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

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

oOperators face an unprecedented increasing demand for mobile data traffic
o70-80% volume from indoor & hotspots already now
oMobile data traffic expected to grow 500-1000x by 2020
oSophisticated devices have entered the market
oIncreased network density introduces Local Area and Small Cells
oIn 2011, an estimated 2.3 million femtocells were already deployed globally, and this is expected to reach nearly 50 million by 2014
oExplosive online Video consumption
oOTT 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 CAPEX and OPEX
•Fiber (leased or built)
• 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
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time
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50% Macro and Pico; Semi-Static
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time
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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
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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
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5
6
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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
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10
MBS
SBS
Frame duration
1
2
3
4
5
6
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8
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10
1
2
3
4
5
6
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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

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

(Classical) Regret-based learning procedure
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
..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

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

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 %
Average femtocell spectrall efficiency [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

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

58
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

59
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 UL UL UL DL DL DL UL
65

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 Electr6on6 ics

• 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
Multiple and access link
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
68

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
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
24.11.2014
78

System Model
24.11.2014
79

Problem Formulation
24.11.2014
80

Fight through data scarcity..
24.11.2014
81

Solution concept
24.11.2014
82
After obtaining the estimated popularity matrix, All the estimated popular files are stored greedily until no storage space remains.

Numerical Results
24.11.2014
83
Satisfied requests and backhaul load vs. number of requests

Numerical Results
24.11.2014
84
Satisfied requests and backhaul load vs. storage constraint

Numerical Results
24.11.2014
85
Satisfied requests and backhaul load vs. Zipf popularity parameter

Leveraging D2D + Storage
24.11.2014
86
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
24.11.2014
87
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)
24.11.2014
88
Evolutions of satisfied requests and backhaul load vs. number of requests

Anticipatory Caching in Small Cell Networks: A Transfer Learning Approach
24.11.2014
89
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.

..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.

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)

Small Cell Networks - Current Research and Future Landscape

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