Recent Advances in Wireless Small Cell Networks This document provides an overview of small cell networks and associated challenges. It discusses: 1) The need for small cell networks to address exponentially increasing mobile data demand. Mobile traffic is expected to grow 1000x by 2020 due to more devices, higher data rates, and video. 2) Characteristics of small cell networks including heterogeneous deployment of different types of small cells (e.g. femtocells, picocells), various access policies, and backhaul challenges. 3) Key challenges for small cell networks including interference management, mobility management, self-organization, energy efficiency, and integration with existing cellular networks. Modeling and analysis of small cell networks is important

1. Recent Advances in
Wireless Small Cell Networks
Mehdi Bennis and Walid Saad
http://www.cwc.oulu.fi/~bennis/
bennis@ee.oulu.fi
University of Oulu, Centre for Wireless Communications, Finland
Electrical and Computer Engineering Department, University of
Miami, USA
http://resume.walid-saad.com
walid@miami.edu
1

2. Outline
• Part I: Introduction to small cell networks
– Introduction and key challenges
• Part II: Network modeling & analysis
– Baseline models and key tools (stochastic geometry)
• Part III: Interference management
– Interference in a heterogeneous, small cell environment
– Emerging techniques for interference management
• Part IV: Toward self-organizing small cell networks
– Introduction to game theory and learning
– Applications in small cells
• Part V: Conclusions and open issues 2

3. Part I
Introduction to Small Cell Networks
3

4. Outline
4/120

5. Outline
5/120

6. What happens in one
hour?
Around the globe, in one hour:
– 685 million sms messages
– 128 million Google searches
– 9 million tweets
– 1.2 million mobile apps downloaded
– 2880 hours of YouTube videos uploaded
– 50,000 smart phones activated
We need innovative network designs to
handle all of this!
6

7. Technology Convergence
Wireless services
Digital imaging
TV and video
Computing
Gaming
7

8. 8
Main Implications
• Operators dilemma
– Meet the demand and maintain low costs (i.e., revenues an issue)
• Need to decrease the expenditure per bit of data (to avoid
uglier alternatives such as limiting usage)
• Solutions that have been explored in the past few years
– Multiple antenna systems and MIMO
• Cannot provide order of magnitude gains
• Scalability and practicality issues
– Cognitive radio
• Availability of white spaces in major areas at peak hours is questionable
• MIMO and Cognitive radio will stay but must co-exist along
with better, more scalable, and smarter alternatives
• Is there any better, cost-effective solution?

9. • Operators face an unprecedented increasing demand for mobile
data traffic
• 70-80% volume from indoor & hotspots already now
• Mobile data traffic expected to grow 500-1000x by 2020
• 1000-times mobile traffic is expected in 2020 to 2023
• Sophisticated devices have entered the market
• Increased network density introduces Local Area and Small Cells
• In 2011, an estimated 2.3 million femtocells were already deployed
globally, and this is expected to reach nearly 50 million by 2014
• Explosive online Video consumption
• OTT are on the rise (20% of internet traffic carried out by
NETFLIX and the likes)
Small Cell Networks – A Necessary Paradigm Shift
Macrocell
Small Cells/Low power Nodes
Consumer behaviour
is changing
- More devices, higher
bit rates, always active
- Larger variety of
traffic types e.g. Video,
MTC
9
Facts
Ultimately, the only viable way of reaching “the 1000X”
paradigm is making cells smaller, denser and smarter

10. • Heterogeneous (small cell) networks operate on licensed 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 GamesLamp PostHotpost
10
In a nutshell….

11. 11
In a nutshell….
Macro-BS
Wired
backhaul
wired
Wireless
backhaul
Relay
Femto
Pico
bzzt!
lamppoles
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)

12. MBS
SBS
MBS
MBS
SBS
Macro-onlyMacro +
small cell
(single flow)
Multi-flow or
soft-cell (///)
MBS
SBS
A mix
HetNets – Leveraging the spatial domain
coordination
coordination
coordination

13. DOCOMO’s View (the CUBE)
Reference: METIS

14. • Small Cell Forum (formerly Femto-Forum) is a governing
body with arguably most impact onto standardization bodies.
• Non-profit membership organization founded in 2007 to
enable and promote small cells worldwide.
• Small Cell Forum is active in two main areas:
1) standardization, regulation & interoperability;
2) marketing & promotion of small cell solutions
Next Generation Mobile Networks (NGMN) Alliance:
• Created in 2006 by group of operators
• Business requirements driven
• Often based on use‐cases of daily networking routines
• Heavily related to Self-Organizing Networks (SON) activities 14
Standardization Efforts

15. • 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.
15
Small Cell Access Policies

16. • 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? 16
 Small cells vs. Wi-Fi:
- Managed vs. Best effort
- Simultaneously push
both technologies for
offloading
Small Cells vs. WiFi
Friends or Foes?

17. • The backhaul is critical for small cell base stations
• Low-cost backhaul is key!
• What is the best solution?
• 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 Links17
The Backhaul – a new bottleneck

18. 18
Radio resource management and Inter-cell
interference coordination
Intra-RAT offloading, inter-RAT offloading
(tighter coordination)
Cell association and
load balancing
Handover and mobility management
Backhaul-aware RRM for small cell networks
Self-organization, self-optimization
Self-healingSecurity
Energy Efficiency and
power savings (green small cells)
Modeling and analysis
And many more..
Summary of Challenges

19. 19
Summary of Challenges
• Dense and ad hoc deployment -> new network models
• How to manage interference?
– Key to successful deployment of small cells
• How can we design the small cells in a way to co-exist with the
mainstream wireless system?
– Most critically, mobility and handover
• What is the best backbone to support the small cells?
– Small cells’ performance can be degraded when the backhaul is being
used by other technologies (e.g. WiFi or home DSL)
• How can we handle dense deployments?
• What about energy efficiency?
• Ultimately, can we have a multi-tier wireless network that is
built in a plug-and-play manner?

20. Challenges in SCNs –
Radio Resource Management and Inter-cell interference coordination
Macro-BS
Small cell UE
Small cell BS
Macro UE
Macro UE inside / near femto coverage
• DL interference from the small cell BS to nearby Macro UE
• A Macro UE far from its MBS will be affected the most

Macro-BS
Small cell UE
Small cell BS
Macro UE

• UL interference from nearby macro UE to small cell BS
• A macro UE far from its MBS causes interference toward the
small cell
Aggressor/Victim: small cell/macro Aggressor/Victim: macro/small cell
20
DL UL

21. Macro-BS
Small cell UE
Small cell BS
Macro UE
Small cell very close to Macro base station
• DL interference from nearby Macro-BS to small cell UE
• Interference from nearby Macro-BS can lower SINR of
small cell UE

• UL interference from small cell UE to nearby Macro-BS
• Many active small cell UEs can cause severe interference to the
Macro-BS
Macro-BS
Small cell UE
Small cell BS
Macro UE

Aggressor/Victim: macro/small cell Aggressor/Victim: small cell/macro
21
DL UL
Challenges in SCNs –
Radio Resource Management and Inter-cell interference coordination

22. Macro-BS
Small cell BS
Macro UE
(co-tier) interference among small cell networks
• DL interference among nearby small cell networks • UL interference among nearby small cell networks
Aggressor/Victim: small cell/small cell
Small cell BS
Macro-BS
Small cell BS
Macro UE
Aggressor/Victim: small cell/small cell
Small cell BS
22
DL UL
Challenges in SCNs –
Radio Resource Management and Inter-cell interference coordination

23. • UE mobility is faster than the HO parameter settings
• HO triggered when the signal strength of the source cell is too low
Too late HO
Too early HO Wrong cell HO
Mobility enhancement for
traffic offloading
Enhancement of small cell discovery is
needed for offloading to small cells
standard macrocell HO parameters are
obsolete
SON enhancements for HetNet
 How to control mobility with SON
features needs to be studied?
 How long to wait ? What is the
threshold? etc
 disruptive to standard scheduling
 Need for context-awareness
Macro LPNLPNLPN
23
Challenges in SCNs –
Mobility management and handover

24.  Standard macrocell HO parameters are obsolete
 all UEs typically use same set of handover parameters (hysteresis margin and
Time-to-Trigger TTT) throughout the network
- 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.
- Develop 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
- Note that traditionally ICIC and Mobility are treated separately  bad!
Macro-2 SBS-1SBS-2
SBS-3
Macro-1 MUE-1MUE-2
Challenges in SCNs –
Mobility and Load Balancing in HetNets

25. MBS
- Deal with asymetric 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.
- Potential gain by alternating between small cell DL and UL+ doing interference
mitigation.
UL
DLDL
UL
interference
signal
UL
BS-to-BS interf.
UE-to-UE interf.
Challenges in SCNs –
Flexible UL/DL for TDD-based Small Cells

26. SON is crucial for enhanced/further enhanced-ICIC, mobility
management, load balancing, etc.. 26
• 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
• Ongoing discussions on pros/cons of Centralized-
SON, Distributed-SON and Hybrid-SON?
Challenges in SCNs –
Self-Organizing Networks (SONs)

27. • 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
27
Challenges in SCNs –
Energy Efficiency

28. Part II
Nework Modeling & Analysis in
Small Cell Networks
28

29. Developing analytically tractable models for cellular
systems is very difficult
• Stochastic Geometry (StoGeo) has been used
in cellular networks with hexagonal base
station model, i.e., macrocell base stations
(grid-based).
With advent of heteregeneous and dense small cell
networks, random and spatial models are needed
• Hexagonal models fairly obsolete
• Need to model HetNets to characterize
performance metrics (Operators want pointers!!)
• Transmission rate, coverage, outage
probability tractability
• Ease of simulation
Wyner model was predominantly used in the 1990’s
• Too idealized; used in Information Theory (IT)
• used in Academia for tractability and analysis
29
Current Cellular Models
Source: J. Andrews, keynote ICC Smallnets, 2012.

30. • How to model and
analyze multi-tier
wireless networks?
• How to characterize
interference?
• How to derive key
metrics such as coverage
probability, spectral
efficiency etc?
Nuts and Bolts
30
Current Cellular Architectures

31. Aggregate interference at tagged receiver
......First, let us look at the coverage probability in a 1-tier setting
coverage
probability
31
Baseline Downlink Model (1-tier)

32. Coverage Probability (1-tier)
Where
Incredibly simple expressions
32
Source: J. Andrews, keynote ICC Smallnets, 2012.

33. How accurate is this model?
• Fairly accurate, even for
traditional planned
cellular networks.
• Yet, industry is
somewhat reluctant to
use these models due to
possible difficulty in
system level simulations
• Trend is changing for 5G 33

34. Moving on to K-tier Hetnets
Aggregate interference at tagged receiver
34

35. K-Tier Small Cell Networks
Theorem 2 [Dhillon, Ganti, Bacelli ’11]: The coverage probability for a typical
mobile user connecting to the strongest BS, neglecting noise and assuming Rayleigh
fading:
Key assumption!
• Single tier cellular network (K=1):
Only depends on SIR target and path loss
• K-tier network with same SIR threshold for all tiers (practical?)
Interestingly, same as K=1 tier.
Neither adding tiers nor base stations changes
the coverage/outage in the network!
- Network sum-rate increases linearly with number of BSs 35
Source: J. Andrews, keynote ICC Smallnets, 2012.

36. How accurate is the K-tier model?
Source: J. Andrews, keynote ICC Smallnets, 2012. 36

37. Summary
• How good is the Poisson assumption?
• Femtocells: deployments fairly random but distribution is known
• Macrocells: have some structure but definitely not grid-like
• Picocells: some randomness due to the deployment at hotspots
• How good is the independence assumption?
• Femtocells: fairly good since users typically don’t know the locations of operator
deployed towers
• Picocells and macrocells: questionable since both are operator deployed
 Need novel tools that capture more realistic models in small cell and heterogeneous
networks
 Need models that actually incorporate space and time correlation (open problem),
correlation patterns, etc
37

38. Open Issues in Stochastic Geometry
• Most results assume base stations to transmit all the time;
• untrue in practical systems
• Biasing and cell association and load balancing
• Push traffic toward open access underload picocells
• Achieving cell splitting gains
• Uplink SINR model much harder
• Requires a thorough study
• Interference management, scheduling, MIMO, mobility management and load
balancing
• Take-away messages
• Stochastic Geometry
 Most importantly, operators want pointers for their network deployments.
 Gradually embraced by industry
38

39. Part III
Interference Management
39

40. LTE-A: Goals
• Greater flexibility with wideband deployments
• Wider bandwidths, intra-band and inter-band carrier aggregation
• Higher peak user rates and spectral efficiency
• Higher order DL and UL MIMO
• Flexible deployment using heteregenous networks
• Coordinated macro, pico, remote radio heads, femto, relays, Wi-Fi
• Robust interference management for improved fairness
• Better coverage and user experience for cell edge users
bps  bps/Hz  bps/Hz/km2
Towards Hyper-Dense Networks
40

41. 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
41
• 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

42. 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
42
e.g., Why not offload the UE to
the picocell ? Source: DOCOMO

43. 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
43

44. 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
44

45. 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
45

46. Inter-cell Interference Coordination
ABS
• Inter-cell interference coordination is necessary for effective femto/pico deployment
• Almost blank subframe (ABS)
• During defined subframes, the aggressor cell does not transmit its control + data
channel to protect a victim cell
• ABS pattern transmitted via X2 (dynamic) for macro/pico
• Macro/pico aggressor/victim
or via OAM (semi static) for macro/femto (=victim/aggressor)
• Issues with the UEs who should know
those patterns + detect weak cells.
• Common reference, sync and primary broadcast
should be protected
• Co-existence of legacy and new devices in pico CRE zone
• Need for enhanced receivers for interference suppression of
residual signals transmitted by macro cells
Macro-BS
Small
cell UE
Femto BS
Aggressor
Macro UE
Victim
FBS DL
Macro DL
Data
transmission
No TXABS
Macro Pico
Legacy
device
New
device
Example of macro/femto ICIC through ABS 46

47. 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)

48.  No ICIC CRE results in low
data rate for cell-edge UEs
 Fixed CRE b= 6 dB is good
for cell-edge UEs
Fixed CRE b = 12 dB is
detrimental for ER PUEs
(why?)
 Mute ABS performs poorly due
to resource under-utilization
 On average 125% gain
compared to RP+23% compared
to Fixed CRE b = 12 dB
Inter-cell Interference Coordination –
Case study

49. Macro-BSMUE-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)

50. Inter-cell Interference Coordination
• 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
X2X2
How to distribute the primary and secondary CCs to
optimize the overall network performance?? 50
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

51. Inter-cell Interference Coordination -
Frequency Reuse
Protecting cell edge users using FFR
X2
X2
X2
X2
X2
X2
HFR
FFR
SFR
Static FFR vs. Reuse 1
51
• Several configurations exist (full, hard, soft, fractional) frequency reuse
• Requires coordination through message exchange (X2)
• Relative Narrowand Transmit Power Indicator (RNTP) for DL
• High Interference Indicator (HII) for UL
• Interference Overload Indicator (OI) for UL; reactive
• Frequency partitioning in HetNet  LTE Rel. 8/9
• Static FFR
• Partition the spectrum into subbands and assign a given subband to a cell in a coordinated
manner that minimizes intercell interference
• E.g., N=1/3 FFR yields improvements in terms of SINR albeit lower spectral efficiency
• Dynamic FFR
• Assignments based on interference levels/thresholds as well as scheduling users based on
CQI from users feedbacks.

52. 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 picoUE
picomacro UE
aggressor
victim
aggressor
victim
How/when to swap victim/aggressor roles?
52

53. Co-tier Interference Management
Macro-BS
Macro UE
Aggressor/Victim: small cell/small cell
FBS-2
FBS-1
FBS-3
 Resources are assigned by a central controller
 More efficient resource utilization than the
distributed approach
 Needs extra signaling between the BSs and the
controller
 Highly computational
 Resources are assigned autonomously by BSs
 Less complexity
 High signaling overhead
 Requires long time period to reach a stable resource allocation
 Low resource efficiency
53
• In dense network deployments, femto-to-
femto interference can be severe
• especially for cell edge users
• Assigning orthogonal resources among
neighboring femtocells protects cell edge UEs
albeig low spectral efficiency
• Need dynamic ICIC techniques which are
scalable to accommodate multiple Ues
• Key: Assign primary CCs and secondary CCs
depending on interference map, dynamic
interference mitigation through resource
partitioning
• Centralized vs. Decentralized approaches

54. - UE makes measurement
- Identifies its interfering neighbors according to a predefined SINR
threshold
• BSs send cell IDs of the interfering neighbors to the central
controller (through the backhaul)
• The central controller maps this information into an interference
graph where each node corresponds to a BS, and an edge
connecting two nodes represents the interference between two BSs
FBS-3
FBS-2
Centralized
controller
FBS-1
#3
#1,3
#2
Interference
Feedback
#3
#2
#1,3
- Using conventional Graph Col + GB‐DFR attains a
significant capacity improvement for cell‐edge Ues
- GB-DFR provides higher throuputs than Graph col.
- Nearly all UEs achieve an SINR exceeding 5 dB
Graph Coloring
5x5 grid case
DL
Focus on F2F
“Graph-Based Dynamic Frequency Reuse in Femtocell Networks,” IEEE VTC 2011 (DOCOMO)
Co-tier Interference Management (Centralized Approach)
Interfering Neighbor Discovery
54

55. Dynamic interference environment
- Number and position of neighbors change during the
Operation
- Fixed frequency planning is sub‐optimal
Dynamic assignment of resources!
Multi‐user deployment
- Users in same cell experience different interference
conditions
- Resource assignment should depend on UE measurements
to maximize resource utilization
 Classify resources according to their foreseen usages
Reserved CC
– Allocated to cell edge UEs
– Protected region
Banned CC:
– Interfering neighbors are restricted to use the RCC
allocated to the victim UE
– This guarantees desired SINR at cell edge UEs
Auxiliary CC:
– Allocated to the UEs facing less interference
– Neighbors are not restricted
– Increases resource efficiency, especially, for the
multi‐user deployments
FBS-3
FBS-2
A B C
A
B
C
FBS-1
FBS-2
FBS-3
CB
A
C
C
Example
“Decentralized interference coordination via autonomous component carrier assignment ,” IEEE
GLOBECOM 2011
Co-tier Interference Management (Distributed Approach)
3 CCs
55

56. • 5x5 grid model, 40 MHz system bandwidth
• Tradeoff between SINR and user capacity
• Proposed approach has more flexibility in assigning
component carriers according to its traffic
• The proposed approach outperforms the static schemes,
especially for cell edge users.
SINR improvements for users at the cost of lower capacity
 Extensions:
 Issues with convergence and scalabilities yet to be
addressed
 Multi-antenna extension
“Decentralized interference coordination via autonomous component carrier
assignment,” in proc. IEEE GLOBECOM 2011 (DOCOMO)
Co-tier Interference Management (Distributed Approach)
56

57. Part IV
Toward Self-Organizing
Small Cell Networks
57

58. 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 58

59. The feedback
foundations
The large
system
foundations
The statistical
inference
foundations
The dynamics
foundations
The intelligence
and protocol
foundations
The traffic
foundations
The economic
and legal
foundations
The uncertainty
foundations
The physics
foundations
The security
foundations
The coding
foundations
PhysicsGame Theory & Learning
Evolutionary Biology
Micro-economics
Queuing Theory
Wireless
Cryptography
Discrete Mathematics
Network
Information theory
Free Probability
Random Matrix Theory
Control Theory
Future Communication
Networks
Toward Self-Organization: Tools
We focus on game-theoretic/learning aspects
59

60. 60
Introduction
• What is Game Theory?
– The formal study of conflict or cooperation
– How to make a decision in an adversarial environment
– Modeling mutual interaction among agents or players that are rational
decision makers
– Widely used in Economics
• Components of a “game”
– Rational Players with conflicting interests or mutual benefit
– Strategies or Actions
– Solution or Outcome
• Two types
– Non-cooperative game theory
– Cooperative game theory
• Close cousins: Reinforcement learning

61. 61
Heard of it before?
• In Movies
• Childhood games
– Rock, Paper, Scissors:
which one to choose?
– Matching pennies:
how to decide on heads or tails?
• You have witnessed at
least one game-theoretic
decision in your life 

62. Non-cooperative game theory
• Rational players having conflicting interests
– E.g. scheduling in wireless networks
• Often…
– Each player is selfish and wishes to maximize his payoff or
‘utility’
• The term ‘utility’ refers to the benefit that a player can
obtain in a game
• Solution using an equilibrium concept (e.g., Nash), i.e., a
state in which no player has a benefit in changing its strategy
• Misconception: non-cooperative is NOT always competition
– It implies that decisions are made independently without
communication, these decisions could be on cooperation!
62

63. Nash Equilibrium
• Definition: A Nash equilibrium is a strategy profile s*
with the property that no player i can do better by
choosing a strategy different from s*, given that every
other player j ≠ i .
• In other words, for each player i with payoff function ui
, we have:
• Nash is robust to unilateral deviations
– No player has an incentive to change its strategy
given a fixed strategy vector by its opponents
63

64. 64
Example: Prisoner’s dilemma
• Two suspects in a major crime held for interrogation in separate
cells
– If they both stay quiet, each will be convicted with a minor offence and
will spend 1 year in prison
– If one and only one of them finks, he will be freed and used as a witness
against the other who will spend 4 years in prison
– If both of them fink, each will spend 3 years in prison
• Components of the Prisoner’s dilemma
– Rational Players: the prisoners
– Strategies: Confess (C) or Not confess (NC)
– Solution: What is the Nash equilibrium of the game?
• Representation in Strategic Form

65. Prisoner’s Dilemma
P2 Not Confess Confess
P1 Not Confess -1,-1 -4,0
P1 Confess 0,-4 -3,-3
Nash EquilibriumPareto optimal
65
• P1 chooses NC, P2’s best response is C
• P1 chooses C, P2’s best response is C
• For P2, C is a dominant strategy

66. Design Consideration
• Existence and Uniqueness
Utility of
player 2
given
strategies
of players
1 and 2
Utility of player 1 given strategies of
players 1 and 2
Pareto optimalityNash equilibrium?
-Convexity/concavity of payoff
function
- Best response is standard function
(positivity, monotonicity,
scalability)
-Potential game
66

67. Non-cooperative Games
• Pure vs. mixed strategies
– Existence result for Nash in mixed strategies (1950)
• Complete vs. incomplete information
• Zero-sum vs. Non zero-sum
• Non zero-sum are games between multiple players
– Two player games are a special case
• Matrix game vs. continuous kernel games
• Static vs. Dynamic
– Evolutionary games
– Differential games
– …..
67

68. More on NC games
• Refinements on Nash
– To capture wireless characteristics or other stability
notions
• Stackelberg game
– Important in small cell networks due to hierarchy
• Correlated equilibrium
– Useful for coordinated strategies
• Special games
– Potential/Supermodular games (existence of Nash)
• Bayesian games, Wardrop equilibrium
• …..
68

69. Cooperative Game Theory
• Non-cooperative games describe situations where the
players do not coordinate their strategies
• Players have mutual benefit to cooperate
• Namely two types
– Nash Bargaining problems and Bargaining theory
– Coalitional game
• Bargaining theory
• For both
– Applications in wireless networks are numerous
69

70. Bargaining Example
Rich Man Poor Man
Can be deemed unsatistifactory
Given each Man’s wealth!!!
Might be a
better scheme ! !
Bargaining theory
and the Nash
bargaining solution!
I can give you 100$ if
and only if you agree
on how to share it
70

71. Coalitional Games
• Definition of a coalitional game (N,v)
– A set of players N, a coalition S is a group of cooperating players
– Worth (utility) of a coalition v
• In general, v(S) is a real number that represents the gain resulting
from a coalition S in the game (N,v)
– User payoff xi : the portion of v(S) received by a player i in coalition S
• Characteristic form
– v depends only on the internal structure of the coalition
• Partition form
– v depends only on the whole partition currently in place
• Graph form
– The value of a coalition depends on a graph structure that connects the
coalition members
71

72. CF vs. PF
In Characteristic form: the
value depends only on
internal structure of the
coalition
72

73. Cooperative Games
Class I: Canonical Coalitional
Games
Class II: Coalition Formation Games Class III: Coalitional Graph
Games
1
3
2
4
1
3
4
2
1 3
2
4
- The grand coalition of all users is an optimal structure.
-Key question “How to stabilize the grand coalition?”
- Several well-defined solution concepts exist.
- The network structure that forms depends on gains and costs from cooperation.
-Key question “How to form an appropriate coalitional structure (topology) and
how to study its properties?”
- More complex than Class I, with no formal solution concepts.
- Players’ interactions are governed by a communication graph structure.
-Key question “How to stabilize the grand coalition or form a network
structure taking into account the communication graph?”
Solutions are complex, combine concepts from coalitions, and non-
cooperative games
73/124

74. • For a general N-player game, finding the set of NEs is
not possible in polynomial time!
• Unless the game has a certain structure
• We talk about learning the equilibrium/solution
• Some existing algorithms
– Fictitious play (based on empirical probabilities)
– Iterative algorithms (can converge for certain classes of
games)
– Best response algorithms
• Popular in some games (continuous kernel games for example)
– Useful Reference
• D. Fundenberg and D. Levine, The theory of learning in games, the
MIT press, 1998.
74
Learning in Games

75. Learning Algorithms
• Distributed Implementation/Algorithm
– Which information can be collected or exchanged
– How to obtain knowledge and state of system
– How to optimize action/strategy
• Distributed Implementation/Algorithm
– Convergence? Speed? Efficiency?
– Overhead and complexity
(communication/computation/storage)
Observe
Analyze and
learning
Optimize
Adapt Cognitive cycle
- Q-learning, fuzzy Q-learning
-Evolutionary based learning
- Non-regret learning
- Best response dynamics
- Gradient update
75

76. Examples:
Access Control in Small Cell Networks (Nash game)
User Association in Small Cell Networks (Matching
game)
Cooperative interference management
(Coalitional game)
76

77. To Open or To Close?
77
FUE FUE
Base Station
Closed accessOpen access for one FAP

78. 78
To Open or To Close?
• Tradeoff between allocating resources and
absorbing MUEs/reducing interference
• Optimizing this tradeoff depends on the locations of
the MUEs, the number of interferers, etc.
• The choices of the FAPs are interdependent
– If an FAP absorbs a certain MUE, it may no longer be
beneficial for another FAP to open its access
• So, Open or Closed?
– Neither: Be strategic and adapt the access policy
– Noncooperative game!

79. 79
Formally…the Femto Problem
• Consider the uplink of an OFDMA system with
– M underlaid FAPs, 1 FUE per FAP, and N MUEs
– Assuming no femtocell-to-femtocell interference
– An MUE connects to one FAP
– For simplicity, we use subbands instead of subcarriers, i.e., each FAP
has a certain contiguous band that it can flexibly allocate
• Noncooperative game
– Players: FAPs
– Strategies: close or open access (allocate subbands)
– Objective: Maximize the rate of home FUE (under a constraint)
Fraction of subband
allocated by FAP m to MUE n
Coupling of actions in SINR
(next slide)

80. 80
Formally…
• Zoom in on the SINR:
• Game solution: Nash equilibrium
• Does it exist?
– Oh not again 
- Coupling of all FAPs actions
- Only MUEs not absorbed by others
are a source of interference
- Discontinuity in the utility function

81. 81
Existence of Nash equilibrium
• Common approaches for finding a Nash equilibrium mostly
deal with nicely behaved functions (e.g., in power control,
resource allocation games, etc.)
– Discontinuity due to open vs. closed choice
• P. J. Reny (1999) showed that for a game with discontinuous
utilities, if
– The utilities are quasiconcave
– The game is better-reply secure, i.e.,
• Our game satisfies both properties => Pure strategy Nash
exists
Non-equilibrium vector
Strategy of an
arbitrary FAP m

82. 82
Simulation results (1)
A mixture of
closed
and open access
emerges at
equilibrium

83. 83
Improved
performance
For the worst-case
FAP (equilibrium
is a more
fair scheme
than all-open)
Simulation results (2)

84. Access point assignment in
small cell networks
84
 A macro-cellular wireless
network
 A number of small cell base
stations
 Different cell sizes
 A number of wireless users
seeking uplink transmission
 How to assign users to access
points?
 More challenging than
traditional cellular
networks

85. Access point assignment
• The problem is well studied in classical cellular networks but..
– ..most approaches focus on the users point of view only in the presence
of one type of pre-fixed base stations
– Do not account for different cell sizes and offloading
• New challenges when dealing with small cell base stations
• Three decision makers with different often conflicting objectives:
– Small cells who want to ensure good QoS, Improve macro-cell coverage
via offloading (cell range expansion)
– Users that want to optimize their own QoS
– Macro-cells seeking to ensure connectivity
• Can we address the problem using a fresh small cell-oriented
approach?
– “A College Admissions Game for Uplink User Association in Wireless
Small Cell Networks”, IEEE INFOCOM 2014 85

86. Student A
Access point assignment as a matching
game
1- Student A
2- Student B
1- U Miami
2- FIU
How to match students (workers) to colleges (employers)?
How to assign wireless users to access points (SCBS and macro) ?
86Student B
1- FIU
2- U Miami
1- Student B
2- Student A

87. 87
Simulation results
Performance
advantage
increasing
with the
users density

88. 88
Notes and Future Extensions
• Adapts to slow mobility by periodic re-runs as well as to
quota changes and users leaving or returning
• Can we design a college admissions game that can handle
fast dynamics, i.e., handovers?
– Combine with dynamic games
• How to accommodate traffic and advanced schedulers?
– Use concepts from polling systems and queueing theory
• Ideally, we can build a matching game that enable us to
design heterogeneous networks where assignment is made
based on preferences and service types!
– Explore new dimensions in network design and resource allocation
– Different classes of matching games to exploit

89. Cooperative Interference Management
• We consider the downlink problem
• Femto access points can form a coalition to share the
spectrum resource (i.e., subchannels), reducing the co-tier
interference
``Cooperative Interference Alignment in Femtocell Networks,'‘ IEEE Trans. on Mobile Computing, to
appear, 2012
Macro base
station
Femto
access
point
f4
Macro
users
f1
f2
f3
m2
m1
Coalition S2
Coalition S1
89

90. • Coalition formation game model
– Players: Femto access points
– Strategy: Form coalitions
– Value of any coalition
Transmission rate
Interference from
femto access points
not in the same coalition
Interference from
macrocell
Cooperative Interference Management
90

91. • Not all femto access points can form coalition, since they may
not be able to exchange coalition formation information
among each other
• Cooperation entails COSTS
• We model it via power for information exchange (more
elaborate models needed)
Macro base
station
Femto
access
point
f4
Macro
users
f1
f2
f3
m2
m1
Coalition S2
Coalition S1
Cooperative Interference Management
91

92. Cooperative Interference Management
Chance of cooperation is small
(information cannot be exchanged
among femto access points)
Many femto access points
can form coalition
Too congested
92
Solution is
co-opetition...

93. Learning how to self-organize in a
dense small cell network?
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)
93

94. 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
94

95. 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
95

96. ..”Behavioral” Rule..
- History
- Cumulated
rewards
Play a given
action
Ultimately,
maximize the
long-term
performance
...
FBS
Should i explore?
Should i exploit?
96

97. Basic Model
Maximize the long-term transmission rate of every
femtocell (selfish approach)
SINR of MUE
SINR of FUE
97

98. • 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
98

99. • 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
99

100. • 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
100

101. 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
101Altruism vs. Selfishness
Myopic vs. Foresighted

102. Second scenario
• 6 MUEs, 6 RBs, K=60 FBSs
Average femtocell spectral efficiency vs. time for SON and best response learning algorithm
0 1 2 3 4 5 6 7 8 9 10
1.5
1.6
1.7
1.8
1.9
2
2.1
2.2
Convergence Time x 1000
AverageSpectralEfficiency(bps/Hz)
SON1
SON2
SON3
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
102

103. Now, let us add some 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.
103

104. © 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

105. © Centre for Wireless Communications, University of Oulu
(Classical) Regret-based learning procedure
Player k would have obtained a higher performance
By ALWAYS playing action
e.g.,

106. © Centre for Wireless Communications, University of Oulu
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
..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

107. © Centre for Wireless Communications, University of Oulu
• 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

108. © Centre for Wireless Communications, University of Oulu
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
Boltzmann
distribution
Always positive!!
Decision function mapping past/history + cumulative regrets into future

109. © Centre for Wireless Communications, University of Oulu
Numerical Results
0.2 0.4 0.6 0.8 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
femtocell density in %
Averagefemtocellspectrallefficiency[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

110. • Can small cells self-organize in a decentralized manner?
Yes!
- no information exchange
- solely based on a mere feedback
- Robust to channel variations and imperfect feedbacks
- No synchronization is required unlike some other learning algorithms!
•Numerous tradeoffs are at stake when studying self-organization
•Open Issues:
•How to speed up convergence?
•Introduce QoS-based equilibria?
•Optimality is not always what operators want!!
Take Home Message
110

111. ”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.
111

112. 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 more contextual information: Offloading combined with long-term scheduling +
users’ contexts
Backhaul
LTE/WiFi (access level)
LTE/WiFi (backhaul level)
Cellular-WiFi Integration aka Inter-RAT Offloading

113. © Centre for Wireless Communications, University of Oulu
The cross-system learning framework is composed of the
following interrelated components:
• Subband selection, power level allocation, 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.
• Context-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)

114. © Centre for Wireless Communications, University of Oulu
Numerical Results
•SCBSs are uniformly distributed within each macro sector, while
considering a minimum MBS-SCBS distance of 75 m.
•The path-loss models and other set-up parameters were selected
according to 3GPP recommendations for outdoor picocells (model 1)
•NUE = 30 mobile UEs were dropped within each macro sector out
of which N_hotspots = 2/3 N_UE /K are randomly and uniformly
dropped within a 40 m radius of each SCBS, while the remaining UEs are
uniformly dropped within each macro sector.
•The traffic mix consists of different traffic models following the
requirements of NGMN
•The bandwidth in the licensed (resp. unlicensed) band is 5 MHz (resp. 20
MHz). The simulations are averaged over 500 transmission time intervals
(TTIs).

115. © Centre for Wireless Communications, University of Oulu
Numerical Results
Convergence of the cross-system learning algorithm vs. standard
independent learning
Oscillations

116. © Centre for Wireless Communications, University of Oulu
Total cell throughput
vs. number of users.
per UE throughput as a
function of the number of UEs.
• While in the macro-only case, cell edge UEs get low throughput gains, adding K = 2 small cells is shown to boost users’ cell
edge throughput under “HetNet” offload
• 50% increase in cell edge UE throughput with K = 2 multimode small cells “HetNet+WiFi”.
• small cell users benefit from the small cells’ multimode capability (K = 2 SCBSs) + gap further increases when adding more
small cells (K = 6 SCBSs).
• Offloading is shown to improve not only the performance of SCUEs, but also MUEs, for K = {2, 4, 6} SCBSs.

117. 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 — No Interoperability Between Services
800M 175M 250M 300M 100M 100M1.06B 1B+

118. Wireless Fabric
Social Fabric
People
Sensors &
Machines
Social
relationships &
ties
Social Influence
Crowd-Place
sourcing
Storage
Caching
Apps &
Contexts
Emotions
GPS
Foursquare
LBS
Cloud
computing
SDN Energy
Multidimensional
Big Data Analytics
Increasingly multi-dimensional complex networks

119. 119
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

120. When Social Meets Wireless
• Offline social network: use stable social relationships to offload
data traffic in the OffSN
• OnSN: probability that same content is requested
• “Exploring Social Ties for Enhanced Device-to-Device
Communications in Wireless Networks”, IEEE GLOBECOM
2013
120

121. 121
When Social Meets Wireless
• If a mobile user downloads a certain content,
what is the likelihood that his “social friend”
will request the same content?
– Indian buffet process!

122. 122
When Social Meets Wireless
• Customers => mobile users
• Content => the dishes, relationship => social
• We can “predict” who will share content =>
improve D2D performance and traffic offload

123. • Problem: Limited backhaul capacity, data- and control- plane separation
• Proposal: Proactive caching for 5G networks
• How?
• Leverage users’ predictable demands, storage, and social relationships to offload
the backhaul and minimize peak demand.
• Proactively caching strategic contents at the network edge (BSs and UEs) yields
significant gains.
•Tools
• Supervised and unsupervised Machine learning
• Clustering communities, classification and regression
• Predicting social ties and influence within a social community
• Learning and influencing users and contents over large graphs
Proactive Caching for 5G
It’s time to render
our networks more
intelligent than
EVER before!
Proactive Caching for 5G
"Social and Spatial Proactive Caching for Mobile Data Offloading", IEEE International Conference on
Communications (ICC), 2014.
”Exploring Social Networks for Optimized User Association in Wireless Small Cell Networks with Device-to-
Device Communications”, IEEE Wireless Communications and Network Conference (WCNC), 2014

124. MBS
LTE/WiFi
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
Where/when/what to cache?
Predictive/Proactive Networking

125. Part VI
Release 12 and Beyond
Open Issues
125

126. 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 ULDL UL UL UL
126

127. Release 12 and beyond
FDD
TDD
Hetero-
CA
Licensed
Band
f1 f2
UL DL
f3 f4
UL DL f5
UL
DL
f6
UL
DL
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
M2
F1
F3
P3
F4 F5
F2
F6
F7
F8
F9
F10
F11
F12
F13
F14
Source: LG Electronics127

128. Release 12 and beyond
• 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
Source: LG Electronics
• 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
Source: LG Electronics
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 linkMultiple
Antennas
UE 128

129. Release 12 and beyond
• 3D Beamforming
Source: KDDI
• 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
Source: LG Electronics
129
Source: Ericsson

130. Advertisements 
• Acknowledgement to Merouane Debbah, Francesco
Pantisano, Meryem Simsek, Stefan Valentin, and all our
collaborators
• Acknowledgement to funding agencies: NSF
• IEEE ICC’14: Workshop on Small cells June 2014
– Interested in SON techniques? => Book
130

131. Conclusions
• Small cell networks are likely to proliferate in next-
generation wireless systems
• Many technical issues to address: interference, topology,
self-organization, etc.
• New tools, inclusive of stochastic geomtery, game theory,
learning
• Co-existence of..
– Small and macro-cells
– Multiple games!
131
Small is Beautiful..
Our job is to make
it smarter!

132. 132
Finally….
Thank You
Questions?

133. References
133
• Jeffrey G. Andrews, Keynote at ICC SmallNet workshop, Ottawa, Canada, June 2012.
• Small Cell Forum, http://smallcellforum.org
• V. Chandrasekhar, J. G. Andrews, and A. Gatherer, “Femtocell networks: A survey," IEEE Comm. Mag., vol.
46, no. 9, pp. 59-67, Sept. 2008.
• G. d. La Roche, A. Valcarce, D. Lopez-Perez, and J. Zhang, ”Access control mechanisms for femtocells," IEEE
Commun. Mag., vol. 48, no. 1, pp. 33-39, Jan. 2010.
• W. Saad, Z. Han, R. Zheng, M. Debbah, and H. V. Poor, "A College Admissions Game for Uplink User
Association in Wireless Small Cell Networks," in Proc. of the IEEE International Conference on Computer
Communications (INFOCOM), Toronto, Canada, April 2014
• L. Wang, Z. Yongsheng, and W. Zhenrong, “Mobility management schemes at radio network layer for LTE
femtocells,“ Proc. IEEE 69th Vehicular Technology Conference, pp. 1-5, 26-29 April, 2009.
• Z. Haijun, W. Xiangming, W. Bo, Z. Wei, and S. Yong, “A novel handover mechanism between femtocell and
macrocell for LTE-based networks," Proc. 2nd International Conference on Communication Software and
Networks, pp. 228-231, 26-28 Feb. 2010.
• F. Pantisano, M. Bennis, W. Saad, M. Debbah, and M. Latva-aho, "Improving Macrocell - Small Cell
Coexistence through Adaptive Interference Draining," IEEE Transactions on Wireless Communications, to
appear, 2013.
• M. Rasti, A. R. Sharafat, and B. Seyfe, “Pareto-ecient and goal-driven power control in wireless networks: A
game-theoretic approach with a novel pricing scheme," IEEE/ACM Trans. Networking, vol. 17, no. 2, pp. 556-
569, April 2009.
• E. Altman, T. Boulogne, R. El-Azouzi, T. Jiminez, and L. Wynter, “A survey of network games in
telecommunications,” Computers and Operations Research, pp. 286-311, Feb. 2006.
• D. Fudenberg and J. Tirole, Game Theory. Cambridge, MA: MIT Press.
• F. Pantisano, M. Bennis, W. Saad, S. Valentin, and M. Debbah, "Proactive User Association in Small Cell
Networks via Collaborative Filtering," in Proc. of the 47th Asilomar Conference on Signals, Systems and
Computers, Pacific Grove, CA, USA, November 2013.

134. References
134
• F. Pantisano, M. Bennis, W. Saad, and M. Debbah, Spectrum leasing as an incentive towards uplink
interference mitigation in two-tier femtocell networks," IEEE JSAC, April 2012.
• S. Samarakoon, M. Bennis, W. Saad, and M. Latva-aho, "Opportunistic Sleep Mode Strategies in Wireless
Small Cell Networks," in Proc. of the IEEE International Conference on Communications (ICC), Mobile and
Wireless Networks Symposium, Sydney, Australia, June 2014.
• Y. Wang, K. Zheng, X. Shen, and W. Wang, “A distributed resource allocation scheme in femtocell
networks,“ in Proc. of IEEE VTC'11-Spring
• G. Cao et al., “An adaptive sub-band allocation scheme for dense femtocell environment," in Proc. of IEEE
WCNC'11.
• L. Tan et al., “Graph coloring based spectrum allocation for femtocell downlink interference mitigation," in
Proc. of IEEE WCNC'11.
• Y. Zhang, L. Song, W. Saad, Z. Dawy, and Z. Han, "Exploring Social Ties for Enhanced Device-to-Device
Communications in Wireless Networks," in Proc. of the IEEE Global Communications Conference
(GLOBECOM), Wireless Networking Symposium, Atlanta, GA, USA, December 2013.
• F. Pantisano et al., “Interference avoidance via resource scheduling in TDD underlay femtocells," in Proc. Of
IEEE PIMRC'11.
• Y. Shi et al., “On resource reuse for cellular networks with femto- and macrocell coexistence," in Proc. Of
IEEE Globecom'10.
• Z. Zhao, F. Zheng, A. Wilzeck, and T. Kaiser, “Femtocell spectrum access underlaid in fractional frequency
reused macrocell," in Proc. of IEEE ICC11.
• Y. Chen et al., “Complete interference solution with MWSC consideration for OFDMA macro/femtocell
hierarchical networks," in Proc. of IEEE WCNC'11.
• C. Kosta et al., “Flexible soft frequency reuse schemes for heterogeneous networks (macrocell and
femtocell),“ in Proc. of IEEE VTC'11-Spring.
• H.-C. Lee, D.-C. Oh, and Y.-H. Lee, “Mitigation of inter-femtocell interference with adaptive fractional
frequency reuse," in Proc. of ICC'10.

135. References
135
• O. Semiari, W. Saad, S. Valentin, M. Bennis, and B. Maham, "Matching Theory for Priority-Based Cell
Association in the Downlink of Wireless Small Cell Networks," in Proc. of the 39th IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014.
• M. S. ElBamby, M. Bennis, W. Saad, and M. Lava-aho, "Dynamic Uplink-Downlink Optimization in TDD-
based Small Cell Networks," in Proc. of the IEEE International Conference on Computer Communications
(INFOCOM), Workshop on Green Cognitive Communications and Computing Networks, Toronto, Canada,
April 2014
• M.-S. Kim, H. W. Je, and F. A. Tobagi, “Cross-tier interference mitigation for two-tier OFDMA femtocell
networks with limited macrocell information," in Proc. of IEEE Globecom'10.
• S. Barbarossa, S. Sardellitti, A. Carfagna, P. Vecchiarelli, «Decentralized interference management in
femtocells: A game-theoretic approach," in Proc. of ICST CrownCom'10 .
• J. Gora, K. I. Pedersen, A. Szufarska, and S. Strzy, ”Cell-specic uplink power control for heterogeneous
networks in LTE," in Proc. of IEEE VTC'10-Fall.
• X. Chu et al, “Decentralized femtocell transmission regulation in spectrum-sharing macro and femto
networks," in Proc. IEEE VTC'11-Fall.
• M. Morita, Y. Matsunaga, and K. Hamabe, “Adaptive power level setting of femtocell base stations for
mitigating interference with macrocells," in Proc. of IEEE VTC'10-Fall.
• V. Chandrasekhar, J.G. Andrews, Z. Shen, T. Muharemovic and A. Gatherer, Power control in two-tier
femtocell networks," IEEE Trans. Wireless Commun., vol. 8, no. 8, pp. 4316-4328, August 2009.
• T. Lan et al., “Resource allocation and performance study for LTE networks integrated with femtocells," in
Proc. of IEEE Globecom'10.
• S. Nagaraja, V. Chande, S. Goel, F. Meshkati, and M. Yavuz, ”Transmit power self-calibration for residential
UMTS/HSPA+ femtocells," in Proc. of WiOpt'11.
• K. R. Krishnan and H. Luss, “Power selection for maximizing SINR in femtocells for SINR in macrocell," in
Proc. of IEEE WCNC'11.
• Z. Huang et al., “Power control in two-tier OFDMA femtocell networks with particle swarm optimization,"
in Proc. of IEEE VTC'11-Spring.

136. References
136
• T. Lee, H. Kim, J. Park, and J. Shin, “An efficient resource allocation in OFDMA femtocells networks," in
Proc. of IEEE VTC'10-Fall.
• T. Kim and T. Lee, “Conference on Advanced Communication Technology, vol. 2, pp. 1047-1051, 7-10 Feb.
2010.
• D.-C. Oh, H.-C. Lee, and Y.-H. Lee, “Power control and beamforming for femtocells in the presence of
channel uncertainty," IEEE TVT, July 2011.
• H.-C. Lee, D.-C. Oh, and Y.-H. Lee, Coordinated user scheduling with transmit beamforming in the
presence of inter-femtocell interference," in Proc. of IEEE ICC'11.
• J. Zhu and H.-C. Yang, Interference control with beamforming coordination for two-tier femtocell networks
and its performance analysis," in Proc. of IEEE ICC'11.
• S. B. Halima, M. Helard, and D. T. Phan-Huy, “New coordination and resource allocation schemes for
uniform rate in femtocell networks," in Proc. of IEEE VTC'11-Spring.
• R. Mo, T. Q. S. Quek, and R. W. Heath Jr., “Robust beamforming and power control for two-tier femtocell
networks," in Proc. IEEE VTC'11-Spring.
• S. Park et al., ”Beam subset selection strategy for interference reduction in two-tier femtocell networks," IEEE
Trans. on Wireless Commun., Nov. 2010.
• X. Chu, Y. Wu, D. Lopez-Perez, X. Tao, “On providing downlink services in collocated spectrum-sharing
macro and femto networks," IEEE Trans. Wireless Commun., vol. 10, no. 12, December 2011.
• M. S. Jin, S. Chae, and D. I. Kim, “Per cluster based opportunistic power control for heterogeneous
networks," in Proc. IEEE VTC'11-Spring, Budapest, Hungary, May 2011.
• S. Guruacharya, D. Niyato, E. Hossain, and D. I. Kim, “Hierarchical competition in femtocell-based cellular
networks," Proc. IEEE Globecom'10, Miami, FL, USA, 6-10 Dec. 2010.
• Z. Zheng, J. Hamalainen, Y. Yang, “On uplink power control optimization and distributed resource allocation
in femtocell networks," in Proc. of IEEE VTC'11-Spring.
• X. Kang, Y.-C. Liang, and H. K. Garg, “Distributed power control for spectrum-sharing femtocell networks
using Stackelberg game," in Proc. of IEEE ICC'11.

137. References
137
• S. Kishore, L. J. Greenstein, H. V. Poor, and S. C. Schwartz, “Uplink user capacity in a CDMA system with
hotspot microcells: Eects of nite transmit power and dispersion," IEEE Trans. Wireless Commun., vol. 5, no.
2, pp. 417-426, Feb. 2006.
• N. Bambos, S. C. Chen, and G. J. Pottie, “Channel access algorithms with active link protection for wireless
communication networks with power control," IEEE/ACM Trans. Networking, vol. 8, no. 5, pp. 583-597,
Oct. 2000.
• M. Rasti, A. R. Sharafat, and B. Seyfe, Pareto-ecient and goal-driven power control in wireless networks: A
game-theoretic approach with a novel pricing scheme," IEEE/ACM Trans. Networking, vol. 17, no. 2, pp.
556-569, April 2009.
• E. Altman, T. Boulogne, R. El-Azouzi, T. Jiminez, and L. Wynter, “A survey of network games in
telecommunications," Computers and Operations Research, pp. 286{311, Feb. 2006.
• D. Fudenberg and J. Tirole, Game Theory. Cambridge, MA: MIT Press.
• Z. Han and K. J. R. Liu, “Noncooperative power-control game and throughput game over wireless
networks,“ IEEE Trans. Commun., vol. 53, no. 10, pp. 1625{1629, Oct. 2005.
• G. J. Foschini and Z. Miljanic, “A simple distributed autonomous power control algorithm and its
convergence," IEEE Trans. Veh. Technol., vol. 42, no. 4, pp. 641{646, Nov. 1993.
• M. Andersin, Z. Rosberg, and J. Zander, “Gradual removals in cellular PCS with constrained power control
and noise," Wireless Networks, vol. 2, pp. 27{43, 1996.
• J. W. Lee, R. R. Mazumdar, and N. B. Shro, “Downlink power allocation for multi-class wireless systems,“
IEEE/ACM Trans. Netw., vol. 13, no. 4, pp. 854-867, Aug. 2005.
• R. D. Yates, A framework for uplink power control in cellular radio systems," IEEE J. Sel. Areas Commun.,
vol. 13, no. 7, pp. 1341-1347, Sept. 1995.
• H. Ji and C.-Y. Huang, “Non-cooperative uplink power control in cellular radio systems," Wireless.
Networks, vol. 4, no. 3, pp. 233{240, 1998.
• C. U. Saraydar, N. B. Mandayam, and D. J. Goodman, “Pricing and power control in a multicell wireless data
network," IEEE J. Sel. Areas Commun., vol. 19, no. 10, pp. 1883{1892, Oct. 2001.

138. References
138
• L. Zhang, L. Yang, and T. Yang, “Cognitive interference management for LTE-A femtocells with distributed
carrier selection," Proc. IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall), pp. 1{5, 6-9 Sept.
2010.
• S.-Y. Lien, Y.-Y. Lin, and K.-C. Chen, “Cognitive and game-theoretical radio resource management for
autonomous femtocells with QoS guarantees," IEEE Trans. Wireless Commun., vol. 10, no. 7, pp. 2196-2206,
July 2011.
• M. Bennis, S. Guruacharya and D. Niyato, “Distributed learning strategies for interference mitigation in
femtocell networks," in Proc. of IEEE Globecom'11.
• F. Pantisano, M. Bennis, W. Saad, and M. Debbah, “Cooperative interference alignment in femtocell
networks," in Proc. of IEEE Globecom'11.
• C. H. Lima, M. Bennis, and M. Latva-aho, “Coordination mechanisms for stand-alone femtocells in self-
organizing deployments," in Proc. of IEEE Globecom'11.
• M. Bennis and D. Niyato, “A Q-learning based approach to interference avoidance in self-organized femtocell
networks," in Proc. of IEEE Globecom'10.
• A. G.-Serrano, L. Giupponi, and G. Auer, “Distributed learning in multiuser OFDMA femtocell networks,"
in Proc. of IEEE VTC'11-Spring.
• A. G.-Serrano and L. Giupponi, “Distributed Q-learning for interference control in OFDMA-based femtocell
• networks," in Proc. of IEEE VTC'10-Spring.
• F. Pantisano, M. Bennis, W. Saad, M. Debbah, and M. Latva-aho, "Interference Alignment for Cooperative
Femtocell Networks: A Game-Theoretic Approach," IEEE Transactions on Mobile Computing, vol. 12, no.
11, pp. 2233-2246, November 2013.
• Y. Chen, J. Zhang, P. Lin, and Q. Zhang, “Optimal pricing and spectrum allocation for wireless service
provider on femtocell deployment," in Proc. of IEEE ICC'11.
• F. Mazzenga, “Performance evaluation of spectrum sharing algorithms in single and multi operator
scenarios," in Proc. of IEEE VTC'11-Spring.