engineering, Tsinghua UniversityTsinghua National Lab for Information Science and Technology

June 14, 2014

Zhisheng Niu

Tutorial at IEEE ICC2014, Sydney

Migration of Mobile Communications

• Cooper’s Law: “The data rate available to a wireless device doubles roughly every 30 months” (Martin Cooper)– This has held for over 50 yrs, leading to 1,000,000x increase – Technology: 1G (’80s) 2G (’90s) 3G (’00s) 4G (’10s)

“People always over-estimate things for 3 years scope,

but under-estimate things for 10 year scope”

– Bill Gates

What does 5G look like?

What will the enabling technologies be for 5G?

2



What’s the technology that mostly contributed to this success?

TDMA? CDMA? AMC? Turbo? OFDM? MIMO? ……

To answer this question, we need understand

25x 5x 5x

1600x

0

500

1000

1500

2000

widerspectrum

dividing thespectrum intosmaller slices

bettermodulationscheme

reduced cellsizes

Wireless Capacity…

Source: William Webb, Ofcom

It’s Cellular!

Cellular was invented for spectrum-efficiency

But, is it really energy‐efficient? Is it smart enough to support massive M2M connections?

4



This has not been a major concern

Energy of firewood:16.2 megajoules/kgOnly one bit: invasion or no-invasion

Extremely energy inefficient, yet needed5

But, it is a big concern today

• But, energy consumption and cost increased dramatically – Globally, #BS > 5 million, #Users>5 billion, EC> 100bn KWh (2012)

– As 4G/5G deploys and IoT boosts, EC & Connections grow dramatically

– Energy cost is also increasing (price and environmental impact)

How to carry 1000X traffic and connections using limited spectrum & energy?

6



EE as a key decision-making factor

Equipment Type TEEER Formula Min. TEEER Allowable

Transport ‐log (Ptotal / Throughput) 7.54

optical and Video 7.54

P2P Microwave 5.75

Switch/Router ‐log (Ptotal / Forwarding Capacity) 7.67

Media Gateway ‐log (Ptotal / Throughput) 6.54

Access (Access Lines / Ptotal ) +1 2.50

Power (POut Total / PIn Total ) X 10 9.20

Power Amplifier (Wireless)

(Total RF Output Power / Total Input Power) X 10

1.05

Base Station ? ?

Verizon’s TEEER (Telecom Equipment Energy Efficiency Rating) since 2009

www.verizonnebs.com/TPRs/VZ-TPR-9207.pdf

Ptotal = 0.35 x Pmax + 0.40 x P50% load + 0.25 x Psleep

7/30

Smartness was also not an issue, but

Densely and randomly deployed

2G/3G/4G Coexisting (HetNet)

J. Andrews, “Seven Ways that HetNets are Cellular Paradigm Shift”, IEEE ComMag, March 2013 8



Diversified Needs for 5G

• Mobile traffic will have another exp. growth by 2020– Capacity-hungry video dominates: higher SE and EE (Green)– Control-intensive massive connections access should be Smarter

9

5G Cellular: Greener and Smarter

Capacity-oriented

2000

Coverage‐oriented

Traffic Vo

lum

e or E

nerg

y Co

nsu

mp

tion Time

2G

Energy-oriented

2010

3G3G+

4G4G

Traffic Volume

Green&

Smart

2020

Energy Consumption

5G

10



LTE‐A

LTE

GREEN

PHY approach only is no more enough

Energy-Spectrum Tradeoff in Wireless Transmissions

11

– 1G (’80s): Analog, Voice, FDMA, Macro (Coverage-oriented)

– 2G (’90s): Digital, Voice, TDMA, Macro (Coverage-oriented)

– 3G (’00s): Digital, Data, CDMA, Micro (SE-oriented)

– 4G (’10s): Digital, Video, OFDMA, Pico/Femto (SE-oriented)

– 5G (’20s): Digital, Video/M2M, BDMA?, ????? (SE/EE-oriented)

12

5G: A Paradigm Shift of Cellular Architecture

Cell densification is trying to further improve SE, but is it

also good for EE and smart enough to support M2M?

12



Where is Energy Spent in Cellular Networks?

• 70~80% energy consumed by BSs in a cellular network

– Reducing the power consumption of BSs is the key!

Source: Ericsson13

Energy Waste in Existing Cellular

Traffic data from 319 HSPA cells in a European capital city measured from Jan. 1-22 2009 (Ericsson)

3 sector HSPA Site

1 25 50 75 1000

10

20

30

40

50

60

70

80

90

100

Load [%]

DC

Pow

er C

onsu

mpt

ion

[%]

OtherFans

RU3

RU2

RU1Base band

80% of the BSs are quite lightly loaded for 80% of the time, but still consume (waste) a lot of energy

14



Energy Waste in Existing Cellular

8 pm5 am

Real measurement data from a Chinese Operator in Zhejiang Province, Feb. 2013

15

Energy Waste in Cellular Networks

E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Towards Dynamic Energy-Efficient Operation of Cellular Network Infrastructure”, IEEE Commun. Mag., June 2011

BSs are densely deployed and overlapping, further wasting energy

BS location data from a part of Manchester, UK.

16



Energy Waste in Cellular Networks

– All BSs are ON (active) all the time (in order to keep coverage), although traffic is almost zero in many areas

– Each BS almost transmits in peak power, although peak traffic only lasts for a very short time in most cells

– Multi‐BSs (small cells, HetNet) are densely deployed in many areas without any collaboration (work almost independently)

– As cell size is getting smaller AND traffic dynamics more bursty, energy waste is getting more serious

Business

Residential

Daytime Nighttime

Residential

Business

Residential

Residential

Business

Business

17

Why so many BSs under‐utilized, while still need to be densely deployed in some area?

Existing cellular is neither smart nor green

Why lightly‐loaded BSs can’t be switched off (sleep)?

- Mobile traffic is highly dynamic!

- BSs need to provide data services as well as network coverage simultaneously

18



Key idea: Reduce Energy Waste by Adapting to Real-traffic Dynamics (REWARD)

• Exploiting traffic dynamics (reduce energy consumption when traffic is low)

– Targeting THROUGHPUT rather than CAPACITY per joule

• Exploit energy model (much energy is consumed at BB/PA/AC rather than RF, therefore BS sleeping is the most efficient way for energy saving)

– Targeting TOTAL ENERGY rather than RF power reduction only

• Exploit cell collaboration (cell densification and HetNet make cell collaboration possible, helping to turn more BSs off)

– Targeting NETWORK rather than LINK/CELL performance

GREEN: Globally Resource‐optimized & Energy‐Efficient Networks

CapacityEE = ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

Power Consumption

Network ThroughputNEE = ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐

Total Energy Consumption

19

Tango: Traffic-aware network planning & green operation- Adapted to traffic distribution (temporally and spatially non-uniform) - Adapted to traffic characteristics (unicast, multicast, broadcast)- Adapted to QoS requirements (realtime, nonrealtime)

5G Cellular: Adapt to Traffic Dynamics(Traffic dynamics can provide opportunities for energy saving)

0:00 12:00 24:00

Power

t

Reduced Consumption

Usual Power Consumption (non-adaptive)

Traffic

Key challenge: How to guarantee the coverage and QoS?How to model and predict traffic dynamics?

Z. Niu, “TANGO: Traffic-Aware Network Planning and Green Operation”,IEEE Wireless Commun., Oct.2011 (invited article)

BS Sleep

Power Adaptation

20



Example: Cell Zooming

Z. Niu, Y. Wu, J. Gong, Z. Yang, “Cell zooming for cost-efficient green cellular network,” IEEE ComMag, Nov. 2010 (IEEE APB Best Paper Award 2014)

• Cell Zooming for Smart Cellular Network

Central cell zooms in as traffic load increases

Central cell zooms out as traffic load decreases

Central cell sleeps as traffic load getting quite low

21

A Dynamic Programing Approach for BS Sleeping

x-axis (m)

y-a

xis

(m

)

500 1000 1500 2000 2500 3000

500

1000

1500

2000

2500

High Load

Medium

Low Load

Active cells

Sleeping cells

J. Gong, S. Zhou, Z. Niu, “A Dynamic Programming Approach for Base Station Sleeping in Cellular Networks,” IEICE Trans. Commun., Vol.E95-B, No.2, pp.551-562, Feb. 2012

22



5G Cellular: Adapt to Environment(BS collaboration can provide opportunities for energy saving)

• CHORUS: Collaborative & Harmonized Open Radio Ubiquitous System– Open Radio: spectrum in HetNet are shared by multi-modal terminals (software defined radio)

– Globally optimized: cross-layer cross-node cross-network/system (software defined network)

cross-netw

ork/systemdesign

cross-layer cross-node design

[1] S. Zhou, Z. Niu, S. Tanabe, “CHORUS: Collaborative and Harmonized Open Radio Ubiquitous Systems”, 4th Intl. Conf. Commun. Sys. & Nets. (COMSNETS), Bangalore, India, Jan. 2012 (invited)[2] S. Zhou, Z. Niu, S. Tanabe, and P. Yang, “CHORUS: Framework for Scalable Collaboration in Heterogeneous Networks with Cognitive Synergy,” IEEE Wireless Commun. Mag, accepted, 2012

Challenges: 1) How to detect the NSI? (information explosion and incompleteness?)2) How to virtualize the network resources? (self-optimizing networks)

HetNet

23

Example: BS Sharing

B. Leng, P. Mansourifard, B. Krishnamachari, Z. Niu, “Microeconomic Analysis of Base-Station Sharing in Green Cellular Networks”, IEEE INFOCOM 2014, Toronto, Canada, April 2014

24



5G Cellular: Deal with the Dilemma

Capacity‐hungry Apps (e.g., mobile videos)

Higher SE

Higher EE

Control‐intensive Apps (e.g., M2M, social networking)

Faster Connectivity

Higher Reliability

?Smaller cells Larger cells

C-plane larger

D-plane smaller

decouple

Less signaling overhead

Global optimization

SmartCoverage-on-demand

Densely deployed

Green25

Bottlenecks of Existing Cellular Architecture

• GREEN involves more controls/signaling exchanges– TANGO needs to get traffic and QoS information of neighboring nodes

– CHORUS needs to collect network-state information (NSI) of other nodes/networks in addition to channel state information (CSI)

– Overhead of signaling traffic will get higher as cell size gets smaller

• Deeply coupled structure can’t flexibly adapt to traffic – Tight coupling of the coverage for control signals and data signals makes it

less flexible to the traffic variation and frequent handovers, and less friendly to cross-network design

– Users in sleeping cells will be shadowed from the network

NSI

control

data

26



Hyper Cellular for Green and Smart

• Decouple control and traffic coverage so that data cells could be more adaptive to traffic dynamics and network state, and control cells can take global optimization

Traffic analysis

Broad

ban

dNarro

wban

d

Signalin

g

Contro

l

GSM

3G

Macro

Micro

Hyper

27

Control BSs serve as a Central Controller?

For on‐demand data services

Unified Signaling Network (Control Plane)for Software‐Define Wireless Networks?For always‐ON coverage

Traffic BS Traffic BS

28



Advantages of Hyper-Cellular Architecture

• Energy-on-Demand– Coverage on-Demand：Macro/Micro/Pico/Femto?

– Resource on-Demand：Full-Power/Half-Power/Sleep？

– Service on-Demand： Realtime/Non-realtime/Soft-realtime？

Unicast/Multicast/Broadcast?

• Global resource optimization– Easily match users to the best BSs depending on the user requests

– Data cells can easily adapt to the traffic and network state changes

– Friendly to management of heterogeneous networks

control

micro

macro

29

Cellular Network Architecture Migration

• R99R5R8 (3GPP)：from tree to mesh, decoupling to cooperate and reconfigure – Decouple of RNC and BTS

– Decoupling of BBU and RRU

30



Hyper-cellular: Virtual (Elastic) Coverage

Decouple of signaling coverage and traffic coverage

Signaling coverage is seamless and traffic coverage is reconfigurable

Challenge: unified signaling for virtualized cellular NWs

U‐Plane

C‐Plane

Seamless

Elastic

31

Hyper-cellular: Virtual Cells

Decouple of antennas and AI processing

No. of antenna is independent of computation units

Node C3 Node C4

Node C1 Node C2

Cable/Fiber

Node A

V-NodeB1

V-NodeB2 MT1

MT2

V‐Cell

32



Hyper-cellular: Virtual (Cloud) Computing

• Decouple of AI functions and computational resources

• The AI functions are processed in any unit, and computed offloading

33

Combine Cellular with Cloud

J. Liu, T. Zhao, Y. Chen, S. Zhou, Z. Niu, “CONCERT: A Cloud-Based Architecture for Next-generation Cellular Systems”, submitted to IEEE Wireless ComMag, 2014

C‐RAN

CONCERTS: CONvergence of Cloud and cEllulaR sysTems

34



C-RAN: It’s just the first step to SDN

35

From DWCS to CRAN

Traditional BTS Distributed BTS C-RAN

• Traditional BTS system– (Huge) integrated system– BTS & supporting facility

require indoor protection– Long RF cable to antenna

• Distributed BTS– Outdoor RU, Indoor BBU– DU to multi-RU– DU-RU connected via

point-to-point dark fiber

• C-RAN– Centralized processing– Collaborative Radio– Open platform towards

Cloud computing



CM’s Vision on NG Wireless Network

37

IT based core network

Anchor BS

Nano AP

Virtual BB pool

Content Pool

Anchor BS

骨干站点

RRU

Relay D2D

relay

D2D

Indoor Coverage

User Centric Access Network Supporting exclusive usage of available spectrum of each user

Branch BS

LSAS

Technical Challenges

• How to decouple signaling from data coverage? How to integrate the signaling functions of HetNets? – Complete decoupling may lead to new bottleneck and delays due to frequent

visits to signaling-BSs (main difference from BCG2), but which functions should be left into the data-BSs?

• How to guarantee signaling coverage highly reliable? – Need new protocol for S-BSs. Also, tradeoff between reliability and delay

• How to detect user behaviors, QoS requests, terminal capability, and provide services in an EE manner? – Data mining, cognitive radio, on-line learning, …

• How to locate users and associate them to the best D-BS? – The best cells may be in sleeping state, activate or not?

• How to balance the EC of network parts and user terminals? – User terminals need to keep associations with S-BS in a wider scope

• ……Z. Niu, S. Zhou, S. Zhou, X. Zhong, J. Wang, “A Hyper-Cellular Paradigm for Globally Resource-optimized and Energy-Efficient Networks (GREEN)”, Science in China, Sep. 2012 (in Chinese） 38



Enabling Technologies for 5G

• Massive MIMO and dense small cell networks (for throughput improvement)

• Highly flexible/reliable and realtime MAC protocol (for efficient support of IoT applications)

• Advanced interference and mobility management

• Cognitive or smart radio technologies (for spectrum efficiency)

• Single frequency full duplex radio technologies

• mmWave (for wireless backhaul and/or access)

• Pervasive networks (for multihoming or multiple concurrent data transmission)

• Multi-hop networks and D2D communications (for coverage extension)

• IPv6 (for seamless handover and roaming)

• Virtualized and cloud-based radio access infrastructure (for network flexibility:

different slices of the network with different technologies for different applications)

• World wide wireless web (WWWW) (for comprehensive wireless-based web applications that include full multimedia capability beyond 4G speeds)

• Wearable devices with AI capabilities (for augmented reality)39

Global Research Activities on “5G”

• “BDMA and Relay with group cooperation” (Korea, 2008)

• “5G Communications Research Lab” (Univ. of Dresden, 2012.5)

– Jointly funded by National Instruments

• “₤35m for 5G Research Centre” (Univ. of Surrey, 2012.10)

– jointly funded by UK Research Partnership Investment Fund (UKRPIF) and a consortium of Huawei, Samsung, Telefonica Europe, Fujitsu Laboratories Europe, Rohde & Schwarz, and Aircom International

• “China launched a WG on 5G” (China Academy of Telecom Research, 2012.11)

• “Huawei invests $600m for 10Gbps 5G network” (2013.11)

• “Korea to spend $1.5 billion on 5G mobile network” (2014.1)

• “China Mobile joined NGMN 5G Alliance” (MWC2014, 2014.2)

40



Global Research Activities on “5G”

• “€50m EU research grants to develop '5G' technology” (EC, 2013.2)– METIS: Mobile and wireless communications Enablers for Twenty-twenty (2020)

Information Society (SWE Ericsson, 29 partners) specifies 5G should provide 1000X higher mobile data volume per area

10‐100X higher No. connected devices for Internet of Things 10‐100X higher typical user data rate

10X longer battery life for low power M2M Communications 5X reduced e2e latency

– 5GNOW: 5th Generation Non-Orthogonal Waveforms for Asynchronous Signalling (GER)

– iJOIN: Interworking and JOINt Design of an Open Access and Backhaul Network Architecture for Small Cells based on Cloud Networks (ESP)

– TROPIC: Distributed computing, storage and radio resource allocation over cooperative femtocells (ESP)

– COMBO: joint optimisation of fixed and mobile access (GER)

– MOTO: Mobile OpportunisTic Traffic Offloading (FRA)

– PHYLAWS: PHYsical LAyer Wireless Security

• "5GrEEn - Towards Green 5G Mobile Networks“ (EIT ICT Labs. 2013.9) 41

Green Activities in China

End-to-End Energy Efficient Networks (National 863 Program, 2012~2015)

Green Radio Excellent in Arch. and Tech. (Huawei Program, 2010 ~ )

Globally Resource-optimized and Energy-Efficient Networks (National 973 Program, 2012~2016)

2014/06/14 42



5G Study in China (“863 Program”, $26m in 2014, 2nd phase in 2015)

5G NET(Hyper, Cognitive, Dense)

(THU, BUPT, Huawei)

5G PHY(Massive Distributed MIMO)

(Southeast University)

5G Testing and Verification (Wireless Communication Research Institute, Shanghai)

5G Overall Description & Standardization

(China Academy of Telecom Research, MII)

43

5G: Key Questions

• What does the network architecture of 5G look like?

How can make cellular architecture more smart and green?

Should we also include WiFi into 5G family? How to offloading?

How does CLOUD and/or SDN help & how to combine with them?

• What should be the fundamental components and the enabling technologies in order to make it happen?

Cognitive radio and networking

Software defined radio and networking

Content delivery network

WiFi offloading

• How to migrate to the new architecture from today?44



5G: Major Design Objectives

• Implementation of massive capacity and massiveconnectivity

• Support for an increasingly diverse set of services, applications and users – all with extremely diverging requirements

• Flexible and efficient use of all available non-contiguous spectrum for wildly different network deployment scenarios

5G is the next frontier of innovation for entire mobile industry (paradigm shift)

45

5G: Timeliness of the Topic

IEEE Communications Magazine Special Issue on 5G Wireless

Communication Systems: Prospects and Challenges (Deadline 15 Sept.

2013)

IEEE JSAC Special Issue on 5G Wireless Communication Systems (Deadline

4 Dec. 2013)

IEEE Communications Magazine Special Issue on Millimeter Wave

Communications for 5G (Deadline 1 Feb. 2014)

IEEE Communications Magazine Special Issue on 5G Networks: End‐to‐

end Architecture and Infrastructure (Deadline 1 Feb. 2014)

• CFPs from leading COMSOC journal/magazine

46



5G: Keywords from the above CFPs

Definition of 5G

Deployment requirements

Heterogeneous and small cell networks (HetSNets)

Cloud-based RAN

Massive MIMO

Energy‐efficiency

Cognitive and reconfigurable

Heterogeneous architecture Interference

coordination Multi-media traffic "no cell" concept

CoMP

Cloud data centers

Collaborative communications

Programmable optical backbone

3D Audio and Video

5G evaluation tools and testbeds

mmWavecommunications

Beyond OFDMA

Full Dimension MIMO

Channel aggregation 5G PHY 5G NET

47

Multi-antenna transmission/reception

5G: PHY vs NET Solutions

Full duplex, network coding, …

Multi-layer coordinationCoordination

Multi-site transmission/reception

Interference suppression

Physical‐layer evolution will remain important

But the main aim for the PHY evolution will be to enable more advanced system‐level features

Courtesy: Erik Dahlman (Ericsson) 48



Some Research Progress in 2012/13

How much energy can be saved by Separation?

Traditional Cell Hyper Cell

in outP k P b

50



How much energy can be saved by separation?

Total power consumption of a cell: more than 50% saving

Average power consumption of a cell: robust to cell size

Z. Wang, W. Zhang, “The Capability of A Separation Architecture for Achieving Energy-efficient Cellular Networking“, IEEE TWC, 2013 (accepted)

51

Elastic Coverage for Non-Uniform Mobile Traffic

1 1m m m

m m m

Ah

A B

Ratio of traffic in hot areas over all traffic

Ratio of hot area over total area

Hyper-Cellular

Always-on Control BS for

traffic in cool area

On-demand Traffic BS for

traffic in hot area

Stochastic Geometry Theory

Data rate in hot area vs data rate in cool area (bps/Hz/m2)

Hot area vs cool area

Grouping degree

52



Energy Saving Gain by Elastic Coverage

If (ratio of basic power of micro and Macro BSs)ESG increases as h increases

/c cm m MP P

IfESG decreases as h increases

/c cm m MP P

0 0.1 0.2 0.3 0.4 0.5 0.60

0.5

1

1.5

2

2.5

3

3.5

m

= 0.02

m = 0.07

m = 0.12

m = 0.17

m = 0.22

m = 0.27

m = 0.32

m = 0.37

m = 0.42

m = 0.47

h

Netw

ork En

ergy Efficiency

53

Energy Saving Gain by Elastic Coverage

Parameter Value

CBS Power 373W/sector

TBS Power 11W

No. of hot spots in CBS

4, 10

Radius of TBS

40m, 50m

Radius of CBS

288m

No. of users in TBS

4, 6

No. of users in CBS

60

Up to 42%~300% ESG by elastic coverage in 3GPP typical scenario

For h=80%, γm=23%, ESG can go to 340%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

50

100

150

200

250

300

350

rm =23%

h

3GPP Typical Parameters

Energy Savin

g Gain

(%)

rm =14%

rm =9%

54



• Separation is not easy!– Challenge 1: difficult to categorize (millions of signal types)

– Challenge 2: Difficult to separate (complicated signal interactions)

– Challenge 3: difficult to manage (synchronization)

Standard Signal TypesCategorize & SeparateCategorize & Separate

Signal Types

Standard

State

FunctionalityFunctionality Separation?

How to Separate? - Principle

2014/06/14 55

How to Separate? – State Definition

X. Xu, G. He, S. Zhang, Y. Chen and S. Xu, “On Functionality Separation for Future Green Mobile Network: Concept Study over LTE”, IEEE ComMag, May 2013

56



How to Separate? - State–Functionality Mapping

State UE Activities

Network Functionalities

Syn.Broadcast of

System Information

Paging Multicast Unicast

Detached Cell Selection √ √

Idle

Acquisition and Update of System Configuration

√ √

Monitoring of Upcoming Transmission Notification

√

Cell Reselection √ √Receiving of MBMS √

Active

Acquisition and Update of System Configuration.

√ √

Monitoring of Upcoming Transmission Notification

√

Cell Handover √ √ √Receiving of MBMS √

Transmission of UE-Specific Data

√

2014/06/14

How to Separate? - Functionality–Signal Mapping

Network

Functionality

Signal Types

Syn. PilotFrame

Control

System

Info.

Bearer

Paging

Info.

Bearer

Multicast

Info.

Bearer

Unicast

Info.

Bearer

Syn. √ △

Broadcast

of System

Information

√ △ √

Paging √ √ √

Multicast √ √ √

Unicast √ √ √

△ means this relationship may change among different standards. For example, in GSM/UMTS system, the location of system information bearer is pre-defined and the frame control signal is omitted. However, in LTE systems, the location of system information bearer will be dynamic and the frame control signal is mandatory.

2014/06/14



How to Separate? - Mapping to 3GPP Standard

Signal Types3GPP Standard

GSM UMTS LTE

SynchronizationFCCHSCH

SCH PSS/SSS

Pilot TSCCPICHDPCCH

S-CCPCHRS

Frame ControlAGCHSACCH

PICHMICHAICH

DPCCHS-CCPCH

PHICHPCFICHPDCCHPMCH

Paging Inform. Bearer

PCH S-CCPCH PDSCH

System Inform. Bearer

BCCHSACCH

P-CCPCHPBCH

PDSCH

Multicast Inform. Bearer

CBCH S-CCPCH PMCH

Unicast Inform. Bearer

SDCCHSACCHFACCH

TCH

S-CCPCHDPDCH

PDSCH

2014/06/14

Channel Separation

Channel separation scheme for 3GPP standards

Implementation for GSM/GPRS protocol

60



A Lab Demo using USRP and OpenBTS

T. Zhao, P. Yang, H. Pan, R. Deng, S. Zhou, and Z. Niu, “Software Defined Radio Implementation of Signaling Splitting in Hyper-Cellular Network,” ACM SIGCOMM Workshop of Software Radio Implementation Forum (SRIF 2013), Hong Kong, Aug. 2013.

61

Lab Demo of Hyper Cellular Concept

GPRS data separation

Simple DBS scheduling scheme

SBS log

DBS logPlatform setup

62



Service Flow

UE associates to Signaling BS.

UE requests for traffic channel,Signaling BS assigns a Data BSand informs UE.

Traffic data transmission is established between UE and Data BS.

63

Establishment of Transmission

DBS DBS

Channel Request

UE SBS

Request Message Request Message

Assignment Message Assignment Message

Immediate Assignment

RLC/MAC BlockRLC/MAC Block

RLC/MAC Data Block RLC/MAC Data Block

Packet Uplink ACK Packet Uplink ACK

Data Base Station Scheduling

64



Traffic-Aware Dynamic BS Sleeping

• Settings

– 10x10 hexagon cells

– Cell Radius 200m

– Binary BS power

– Link parameters according to

ITU micro-cell test environment

– Traffic:

3 hotspots in the network – space

– Hotspot center traffic , 1st tier traffic , 2nd tier traffic

, others ,

Average intensity varies ‐ time

( )h t 1 ( )h t

2 ( )h t 3 ( )h t 3 2 10 1

Jie Gong, Sheng Zhou, Zhisheng Niu, “A Dynamic Programming Approach for Base Station Sleeping in Cellular Networks,” IEICE Trans. Commun., Feb. 2012

65

Traffic-Aware Dynamic BS Sleeping

• Compare with uniform sleeping alg. [Marsan’09]– Multiple sleeping pattern– Active BSs uniformly located

(0.88 0.63 0.50) (0.83 0.50 0.33) (0.81 0.44 0.25) (0.80 0.40 0.20) (0.79 0.38 0.17)60

61

62

63

64

65

66

67

68

69

70

Ave

rag

e N

o. o

f Act

ive

BS

s

(0.88 0.63 0.50) (0.83 0.50 0.33) (0.81 0.44 0.25) (0.80 0.40 0.20) (0.79 0.38 0.17)10

-4

10-3

10-2

10-1

Ave

rag

e B

lock

ing

Pro

ba

bili

ty

(1 2

3)

Uniform alg. ave. active BSs

DP alg. ave. active BSs

DP alg. ave. blocking

Uniform alg. ave. blocking

DP algorithm performs better as the hotspots become hotter

Non‐uniformity increases

66



How densely should D-BSs be deployed?

• Problem: For given QoS, how densely should the DBs be deployed for a given coverage and QoS guarantee? – BS density should adapt to traffic dynamics (e.g., cell zooming, BS sleeping)– Deploying more smaller BSs may save energy ?!(increasing sleeping

opportunity)

[1] Z. Niu, Y. Wu, J. Gong, Z. Yang, “Cell zooming for Green cellular networks”, IEEE Com Mag, Nov. 2010 [2] X. Weng, D. Cao, Z. Niu, “Energy-Efficient Cellular Network Planning under Insufficient Cell Zooming”, IEEE VTC2011-spring, Budapest, Hungary, May 2011

0 1 2 3 4 5 60

1

2

3

4

5

6

0.5

1

1.5

2

2.5

3

3.5

4x 10

-4

0 10 20 30 400

0.05

0.1

0.15

0.2

0.25

到率

Temporal Dynamics Spatial Dynamics Insufficient Zooming Sufficient Zooming

67

Optimal BS Density for Green(Regular Deployment Case)

• Normalized EC vs Inter-BS Distance (PB<2%)

Deploying more smaller BSs can save energy!!!

1. Z. Niu, S. Zhou, Y. Hua, Q. Zhang, and D. Cao, “Energy-aware network planning for wireless cellular systems with inter-cell cooperation”, IEEE TWC., vol.11, no.4, pp.1412-1423, 2012

2. Y. Wu, Z. Niu, “Energy Efficient Base Station Deployment in Green Cellular Networks with Traffic Variations”, IEEE ICCC2012, Beijing, China, Aug. 2012

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

时间(小时)

到达

率(/

Km

)

业务模型 1业务模型 2

Time (h)

Arrival rate (/km

)

300 400 500 600 700 8000.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

基站部署间距 (m)

归一

化网

络能

耗

业务模型 1业务模型 2

Traditional planning

EE planning

Inter‐BS Distance (m)

Norm

alized EC

68



Optimal BS Density for Green(Heterogeneous & Stochastic Deployment Case)

1. Two‐tier PPP models with BS density ρM and ρm

2. Always connect to the BS with highest SNR (not necessarily the nearest)

Weighted Poisson‐Voronoi Tessellation:

f(A) follows ‐distribution with density

where：Stochastic Geometry Modeling

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

X coordinate

Y c

oo

rdin

ate

D. Cao, S. Zhou, Z. Niu, “Optimal Combination of Base Station Densities for Cost-Efficient Two-tier Heterogeneous Cellular Networks”, IEEE TWireless, Sep. 2013

69

B. Rengarajan, G. Rizzo, and M. A. Marsan, ``Bounds on QoS-Constrained Energy Savings in Cellular Access Networks with Sleep Modes’’, ITC 2011, pp. 47-54, San Francisco, USA, Sep. 2011.

Bay area of Sydney, Australia.Dense deployment: 81.64 per Km^2

Verification of PPP Models

Australian Geographical Radio Frequency Map (http://spench.net/)

70



Verification of PPP Models

Distribution of BSs in a Square Area of a Chinese Operator

Rural Dense Urban

No. of BSs No. of BSs

PDF

PDF

71

Verification of Gamma Distribution

Distribution of Cell Areas of a Chinese Operator

72



QoS Metrics

• Coverage Probability

If α=4,

• Service Outage Probability

73

Optimal BS Density - Formulation(Homogeneous Case)

74



Optimal BS Density – Upper Bound(Homogeneous Case)

75

Optimal BS Density – Lower Bound(Homogeneous Case)

76



Optimal BS Density - Performance(Homogeneous Case)

Table: Optimal BS density for 3 typical scenarios (in BSs/Km2, EARTH model)

Table: Optimal BS density with transmit power adaption

Conclusion: noiseless assumption is acceptable for suburban and dense urban scenarios, but not in rural scenario

Conclusion: Joint BS density adjustment and transmit power adaption can help save more energy

D. Cao, S. Zhou, Z. Niu, “Optimal Combination of Base Station Densities for Cost-Efficient Two-tier Heterogeneous Cellular Networks”, IEEE TWC, Sep. 2013 77

Optimal BS Density and Tx Power(Heterogeneous Case)

1. Two‐tier PPP models with BS density ρM and ρm

2. Always connect to the BS with highest SNR (not necessarily the nearest)

Weighted Poisson‐Voronoi Tessellation:

f(A) follows Gamma distribution with density

where：Stochastic Geometry Modeling

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

X coordinate

Y c

oord

inat

e

where {CM , Cm} are deployment (energy) cost

Coverageguarantee

78



Optimal BS Density – Near-optimal Solution(Heterogeneous Case)

;

79

Optimal BS Density and Tx Power(Heterogeneous Case)

• Dynamic BS Sleeping in Dense Urban Scenario (EARTH Model)

– CM = 780 + 28.2PM , Cm = 112 + 5.2Pm

– PM = 20W, Pm =2.42W = 0.0927 < c-1=0.3162

– Reference model: macro-only homogeneous network with no BS sleeping: total energy consumption=3.26 KW/Km2

0.82 (average)(75% saving)

Conclusion: Joint optimization of Macro/Micro‐BS densities can help to save more energy!

80



Optimal BS Density – Optimal Policy(Heterogeneous Case)

;

If <c=0.3162, preferentially add micro BSs or sleep macro BSsIf >c=0.3162, preferentially add macro BSs or sleep micro BSs

Ratio of Micro‐BS density and Macro‐BS density ()

Total Energy Density

81

Heterogeneous Networks with PSR

• PSR (Partial Spectrum Reuse) to reduce over-provisioningand potential interference (to macro BSs and among micro BSs)

Total Spectrum

Macro BS

Micro BS1

Micro BS2

Micro BS3

D. Cao, S. Zhou, Z. Niu, “Improving the Energy Efficiency of Two-Tier HetwrogeneousCellular Networks through Partial Spectrum Reuse”, IEEE TWC, Aug. 2013

Optimal β=Wm/WM?

If β<1, allocate FULL spectrum to macro BSs and PARTIAL spectrum to micro BSs; If β>1, vice versa.

2

; ( )m M

M m

C Pe c

C P

82



Energy Saving Gain by PSR

PSR achieves the near-optimal performance

PSR can save up to 50% of energy consumption

83

Application to Network Planning – Capacity Extension (EARTH Model: Dense Urban, Peak Traffic increases up to 74.3/Km2）

ρM=1 BS/km2 (¾ used for coverage), EC=5.9kW/km2

Macro BSs for coverage guarantee

Other macro BSs (could be switched off)

Newly added BSs for capacity extension

Network Topology before capacity extension

Km

Km

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Adding macro BSs: ρM 1.75 BSs/km2

EC 3.56 kW/km2 (40% saving)

Capacity extension by homogeneous BSs

Km

Km

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Km

Adding micro-BSs: ρm 4.25/Km2

EC 1.87 kW/km2 (48% further savings)

Km0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Capacity extension by heterogeneous BSs

84



Application to Energy Saving – BS Sleeping(EARTH Model, Dense Urban)

Network Topology during Peak Traffic (75/Km2)

ρM=1 BS/km2 , ρm=4.25 BS/km2, EC=1.87 KW/Km2

Macro BSs for coverage guarantee

Other macro BSs (could be switched off)Micro BSs

Km

Km

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Awake 35% micro BSs: ρm=1.5 /km2, EC=1.18KW/Km2 (↓37%)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Traffic Load up to 50% (37/Km2)

All unnecessary BSs going to sleep, ρM=0.75/km2, EC=0.97KW/Km2 (↓50%)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Traffic load down to 20% (15/Km2)

85

How long should a BS sleep?

• Energy-Delay Tradeoff (EDT) in BS Sleeping Control– Longer sleep can save energy, but bring delays to customers

• Wake-up Policies– N-Policy: wake up whenever N new requests come during sleep– SV-Policy: wake up after a random sleep and then keep awake– MV-Policy: wake up after a random sleep and sleep again if find no requests

• Challenge #1: Both energy and delay concepts need to be extended– Energy = transmitting power + circuit (processing) power + basic power– Delay = transmitting delay + queueing delay + sleeping period

• Challenge #2: EDT should be evaluated in the whole network wide– EDT on link-level single-cell level multi-cell level

1. Z. Niu, Jianan Zhang, Xueying Guo, Sheng Zhou, “On the Energy-Delay Tradeoff in Base State Sleep Mode Operation”, IEEE ICCS2012, Singapore, 21-23 Nov., 2012 (invited)2. X. Guo, S. Zhou, P. R. Kumar, Z. Niu, “Optimal Wake-up Mechanism for Single Base Station with Sleep Mode”, 25th International Teletraffic Congress (ITC25), Shanghai, China, Sep. 2013. (Best Paper Award)

Energy‐Delay Tradeoff is getting much more complicated

86



Ex：Transmit Power vs Transmit Delay

AWGN Channel Upper limit of transmit rate

Delay per bit

Energy per bit

20

log (1 )P

R WWN

1/bt R1

0 (2 1)bt Wb bE WN t

Power can be traded off by delay!

87

Ex：Total Power vs Transmission Delay

Transmission delay

Cir

cuit

Po

wer

RF

Po

wer

Tota

l P

ow

er RF Power Dominant

Circuit Power Dominant

Channel（transmission delay）

TxGreedyTraffic Rx Throughput

RF

Circuit

RF

Circuit

Transmission delay Transmission delay

)log(N

PWC r 1

G. Miao, G. Y. Li, “Cross-Layer Energy-Efficient Wireless Communications: A survey,” Wireless Com & Mobile Comp, 2009

Energy and Delay is not always a tradeoff!But, traffic dynamics was not considered (no queueing delay, etc)

88



Ex: Impact of Queueing Delay on EDT

Server(capacity)

Poisson (λ)

M/M/1(k) – non realtime service system

Exp (μ)

Buffer (k)

PB(2)=ρ2

k+1(1-ρ2)/(1-ρ2k+2)

where ρ2=λ/μ

For k=2 and ρ2=0.5, then s=7, while W2=1.7μ2-1

For k=8 and ρ2=0.5, then s=511, while W2=1.9μ2-1

PB(1)=PB

(2)

s=(1-ρ2k+1)/[(1-ρ2)ρ2

k ]

Server(capacity)

M/M/1(0) – realtime service system

PB(1)=ρ1/(1+ρ1)

where ρ1=λ/sμ

Poisson (λ)

Exp (sμ)

Considering , we know a small delay can trade for a great amount of energy savings

)log(N

PWC r 1

89

Characterizing EDT need to combine IT and QT

• Information Theory (IT) focuses mainly on link capacity (i.e., capability) over noisy channels, but not traffic dynamics

• Queueing Theory (QT) focuses mainly on traffic dynamics and systemperformance, but not channel unreliability

μRandom

arrival (λ)Random departure

(=λ bits/s)

noiseGreedy Source

Random departure

(≤ C bits/s)

noise

An Unconsummated Union

A. Ephremides, B. Hajek, “Information Theory and Communication Networks: an Unconsummated Union”, IEEE Trans. IT, Oct. 1998

Effective Capacity(Rate‐Accuracy Tradeoff)

Efficient Bandwidth(QoS‐Load/Randomness Tradeoff)

90



Optimal Condition of a Queueing System

• For a given traffic load, what’s the best service rate? – Higher service rate (capacity) lower queueing delay Wq (higher throughput)

– Lower service rate (capacity) higher server utilization ρ (less energy waste)

• Considering a queue as a blackbox with equivalent service rate μ’=1/(Wq+1/μ), maximize ρμ’ – effective power– For M/G/1, ρμ’=2 μqρ(1- ρ)/[ρμ2b2+2(1- ρ)], which leads to

ρopt=1/[1+sqrt(μ2b2/2)]

Wqopt=sqrt(b2/2)

W opt=sqrt(b2/2)+1/μ

Lqopt= ρopt sqrt(μ2b2/2)

L opt= ρopt [sqrt(μ2b2/2)+1]=1 (!)

Y. Yoshioka, “on the Optimization of Queueing Systems”, IEICE Trans. Commun., Aug. 1977. (in Japanese)

M/Ek/1

How does sleep operation change this EDT? (When and how long should the BS sleep?)

91

Dynamic Control of a Queue

• Considers a M[x]/M/2 queue. The faster server is always on, but the slower server is only used when the queue length exceeds a certain level and switched off when it completes a service.

• Activating the slower server involves fixed set-up costs. Also there are linear operating costs and linear holding costs.

• Conclusion: the two-level hysteretic switching rule that turns the slower server on when the number of jobs in the system exceeds some pre-specified upper level and turns the slower server off when upon service completion by the slower server the number of jobs left behind is below some pre-specified lower level.

Rein D. Nobel and Henk C. Tijms, “Optimal Control of a Queueing System with Heterogeneous Servers and Setup Costs”, IEEE Trans. Automatic Control, April 2000

f

sL2

L1

92



How does sleep operation change the tradeoff?

• For a typical server under dynamic load, where entering and leaving sleeping mode incurs an energy and a response time penalty, the optimal sleeping policy has a simple hystereticstructure– When asleep, the server stay in OFF mode until the queue builds up to

the point where the ON threshold is met

– After waking up, the server stays awake until all jobs in the queue are processed and then is turned OFF

• Compared with a baseline policy that never puts the server to sleep, it was shown that – low utilization can result in almost 87% energy saved

– high utilization results in only 7.4% energy saved

Ioannis Kamitsos, Lachlan Andrew, Hongseok Kim, Mung Chiang, “Optimal Sleep Patterns for Serving Delay-Tolerant Jobs”, Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, pp.31-40, 2010

93

Other Related Work

Delay

Transmitting Delay

Processing Delay

QueueingDelay

Energy

Transmit Power

Circuit Power

Basic Power

[Anatharam96][Morabito07]

[Miao09]

[Anatharam96] V. Anantharam, S. Verdu, “Bits Through Queues”, IEEE Trans. on Info. Theory, 1996

[Morabito07] G. Morabito, “Increasing capacity through the use of the timing channel in power‐constrained satellite network”, INFOCOM, 2007

[Miao09] G. Miao, G. Y. Li, “Cross‐Layer Energy‐Efficient Wireless Communications: A survey,” Wireless Com & Mobile Comp, 2009

[Berry02] R. A. Berry, R. G. Gallager, “Communication over Fading Channels with Delay Constraints”, IEEE Trans. IT, May. 2002

[Neely09] M.J. Neely, “ Intelligent Packet Dropping for Optimal Energy‐Delay Tradeoffs in Wireless Downlinks”, IEEE Trans. Automatic Control, March 2009

[Chen11] Y. Chen , G. Y. Li, et al, “Fundamental trade‐offs on green wireless networks”, IEEE Comm. Mag., June 2011

Single-Link[Berry02] [Neely09]

94



M/G/1 Vacation queue with setup and close-down time

• Rationale of single-server queue– Consider whole BS as a single server,

which is either serving customers (busy) or idling (close-down, idle, setup)

–

95

Some Preliminary Studies

1. Z. NIU and Y. TAKAHASHI, “A Finite-Capacity Queue with Exhaustive vacation/close-down/setup times and Markovian Arrival Processes”, QUESTA, vol.31, no.1, pp.1-23, 1999.

2. Z. Niu, T. Shu, Y. Takahashi, “A Vacation Queue with Setup and Close-down Times and Batch Markovian Arrival Processes”, Performance Evaluation, vol. 54, pp.225-248, 2003.

3. F. Zhu, Y. Wu and Z. Niu, “Delay analysis for sleep-based power saving mechanisms with downlink and uplink traffic,” IEEE Com Lett, 2009.

4. F. Zhu, Z. Niu, “Queueing Delay and Energy Efficiency Analyses of Sleep Based Power Saving Mechanism”, IEICE Tran Com，2010.4

Z. Niu, Jianan Zhang, Xueying Guo, Sheng Zhou, “On the Energy-Delay Tradeoff in Base State Sleep Mode Operation”, The 13th International Conference on Communication Systems, Singapore, 21-23 Nov., 2012 (invited)

Xueying Guo, Sheng Zhou, P. R. Kumar, Zhisheng Niu, “Optimal Wake-up Mechanism for Single Base Station with Sleep Mode”, 25th International Teletraffic Congress (ITC25), Shanghai, China, Sep. 2013. (Best Student Paper Award)

96



EDT in terms of Close-down Time

• M/G/1 N-policy vacation queue with setup and close-down time– Impact of close-down time

Mean Sojourn Time

Average Power

Sleep mode brings more benefits on energy saving when traffic load is light

PST = PCD = 0.9PON, PSL = 0.2PON

97

EDT in terms of Close-down Time and N

Power –Delay Tradeoff

Without sleep operation, EDT always exists

With sleep operation, energy can be further

reduced by

with same delay

It’s Linear!

98



EDT in terms of N and Setup Time

• Impact of N (for simplicity, assume close-down time equals to zero)

99

Optimization of N

Monotone if setup time is fixed and therefore N=1 is optimal

Convex if setup time is burstyand therefore there is an optimal N>1

100



EDT in terms of N and Setup Time

Deviation of setup time does not affect on mean power, while only a little on mean time while λE[S] is high

If traffic load is low, accumulating N customers take a long time, and therefore N should be small

If traffic load is high, accumulating N customers does not take a long time, and therefore N could be larger

101

EDT for Mean Delay vs Delay Bound

( ) 1 ( ) (1 ( )) [ ] (1 )(1 ( ))( ) {

[ ] 1 [ ][ ( )]

( ) [ / ( )] [ ( )] 1 ( )( ( )) ( ) ( ) }.

/ ( ) ( ) ( )

N N N

B s D D E B B sT s

E C E B s B s

N S s s B s S s B sD D

N s B s s B s

Mean delay and delay bound are almost linear, therefore similar tradeoffs hold for energy and delay bound

Large deviations in service times lead to significantly large delay bound

102



Traffic-aware Dynamic Sleeping Control and

Power Adaptation in GREEN Communication

J. Wu, Z. Niu, S. Zhou, "Traffic-Aware Base Station Sleeping Control and Power Matching for Energy-Delay Tradeoffs in Green Cellular Networks“, IEEE Trans. Wireless Commun., Vol.12, no.8, Aug. 2013

• System Modeling

– Single Cell with n users, Processor-sharing model[1]

– Traffic dynamics:

Large‐scale: load-aware

Small‐scale: queue-aware

– Optimization

where

– Lambert function: W(z)

Dynamic Power Adaptation without Sleep

[1] S.C. Borst, “User‐level performance of channel‐aware scheduling algorithms in wireless data networks”. Proc. Infocom 2003 Conference.

delay power

104



• Upper and lower bounds

Dynamic Power Adaptation without Sleep

Upper bound：

Lower bound：

λ=0.5，l=2MB105

• Objective function: weighted sum of energy consumption and mean delay

where represents the cycle time and is the switching energy

• Sleeping mechanism – N-based: go to sleep whenever queue becomes empty and wake up if N

requests accumulated during sleeping period

– V-based: go to sleep whenever queue becomes empty and wake up if V time expires

Dynamic Power Adaptation with Sleep

But, setup and close‐down times and their impact on energy consumption not considered

106



M/G/1 Processor Sharing Modeling

107

N-Policy: With or Without Sleep

Sleep operation can only help to save energy when traffic load is lower than a specific value

108



N-Policy: Region for EDT

Energy can only be traded off by delay if traffic load is higher than a specific value in N‐policy sleeping operation

109

N-Policy: Region for Energy Saving Gain

Sleeping control helps to save energy only when 1) Traffic load is light, 2) (P0-Psleep)/Esw is large, or3) N is small

110



N-Policy: Optimality of x* and Lower Bound of Ptotal

x*en is an increasing function of γ, so transmitting faster

when channels are good indeed saves energy. In addition, fast transmission is beneficial when Po - Psleep is larger

If traffic load is further lower than a specific value (?), EDT doesn’t exist all the time

111

N* is related to the switching cost in a square root form!

112



N-Policy: Pareto-Optimal (N*, X*)

113

V-Policy: EDT with Different Weight

114



V-Policy: “PA+Sleep” vs “PA only”

Sleeping Control helps only in lightly‐loaded systems

115

How long should a BS sleep?

‐ Small delay can help to save energy if well designed‐ N* is related to the switching cost in a square root form‐ Sleeping Control should be used with power adaptation

J. Wu, Z. Niu, S. Zhou, "Traffic-Aware Base Station Sleeping Control and Power Matching for Energy-Delay Tradeoffs in Green Cellular Networks“, IEEE TWC, Vol.12, no.8, Aug. 2013

116



Impact of Bursty (IPP) Arrivals

The higher the burstness, the larger the ES region117


Total power consumption always decreases as the burstiness increases and optimal Pt may exist. But, the benefit for average delay is available only when N is large.

Smaller Higher burstiness

118




ES gain highly depends on the system parameters 119


Energy-Delay Tradeoff can be optimized by jointly adjusting N and Pt

Lower bound of total power consumption

120



Summary

• What’s 5G?– 5G should be a paradigm shift of cellular architecture for Green and Smart

• Major approaches towards 5G – Reduce Energy Waste by Adapting to Real-traffic Dynamics (REWARD)– Traffic-Aware Network Planning and Green Operation (TANGO)– Collaborative and Harmonized Open Radio Ubiquitous Systems (CHORUS)

• A novel Hyper Cellular architecture for 5G– Decoupling signaling functions from data services to make cellular more

adaptive and intelligent– Always-on hyper cells for coverage guarantee and on-demand data cells

• Enabling technologies for 5G– Separation of control and data coverage – Resource/network virtualization and network dimensioning– Traffic adaptation technologies, including cell zooming, BS sleeping,

coverage extension, ……– Energy-delay tradeoff can help to shift the peak and therefore save energy 121

Concluding Remark

• from World-Wide-Web to World-Wide-Wireless

• for World-Wide-Watch & World-Wide-Wisdom

but definitely should not World-Wide-Wait

and World-Wide-Waste!

: Smart IT for Low-carbon Environment

122



For more information, visit http://network.ee.tsinghua.edu.cn/niulab/?category_name=publications

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