Small Cell Networks - Current Research and Future Landscape

Wireless Small Cell Networks:

Current Research and Future

Landscape

Dr. Mehdi Bennis http://www.cwc.oulu.fi/~bennis/

Centre for Wireless Communications

University of Oulu, Finland

2nd International Workshop on Challenges and Trends on Broadband Wireless Mobile Access Networks

Beyond LTE-A

Acknowledgments: Prof. Merouane Debbah, Prof. Vincent Poor, Prof. Walid Saad, and

all PhD students and collaborators

http://www.cwc.oulu.fi/~bennis/



Outline

• Part I: Motivation and challenges

• Part II: Interference management in small cells

• Part III: Towards Self-Organizing small cells

• Part IV: Future landscape: From reactive to

proactive networking

• Part V: Conclusions

2

o Operators face an unprecedented increasing demand for mobile data

traffic

o 70-80% volume from indoor & hotspots already now

o Mobile data traffic expected to grow 500-1000x by 2020

o Sophisticated devices have entered the market

o Increased network density introduces Local Area and Small Cells

o In 2011, an estimated 2.3 million femtocells were already deployed

globally, and this is expected to reach nearly 50 million by 2014

o Explosive online Video consumption

o OTT are on the rise (20% of internet traffic carried out by

NETFLIX and the likes) huge by bit content but low in revenues

& value for telcos

Small Cell Networks – A Necessary Paradigm Shift

Macrocell

Small Cells/Low power Nodes

Witnessing consumer

behaviour change

- More devices, higher

bit rates, always active

- Larger variety of

traffic types e.g. Video,

MTC

3

Facts & Figures

Ultimately, the viable way of reaching ―the 1000X‖ paradigm is

making cells smaller, denser and smarter……[is that enough?]

• Heterogeneous (small cell) networks operate on licensed (and unlicensed) spectrum

owned by the mobile operator

• Fundamentally different from the macrocell in their need to be autonomous and self-

organizing and self-adaptive so as to maintain low costs

• Femtocells are connected to the operator through DSL/cable/ethernet connection

• Picocells have dedicated backhauls since deployed by operators

• Relays are essentially used for coverage extension

• Heterogeneous (wired,wireless, and mix) backhauls are envisioned

• Operator-Deployed vs. User-deployed

• Residential, enterprise, metro, indoor,outdoor, rural

Solar panel

@ London’s

Olympics Games Lamp Post Hotpost

4

In a nutshell….

5

(multi-dimensional) HetNets

Macro-BS

wired

Wireless

backhaul

Relay

Femto

Pico

bzzt! 3G/4G/WiFi

Characteristics

• Wireless backhaul

• Open access

• Operator‐deployed

Major Issues

• Effective backhaul design

• Mitigating relay to macrocell

interference

Characteristics

• Wired backhaul

• Operator‐deployed

• Open access

Major Issues

•Offloading traffic from

macro to picocells

• Mitigate interference

toward macrocell users

Characteristics

• Wired backhaul

• User-deployed

• Closed/open/hybrid

access

Major Issues

• Femto-to-femto

interference and femto-to-

macro interference

Characteristics

• Resource reuse

• Operator‐assisted

Major Issues

• Neighbor discovery

• Offloading traffic

D2D

Macrocells: 20-40 watts (large footprint)

Characteristics

• large number of antennas

• vertical beamforming

Major Issues

• fight through pilot

contamination

“Backhaul and cell location

are big deals for

operators”

MBS SBS

Macro + small cell

(cochannel)

HetNets – Leveraging the frequency domain

f1

f1

MBS SBS

Macro + small cell

(split channel) f1

f2

MBS SBS

MBS

MBS SBS

Macro-only Macro +

small cell

(single flow)

Multi-flow or

soft-cell (///)

MBS SBS

A mix

HetNets – Leveraging the spatial domain

coordination

coordination

coordination

DOCOMO’s View (the CUBE)

Reference: METIS

• Three access policies

• Closed access:

only registered users belonging to a closed subscriber group (CSG) can

connect

Potential interference from loud (macro UE) neighbors

• Open access:

all users connect to the small cells (pico/metro/microcells)

Alleviate interference but needs incentives for users to share their access

• Hybrid access:

all users + priority to a fixed number of femto users

Subject to cost constraints and backhaul conditions

• Femtocells are generally closed, open or hybrid access

• Picocells are usually open access by nature and used for offloading macrocell

traffic and achieving cell splitting gains.

9

Small Cell Access Policies

• Recent trials using a converged

gateway Wi-Fi/3G architecture

showed how the technologies

could be combined and exploited

• Several companies are likely to

simultaneously introduce both

technologies for offloading.

- Deployed to improve network coverage and

improve capacity (closed access)

- There is considerable planning effort from the

operator in deploying a femtocell network

- Prediction: there will be more small cells than

devices! (Qualcomm CTW 2012)

- A cheap alternative for data offloading

- Availability of Wi-Fi networks, high data rates

and lower cost of ownership has made it

attractive for catering to increasing data demand

- However, seamless interworking of Wi-Fi and

mobile networks are still challenging

Open Problem

How to combine and integrate 3G/4G/Wi-Fi in a cost effective manner?

Small cells vs. Wi-Fi:

- Managed vs. Best effort

- Simultaneously push

both technologies for

offloading

10

Small Cells vs. WiFi True love or arranged marriage?

Down the road, WiFi will become just ‖another RAT‖

• The backhaul is critical for small cell base stations

• Low-cost backhaul is key!

• What is the best solution? NO SILVER BULLET

• Towards heterogeneous small cell backhaul options

• Conventional point-to-point (PtP):

• high capacity

• coverage, spectrum OPEX, high costs

• E-band (spectrum available at 71-76 and 81GHz)

• high capacity

• high CAPEX and OPEX

• Fiber (leased or built)

• high capacity

• recurring charges, availability and time to deploy

• Non-Line of sight (NLOS) multipoint microwave

• good coverage, low cost of ownership

• low capacity, spectrum can be expensive

+ possibly TV White Space...

Milimeter-wave backhaul currently a strong potential

Proactive caching ~30-40% savings (more on this later) Sub 6 GHz Point-to-Multipoint Backhaul Links 11

The Backhaul – a serious bottleneck

Standard macrocell handover parameters are obsolete

all UEs typically use same set of handover parameters (hysteresis margin and

Time-to-Trigger TTT) throughout the network

- Challenges:

- When does a network hands off users as a function of interference, load, speed,

overhead?

- UE-specific and cell-specific handover parameter optimization (e.g., using

variable TTTs according to UE velocity), and applying interference coordination

(for high speed UEs), etc.

- Need for mathematical models and tools that enable detailed analysis of

capacity and mobility in HetNets w/o cumbersome Monte-Carlo pointers to

operators

- Interrelated with enhanced ICIC solutions + inter-RAT offloading

- Typically traditionally ICIC and Mobility are treated separately bad!

Macro-2 SBS-1 SBS-2

SBS-3

Macro-1 MUE-1 MUE-2

Challenges in SCNs –

Mobility and Load Balancing in HetNets

MBS

UL

DL DL UL

interference

signal

UL

BS-to-BS interf.

UE-to-UE interf.

Challenges in SCNs –

Flexible UL/DL for TDD-based Small Cells

On link level … … or only on system level

Full duplex will happen

Simultaneous TX/RX on same frequency

- Deal with asymmetric traffic in DL and UL

- Tackle BS-to-BS interference and UE-to-UE

interference (among others)

- Possible options are possible: (i)- adopt

same DL/UL duplexing among far away

small cells, or (ii)- different duplexing

method among clusters of small cells with

strong coupling.

Source: ///

SON is crucial for enhanced/further enhanced-ICIC, mobility

management, load balancing, etc.. 14

• Traditional ways of network optimization using

manual processes, staff monitoring KPIs, maps,

trial and errors ..........is unreasonable in SCNs!

• Self-organization and network automation is a

necessity not a privilege. Why?

• Femtocells (pico) are randomly (installed)

deployed by users (operators)

need fast and self-organizing capabilities

• Need strategies without human intervention

• Self-organization helps reduces OPEX

• Homogeneous vs. Heterogeneous deployments

every cell behaves differently

Individual parameter for every cell

• Eternal discussions on embracing Centralized-

SON, Distributed-SON or Hybrid-SON?

Challenges in SCNs – Self-Organizing Networks (SONs)

• Green communications in HetNets requires redesign at each level. Why?

• Simply adding small cells is not energy-efficient (need smart mechanisms)

• Dynamic switch ON/OFF for small cells

• Dynamic neighboring cell expansion based on cell cooperation

Macro-BS Macro-BS

Small

cell

Small

cell

Dynamic cell ON/OFF

Active Mode

Switch OFF

Switch OFF for power savings Cell range expansion

Dynamic neighboring

cell expansion

Energy harvesting is also a nice trait of HetNets! e.g., autonomous network configuration properties

converting ambient energy into electrical during sleep mode 15

Challenges in SCNs – Energy Efficiency

16

Challenges in SCNs

• Load balancing

• Spectrum sharing

• Co tier and cross tier interference management

• Energy efficiency

• Traffic offloading

• Cell association

• SON

• D2D

• Etc

+ industry-centric/driven issues such as:

LTE-U, LSA, LAA, ASA, CoPSS

Part II

Interference Management in Small Cells

17

Inter-cell Interference Coordination in LTE/LTE-A

• LTE (Rel. 8-9)

• Only one component carrier (CC) is available

Macro and femtocells use the same component

carrier

Frequency domain ICIC is quite limited

18

• LTE-A (Rel. 10-11)

•Multiple CCs available

•Frequency domain ICIC over multiple CCs is possible

•Time domain ICIC within 1 CC is also possible

•Much greater flexibility of interference management

Source: Ericsson

ICIC in LTE-A: Overview

• Way to get additional capacity

cell splitting is the way to go

• Make cells smaller and smaller and make the network

closer to user equipments

• Flexible placement of small cells is the way to address

capacity needs

How do we do that?

In Release-8 LTE, picocells are added where users

associate to strongest BS.

Inefficient

Release-10 techniques with enhanced solutions are

proposed

Cell range expansion (CRE)

Associate to cells that ‖makes sense‖

Slightly weaker cell but lightly loaded

19

e.g., Why not offload the UE to

the picocell ? Source: DOCOMO

Inter-cell Interference Coordination

Time-Domain

ICIC Frequency-

Domain ICIC

Spatial Domain

ICIC

Orthogonal

transmission,

Almost Blank

Subframe, Cell

Range

Expansion, etc

Orthogonal

transmission,

Carrier

aggregation,

Cell Range

Expansion, etc

A combination

thereof +

coordination

beamforming,

coordinated

scheduling, joint

transmission,

DCS, etc

• ICIC and its extensions are study items in SON

20

Inter-cell Interference Coordination -

Time Domain

• Typically, users associate to base

stations with strongest SINR • BUT max-SINR is not efficient in

SCNs

• Cell range expansion (CRE) ?

• Mandates smart resource

partitioning/ICIC solutions

• Bias operation intentionally allows

UEs to camp on weak (DL) pico cells • RSRP = Reference signal

received power (dBm)

• Pico (serving) cell RSRP + Bias

= Macro (interfering) cell RSRP

•Need for time domain subframe partitioning

between macro/picocells

• In reserved subframes, macrocell does not

transmit any data •Almost Blank Subframes (ABS) + duty cycle

Macro

Pico Pico

Limited footprint of pico due

To macro signal

Subframes reserved for macrocell transmission

Macro Pico

Pico

Increased footprint of pico

When macro frees up resources

Subframes reserved for picocell transmission

21

Inter-cell Interference Coordination -

Time Domain • (Static) Time-Domain Partitioning

• Negotiated between macro and

picocells via backhaul (X2)

• Macro cell frees up certain

subframes (ABS) to minimize

interference to a fraction of UEs

served by pico cells

• All picocells follow same pattern

Inefficient in high loads with non-

uniform user distributions

• Duty cycle: 1/10,3/10,5/10 etc

• Reserved subframes used by

multiple small cells

• Increases spatial reuse

• Adaptive Time-Domain Partitioning

• Load balancing is constantly

performed in the network

• Macro and picocells negotiate

partitioning based on

spatial/temporal traffic distribution.

0 1 2 3 4 5

time

6 7 8 9 0 1 2 3 4 5 6 7 8 9

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

50% Macro and Pico; Semi-Static

0 1 2 3 4 5

time

6 7 8 9 0 1 2 3 4 5 6 7 8 9

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

25% Macro and Pico; Adaptive

Macro DL

Pico DL

Macro DL

Pico DL

Possible

transmission No

transmission

Data

transmission

No

transmission

Data

transmission

#1 Macro Pico

#1

22

f1

MBS

- Push macrocell traffic to picocells through biasing

- Using same biaising parameters for all small cells is bad!

- Need to optimize cell-specific range expansion bias, duty cycle, transmit power

according to traffic, QoS requirements, backhaul and/or deployment costs, etc

- What happens in ultra dense networks with more than 4 Picos per sector (viral

deployment).

- Inside-outside approach where indoor small cells can also help offload traffic.

CRE bias-2

CRE bias-1

Inter-cell Interference Coordination (recap)

No ICIC CRE results in low

data rate for cell-edge UEs

Fixed CRE of 6 dB is good

for cell-edge UEs

Fixed CRE of 12 dB is

detrimental for ER PUEs (why?)

Mute ABS performs poorly due

to resource under-utilization

125% gain compared to

RP+23% compared to Fixed

CRE b = 12 dB

Inter-cell Interference Coordination –

Case study

reinforcement learning

Macro-BS MUE-2

MUE-1

SBS

High velocity

SUE-1 SUE-2

Range expansion

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame

duration

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

MBS

SBS

Frame duration

Zero power almost blank subframe (ABS) in 3GPP LTE Release-10

A possible approach for enhancing mobility performance

Reduced power ABS in 3GPP LTE Release-11

generalized ICIC approach that simultaneously improves capacity and mobility.

This is time-domain ICIC+mobility but same

thing can be considered for frequency

Zero-ABS Soft-ABS

Mobility and Load Balancing

(capacity/mobility tradeoffs)

Inter-cell Interference Coordination Further enhancements

• Further enhanced ICIC (f-eICIC) for non-CA based

deployment

• Some proposals:

• At the transmitter side in DL combination of ABS +

power reduction (soft-ABS)

• At the receiver side in DL use of advanced receiver

cancellation

Macro Pico

X2 X2

How to distribute the primary and secondary CCs to

optimize the overall network performance?? 26

Cross scheduling

• Further enhanced ICIC (feICIC) for CA based

deployment

• Several cells and CCs are aggregated

• Up to 5 CCs (100 MHz bandwidth)

• Cross scheduling among CCs is

possible

• Primary CC carrying

control/data information and

rest of CCsc carrying data

and vice-versa

• Greater flexibility for

interference management

Inter-cell Interference Coordination - Carrier Aggregation

• Carrier aggregation is used in LTE-A via Component

Carriers (CCs)

• Macro and Pico cells can use separate carriers to

avoid strong interference

• Carrier aggregation (CA) allows additional flexibility

to manage interference

Macrocells transmit at full power on anchor

carrier (f1) and lower power on second carrier

(f2), etc

Picocells use second carrier (f2) as anchor carrier

Partitioning ratio limited by number of carriers

But trend is changing (multiflow CA/3GPP release-

12)

(in some cases) Aggressor is victim and victim is

aggressor

CC1 CC2 CC3 CC4 CC5

100 MHz

freq.

CC1 CC2 CC3 Macro

CC1 CC2

CC3 Pico

freq.

CC1

CC2 S

macro pico UE

pico macro UE

aggressor

victim

aggressor

victim

How/when to swap victim/aggressor roles? 27

Part III

Toward Self-Organizing

Small Cell Networks

28

Self-Organizing Networks

• Manual network deployment and maintenance

is simply not scalable in a cost-effective

manner for large femtocell deployments

– Trends toward Automatic configuration and

network adaptation

• SON is key for

– Automatic resource allocation at all levels

(frequency, space, time, etc.)

• Not just a buzzword

– It will eventually make its way to practice

Large

picocell

footprint

with fewer

users

Small

picocell

footprint

with more

users 29

How to self-organize in

small cell networks?

M. Bennis, S. M. Perlaza, Z. Han, and H. V. Poor "Self-Organization in Small Cell Networks: A Reinforcement

Learning Approach," IEEE Transactions on Wireless Communications 12(7): 3202-3212 (2013)

30

http://www.informatik.uni-trier.de/~ley/db/journals/twc/twc12.html

Femtocell networks aim at increasing spatial reuse of spectral resources, offloading,

boosting capacity, improving indoor coverage

• BUT inter-cell/co-channel interference Need for autonomous ICIC, self-

organizing/self-configuring/self-X interference management solutions to cope with

network densification

• Many existing solutions such as power control, fractional frequency reuse

(FFR), soft frequency reuse (SFR), semi-centralized approaches …

We examine a fully decentralized self-organizing learning algorithm based on local

information, robust, and without information exchange

•Femtocells do not know the actions taken by other femtocells in the network

•Focus is on the downlink

•Closed subscription group (CSG)

•No cross-tier nor co-tier cooperation + No carrier aggregation (no leeway !!)

Toward Evolved SON

31

Due to their fully-decentralized nature, femtocells need to:

- Estimate their long-term utility based on a feedback (from their UEs)

- Choose the most appropriate frequency band and power level based on the accumulated

knowledge over time (key!)

- A (natural) exploration vs. exploitation trade-off emerges;

i. should femtocells exploit their accumulated knowledge OR

ii. explore new strategies?

- Some reinforcement learning procedures (QL and its variants) implement (i)-(ii) but

sequentially

- Inefficient

- Model-based learning.

Solution (in a nutshell)

Proposed solution is a joint utility estimation + transmission optimization where

the goal is to mitigate interference from femtocells towards the macrocell network

+ maximize spatial reuse

• (i)-(ii) are two learning processes carried out simultaneously!

• Every femtocell independently optimizes its own metric and there is no

coupling between femtocell‘s strategies (correlation-free);

• for correlation/coordination other tools are required

32

..‖Behavioral‖ Rule..

- History

- Cumulated

rewards

Play a given

action

Ultimately,

maximize the

long-term

performance

...

FBS

Should i explore? Should i exploit?

33

Basic Model

Maximize the long-term transmission rate of every

femtocell (selfish approach)

SINR of MUE

SINR of FUE

34

• The cross-tier interference management problem is

modeled as a strategic N.C game

• The players are the femto BSs

• The set of actions/strategies of player/FBS k is the

power allocation vector

• The utility/objective function of femtocell k

• Rate, power, delay, €€€ or a combination thereof Here transmission rates are considered

• At each time t, FBS k chooses its action from the finite

set of actions following a probability distribution:

Game Model

35

• Femtocells are unable to observe current and all previous actions

• Each femtocell knows only its own set of actions.

• Each femtocell observes (a possibly noisy) feedback from its UE

• Balance between maximizing their long-term performance AND

exploring new strategies-----------okay but HOW?

• A reasonable behavioral rule would be choosing actions yielding

high payoffs more likely than actions yielding low payoffs, but in any

case, always letting a non-null probability of playing any of the

actions

• This behavioral rule can be modeled by the following probability

distribution:

(x)

Entropy/Perturbation

Information Aspects

Maximize the long-term

performance utility +

perturbation 36

• At every time t, every FBS k jointly estimates its long-term utility function and

updates its transmission probability over all carriers:

Other SON variants can be derived in a similar way

Both procedures are

done simultaneously!

Utility

estimation

Strategy

optimization

This algorithm converges to

the so-called epsilon-close Nash

!!!

Players learn their utility faster than the

Optimal strategy

Proposed SON Algorithm

Learning

parameters

37

First scenario 2 MUEs, 2 RBs, K=8

FBSs

Convergence of SON 1 learning algorithms with respect to the Best NE.

The temperature parameter has a considerable impact on the performance

Parameters

Macro BS TX power

Femto BS TX power

Numerical Results

•The larger the temperature parameter is,

the more SON explores, and the

algorithm uses more often its best

transmission configuration and

converges closer to the BNE.

•In contrast, the smaller it is, femtocells

are more tempted to uniformly play all

their actions

38

Second scenario

• 6 MUEs, 6 RBs, K=60 FBSs

Average femtocell spectral efficiency vs. time for SON and best response learning algorithm

SON1 SON-RL

SON2:

SON1(+imitation)

SON3:

Best response

- no history

- myopic (maximize

performance at every

time instant)

SON1 outperforms SON2 and SON3

Being foresighted yields better performance in the long term

Numerical Results

39

Now, let us ADD implicit

coordination among small cells

M. Bennis et al. ‖Learning Coarse correlated equilibria in small cell networks," IEEE

International Conference on Communications (ICC), Ottawa, Canada, June 2012.

40

M. Simsek, M. Bennis, I. Guvenc, "Enhanced Inter-Cell Interference Coordination in

Heterogeneous Networks: A Reinforcement Learning Approach," IEEE Transaction of

Vehicular Technology, November 2014.

© Centre for Wireless Communications, University of Oulu

The cross-tier interference management problem is modeled as a

normal-form game

At each time instant, every small cell chooses an action from its

finite set of action following a probability distribution:

The Cross-Tier Game

41


(Classical) Regret-based learning procedure

42

Player k would have obtained a higher performance

By ALWAYS playing action

e.g.,


Given a vector of regrets up to time t,

Every small cell k is inclined towards taking actions yielding

highest regret, i.e.,

Regret-based Learning

43

..From perfect world to reality...

In classical RM, each small cell knows the explicit expression of its utility function

and it observes the actions taken by all the other small cells full information

Impractical and non scalable in HetNets


• Remarkably, one can design variants of the classical regret

matching procedure which requires no knowledge about other

players‘ actions, and yet yields closer performance. How?

• (again) trade-off between exploration and exploitation,

whereby small cells choose actions that yield higher regrets

more often than those with lower regrets,

– But always leaving a non-zero probability of playing any of

the actions (perturbation is key!)

Regret-based Learning

44


The temperature parameter represents the interest of small

cells to choose other actions than those maximizing the regret,

in order to improve the estimation of the vector of regrets.

The solution that maximizes the behavioral rule is:

Exploration vs. Exploitation

45

Boltzmann

distribution Always positive!!

Decision function mapping past/history + cumulative regrets into future


Numerical Results

46

0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

femtocell density in %

Aver

age

fem

toce

ll s

pec

tral

l ef

fici

ency

[bps/

Hz]

reuse 1

reuse 3

SON-RL; [Bennis ICC'11]

regret-based

Average femtocell spectral efficiency versus the density of

femtocells for SON learning algorithms.

2X increase

When Cellular Meets WiFi in Wireless

Small Cell Networks

M. Bennis, M. Simsek, W. Saad, S. Valentin, M. Debbah, "When Cellular Meets WiFi in Wireless Small Cell

Networks," IEEE Commun. Mag., Special Issue in HetNets, Jun. 2013.

47

M. Simsek, M. Bennis, M. Debbah, "Rethinking Offload: How to Intelligently Combine Wi-Fi and

Small Cells?," in Proc. IEEE ICC, Jun. 2013, Budapest, Hungary.

MBS

Goal: A cost effective integration of small cells and WiFi! (dual-mode)

- Distributed cross-system traffic steering framework is needed, whereby SCBSs leverage the

(existing) Wi-Fi component, to autonomously optimize their long-term performance over

the licensed spectrum band, as a function of traffic load, energy expenditures, and users‘

heterogeneous requirements.

- Different offloading policies/KPIs: (i)-load based, (ii)-coverage based, and (iii)-a mix + Account

for operator-controlled and user-controlled offloading.

- Leverage contextual information:

- Offloading combined with long-term scheduling + users‘ contexts

Backhaul

LTE/WiFi (access)

LTE/WiFi (backhaul)

Cellular-WiFi Integration a.k.a Inter-RAT Offloading


The cross-system learning framework is composed of the

following two interrelated components:

1. Subband selection and cell range expansion bias:

– Every SCBS learns over time how to select appropriate

sub-bands with their corresponding transmit power levels

in both licensed and unlicensed spectra, in which delay-

tolerant traffic is steered toward the unlicensed spectrum.

– Besides, every SCBS learns its optimal CRE bias to offload

the macrocell traffic to smaller cells.

2. Traffic-Aware scheduling: Once the small cell acquires its

subband, the scheduling decision is traffic-aware, taking into

account users‘ heterogeneous QoS requirements (throughput,

delay tolerance, and latency).

Cross-System Learning (in a nutshell)


• We study the coexistence between macro and small cells from a game

theoretic learning perspective.

• Regret-based learning algorithm is proposed whereby small cells self-

organize subject to the cross-tier interference constraint.

• The proposed solution is autonomous in nature, based on local information,

and no information exchange among small cells.

• Tool/Machinery: regret-based reinforcement learning

– Small cells learn their long-term probability distribution over their

transmission strategies (power level and sub-band) by minimizing their

regret over time for using some strategies

– Leverage the accumulated knowledge over time + history

– Exploration vs. Exploitation tradeoffs

• The regret-based learning algorithm converges towards the epsilon-Coarse

Correlated Equilibrium (epsilon-CCE); a generalization of the NE.

• The proposed algorithm is validated in an extensive LTE system level simulator

+ comparison with benchmark solutions.

Problem Formulation

50


Numerical Results

Convergence of the cross-system learning algorithm vs. standard

independent learning

Oscillations

•The bandwidth in the licensed (resp. unlicensed) band is 5 MHz (resp. 20 MHz).

• Simulations are averaged over 500 transmission time intervals (TTIs).

•3GPP outdoor picocells (model 1)

•The traffic mix consists of different traffic models following requirements of NGMN


Numerical Results

Total cell throughput

vs. number of users.

per UE throughput as a

function of the number of UEs.

Opportunistic Sleep Mode Strategies in

Wireless Small Cell Networks

53

S. Samarakoon, M. Bennis, W. Saad and M. Latva-aho, "Opportunstic Sleep Mode

Strategies for Wireless Small Cell Networks," in Proc. IEEE ICC 2014, Sydney, Australia.

54

Setting • A novel approach for opportunistically switching ON/OFF cells to improve

energy efficiency is proposed

• Proposed approach enables small cells to optimize their downlink

performance while balancing the load among each another, while satisfying

their users‘ quality-of-service requirements

• The problem is formulated as a non-cooperative game among SBSs seeking

to minimize a cost function which captures the tradeoff between energy

expenditure and load.

• A distributed algorithm is proposed where SBSs autonomously choose their

optimal transmission strategies.

• Solution: SBSs learn their best strategy profile based on their individual

energy consumption and handled load, without requiring global

information.

Network Model • B small cell base stations (SBSs) underlaying the macrocell

• Co-channel deployment is assumed

• At location x, every user requests a file with a given size

Packet arrival rate

Mean file size

Load density of BS b

Total load of BS b is

56

Network Model

• Power model:

(*) Classical user association is based on RSSI or RSRP

Load-dependent

57

Problem Formulation

• For each BS b, a cost function is defined that captures both

energy consumption and load, as follows:

• Due to mutual interference among SBSs + seeking a

decentralized solution,we model this problem as a N.C

game with implicit coordination:

Network configuration

Utility of SBS b Action space

Players: SBSs

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Self-Organizing switch ON/OFF

mechanism

• We propose a self-organizing solution in which each BS

individually adjust its transmission parameters, without

global network information

• Assumptions:

• BSs do not communicate among each others

• Each BS makes its decision independently.

• Each BS needs to estimate its long-term load autonomously

• Each BS needs to deal with adjacent interference

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Self-Organizing switch OFF mechanism

User Association:

• Classical UE association rule may cause cell overload and

lowering spectral efficiency

– Smarter mechanism required

SBS Load estimation:

Time-scale

separation

60

Numerical results

Baselines:

• Classical approach: all BSs on!

• Proposed approach: opportunistic sleep/wake modes.

• Exhaustive search

61

Numerical results

Variation of the cost per BS as a function of the number of BSs

with 100 UEs.

62

Numerical results

Variation of the cost per BS as a function of the number of UEs

with 8 BSs.

63

Numerical results

Tradeoff between BS load and energy

consumption for networks with 4 SBSs and 8 SBSs

3GPP Release 12 and Beyond

64

Release 12 and beyond

Macro-BS FUE

f1/booster f2

Small cell BS

Non-fiber based connection

LTE multiflow / inter site CA

Soft-Cell concepts

• Facilitate ―seamless‖ mobility between macro and pico layers

• Reduced handover overhead, increased mobility robustness, less loading to the core network

• Increased user throughput with carrier aggregation or by selecting the best cell for uplink and

downlink

• Wide-area assisted Local area access

TDD Traffic Adaptive DL/UL Configuration

DL is dominant

UL is dominant

Macro-BS

• Depends on traffic load and distribution

• Interference mitigation is required for alignment

Of UL/DL

• Flexible TDD design DL DL DL UL DL UL UL UL

65

FDD

TDD

Hetero-

CA

Licensed

Band

f1 f2

UL DL

f3 f4

UL DL f5ULDL

f6ULDL

CA btw LB & ULB

CA btw FDD & TDD

Unlicensed

Band

LTE or WiFi

Utilization of various frequency resources

Aggregation of FDD and TDD carriers

Aggregation of unlicensed band (LTE or

WiFi)

Source: LG Electronics

• Intra-RAT Cooperation

• CoMP based on X2 interface

• More dynamic eICIC

• Maximized energy saving

Carrier based ICIC for HeNB

Macro/Pico-Femto, Femto-Femto

Multi-carrier supportable HeNB

M1

M2F1

F3P3

F4 F5

F2

F6

F7

F8

F9

F10

F11

F12

F13

F14

Source: LG Electronics 66


• Inter-RAT Cooperation

Hotspot area service via the inter-RAT

connection between the cellular and Wi-Fi

network

LTE & Wi-Fi aggregation at co-located

transceiver site may also be considered

Measurement and signaling across intra/inter-

RAT nodes will be supported


• Relaying on Carrier Aggregation

Carrier aggregation for backhaul and

access link

Access link optimization/enhancement

with HD relay operation

Multiple antenna transmission

techniques for relaying

Mobile Relay

Multi-hop Relay


Hotspot 1 Hotspot 2

Inter-RAT Network

Pico Node

eNB

Wi-Fi AP

Tx Power Off

DeNB

Relay

Access link optimization

CA on backhaul link and access link Multiple

Antennas

UE 67


• 3D Beamforming

• Machine Type

Communication

New revenue streams

• Many devices

• Low-cost terminals essential

- Address conclusions from

Rel-11 study

• Support machine-type traffic

efficiently

• Handle priority and QoS

appropriately

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Part IV— Towards 5G:

From Reactive to Proactive

Networking

What‘s next? –Recent Trends

Source: Ovum, April 2013

Average Daily Mobile Messaging Volumes

Mobile Non-Cloud Traffic

Mobile Cloud Traffic

Mobile Data Traffic Mobile Non-Cloud vs. Cloud Traffic

WhatsApp

Billions of Users, services, etc.

800M 175M 250M 300M 100M 100M 1.06B 1B+

MBS

SBS-1

SBS-2

5G Leverage

Context/Content/Social

Demands/interactions

Understand users‘

behavior, demands,

etc

Need a framework

that is context-

aware, assesses

users‘ current

situation and be

anticipative by

predicting required

resources,

Anticipate

disruptions,

outages, etc

Networked Society

Internet of

everything!

•Classical networking paradigm have been restricted to physical layer aspects overlooking aspects related

to users‘ contexts, user ties, relationships, proximity-based services

• Traditional approaches are unable to differentiate individual traffic requests generated from each UE‘s

application => does not take advantage of devices ―smartness‖

•Urgent need for a novel paradigm of predictive networking exploiting (big) data, contexts, people,

machines, and things.

•Context information includes users‘ individual application set, QoS needs, social networks, devices‘

hardware characteristics, batter levels, etc

• Over a (predictive) time window which contents should SBSs pre-allocate? when (at which

time slot should it be pre-scheduled)? to which UEs ? And where in the network (location of

files/BSs)?

• Leverage storage, computing capabilities of mobile devices, social networks via D2D, etc

Predictive/Proactive Networking

Wireless Fabric Social Fabric

People

Sensors &

Machines

Social

relationships &

ties

Social

Influence

Crowd-Place

sourcing

Storage

Massive

MIMO Spectrum

Emotions

GPS

Foursquare

LBS

Cloud

computing

SDN Energy

Intelligence with

Big Data Analytics

Truly multi-dimensional (complex) networks

73

Toward Context-Aware Networks

• We can show that

using context

data can

significantly

improve the

performance of

wireless networks

• Foundations of

context-aware

wireless systems

Need new tools to tackle increasingly complex networks

When Social helps Wireless

74

If a user downloads a content,

what is the likelihood that his

―social friend‖ will request the

same content? Machine learning

Indian buffet process!

Customers => mobile users

Content => the dishes, relationship =>

social

We can ―predict‖ who will share content =>

improve D2D performance and traffic

offload

Social=context

Living on the Edge:

The Role of Proactive Caching in 5G Wireless

Networks

E. Bastug, M. Bennis, and M. Debbah, "Living on The Edge: On the Role of Proactice Caching in

5G Wireless Networks," IEEE Comm. Mag. SI on Context Awareness, 52(8): 82-89, Aug. 2014.

Few Facts • Today:

– Mobile video streaming accounts for 50% of mobile data traffic, with a

500X increase by 2025.

– Online social networks are the 2nd largest contributors to this traffic with

an almost 15% average share.

• One way of dealing with this problem

– Deployment of small cell networks (SCNs), by deploying short-range,

low-power, and low-cost small base stations underlaying the

macrocellular network => cost-inefficient + backhaul issues

– Most of the research efforts in SCNs focus on self-organization, inter-cell

interference coordination (ICIC), traffic offloading, energy-efficiency.

• However:

– Current studies are based on the reactive networking paradigm in which

users' traffic requests are immediately served upon their arrival or

dropped causes outages.

– Serving peak traffic demands in ultra-dense networks mandates

expensive high-speed backhaul deployments

24.11.2014 CWC | Centre For Wireless Communications 76 Solution: proactive caching!

From reactive to proactive Networks

24.11.2014 CWC | Centre For Wireless Communications 77

Problem:

We need a proactive/predictive/anticipatory networking paradigm which:

• exploits users' context information, in-network features to predict users'

demands, congestion levels, etc………….in advance!

• Leverages users' social networks and device-to-device (D2D)

opportunities.

• Proactively stores users' contents at the edge of the network (SBSs, user

terminals´, gateways, etc.).

Benefits:

• Better usage of the limited backhaul (higher offloading gains)

• Satisfy users‗ QoE + lower outages with same spectrum!!

• Better resource utilization across time, space, frequency and devices.

• Lower latency + more energy efficiency savings,

• Usher in the tactile internet, etc

Let us examine two cases:

• Proactive small cells

• Social networks aware caching via D2D communications.

Network Model


System Model


Problem Formulation


Fight through data scarcity..


Solution concept


After obtaining the estimated popularity matrix, All the estimated popular

files are stored greedily until no storage space remains.

Numerical Results


Satisfied requests and backhaul load vs. number of requests

Numerical Results

24.11.2014 CWC | Centre For Wireless Communications 84 Satisfied requests and backhaul load vs. storage constraint

Numerical Results

24.11.2014 CWC | Centre For Wireless Communications 85 Satisfied requests and backhaul load vs. Zipf popularity parameter

Leveraging D2D + Storage


Another way of offloading traffic is storing the contents at the users‗ terminals and

harnessing D2D communications for content dissemination.

• The aim is to reduce the load of SBSs (and the backhaul load as a by-product).

• By exploiting the interplay between users' social relationships and their D2D

opportunities, each SBS can track and learn the set of influential users from the

underlying social graph, and store the les in the cache of those inuential users.

The delivery protocol is as follows:

- When a user requests a file, the SBS delivers the content via the infrastructure.

-In case the requested content is available in the cache of D2D neighbours of the user,

these neighbours also contribute to the delivery.

Motivation


The behaviour of the proactive D2D caching procedure is analogous to the table

selection in an CRP.

-If we view the social network as a Chinese restaurant, the contents as the very

large number of files, and the users as the customers, we can interpret the contents

dissemination process online by an CRP.

-That is within every social community, users sequentially request to download

their sought-after content, and when a user downloads its content, the hits are

recorded (i.e., history).

-In turn, this action affects the probability that this content will be requested by others

users within the same social community, where popular contents are requested more

frequently and new contents less frequently. => CONTEXTUAL INFORMATION

Executing CRP per cluster and obtaining the popular files of users, the files can be

stored in the cache of influential users!

Numerical Results (Social-aware D2D)


Evolutions of satisfied requests and backhaul load vs. number of requests

Anticipatory Caching in Small Cell Networks:

A Transfer Learning Approach


Knowledge of content popularity

• Caching at the edge

• offload the backhaul traffic

• enhance the users' experience

• requires statistics of file/content

popularity

• File popularity matrix (users/files)

• large and sparse in practice

• estimated via machine learning (CF)

However

• high data sparsity ! cold start problem in CF

• expensive or even impossible to collect data

in practice

Solution: Transfer learning!

E. Baştuğ, M. Bennis, and M. Debbah, "Anticipatory Caching in Small Cell Networks: A

Transfer Learning Approach", 1st KuVS Workshop on Anticipatory Networks, Stuttgart,

Germany, September 2014.

http://www.flexible-radio.com/publication/anticipatory-caching-small-cell-networks-transfer-learning-approach




















What is (not) 5G?

..Fist A peek at 5G..

China Mobile

• The future must be green

• EE/SE co-design

• No more cells

• Rethinking signalling/control

• BS invisible

• Refarming 2G spectrum

METIS

• Perfect cell edge coverage

• Extremely low latency

(RTT<1ms)

• Significant volume of

information (UHD, 4K)

• Ability to handle a huge amount

of M2M

• Interference management

• Low power consumption

Samsung

• Mmwave technology

• Advanced small cells

• Advanced coding and

modulation

• D2D

• etc

Ericsson

• Ultra dense deployments

• V2V

• M2M

• Ultra-reliable com.

• D2D and cooperative devices

• Multihop com.

Alcatel Lucent Views

DOCOMO’s View

Going dense with mmwave

• Cannot dig fiber-based backhaul everywhere! Major headache for Telcos

• Density and backhaul: the key tradeoff

• Self-backhauling: allow anchor BSs with wired backhauls to provision wireless

backhauling to other BSs.

• Challenges: NLOS, oxygen absorption, cell-edge users do not benefit from mmwave

5G and 1000x the bps/Hz/km2:

Where will the gains come from? Bandwidth (10x more Hz)

millimeter Wave - LTE-U, LAA, ASA are just stopgaps

Spectral efficiency (5x more bps/Hz)

• More dimensions (massive MIMO?)

• Interference suppression? (must fight

through log)

Effective Density (20x

More Loaded BSs/km2):

Efficient HetNets, yet more

small cell and WiFi

offloading, maybe D2D

4G

5G

mmWave + HetNets • Densifying mmWave cells

yields huge gains (SNR

plus cell splitting)

• Can possibly do self-

backhauling!

• Win-Win

mmWave + massive

MIMO • Smaller antennas for

mmWave, seems promising

• But competition for the

DoF offered by antennas

• Improved SINR via

mmWave with high gain

antennas, interference

becomes negligible?

HetNets + massive MIMO • Small cells probably not be

able to utilize massive

MIMO

• Cost a key challenge

ref: Jeff Andrews

Conjecture

5G=Mixture of

antennas/cells+spectrum+memory

+ actionable intelligence (context-awareness)

+ smashing (some) of the current business

models (=barriers)

Thanks a lot!!

97

Small Cell Networks - Current Research and Future Landscape

Technology

Transcript of Small Cell Networks - Current Research and Future Landscape