LTE and Beyond in a Sharing Economy

Luiz DaSilva

Stokes Professor in Telecommunications, Trinity College Dublin

Professor, ECE, Virginia Tech

… with Danny Finn, Paolo Di Francesco, and Jacek Kibiłda

I International Workshop on Challenges and Trends for Broadband Mobile Networks – Beyond LTE-A

Campinas, Brazil, 6 November 2013

Beyond LTE-A Networks without Borders Sharing and macro-cells: efficiencies in coverage and capacity Sharing and small cells: MU-MIMO The road ahead

Beyond LTE-A

• Smartphones use 24x more data than regular phones

• Tablets use 122x more data than smartphones

• However, it is not feasible for operators to increase prices proportionally to this demand

• More bands licensed for LTE

• TVWS

• 3.5 GHz (US)

• 2.3 GHz (UK)

• 30 GHz

• Pico/femto cells

• Operator-deployed WiFi

• Ultra-dense cells in unlicensed spectrum (60 GHz)

• Interference tolerance

• Massive MIMO

• CoMP

S H A R I N G

Core research

transient ownership of resources

ability to learn distributed and autonomous decision

making

fundamental principles that will allow the wireless network of the future to evolve into new architectures characterized by increasing autonomy, resource sharing, and ubiquity of wireless services

Networks without Borders

• Network composed on the run from a pool of resources (spectrum, infrastructure, management services, …)

• Contributors to this pool range from households to small scale operators to traditional wireless providers

• Network exists, virtually, to provide specific services to a specific subscriber/user population

• Virtualization is a key component, leading to new entities (the resource aggregator, the virtual architect)

• New business models

An evolution…

Service Provider

Infrastructure

Mobile Provider A

Service Provider

Infrastructure

Mobile Provider B

Service Provider

Infrastructure

Mobile Provider C

An evolution…

Service Provider

Infrastructure

Mobile Provider A

Service Provider

Mobile Provider B

Infrastructure

Service Provider

Mobile Provider B

Infrastructure

An evolution…

Service Provider

Infrastructure

Mobile Provider A

Service Provider

Mobile Provider B

Infrastructure

Service Provider

MVNO C

Networks without Borders

Resource Aggregator

Virtual Architect

Service Provider

Traditional Mobile

Operator

Business or Enterprise

Infrastructure Provider

Household Infrastructure

Provider

Infrastructure Wholesaler

Individual Infrastructure

Provider

SERVICE (Call, Game, Content, ....)

Spectrum Provider

Resource Pool

L. DaSilva, J. Kibilda, T. Forde, P. di Francesco, and L. Doyle, “Customized Services over Virtual Wireless Networks: The Path towards Networks without Borders,” Proc. FMNS, July 2013

Increased efficiency and lower costs through…

❶ Incentives for the deployment of localized (small cell, primarily) infrastructure by medium-sized and small operators

❷ The ability to provide service over infra-structure that employs heterogeneous technologies, and has different properties and ownership

❸ Improved service in currently under-served areas

❹ The ability to offer virtual wireless networks with different associated quality of experience, at different price points

Current areas of investigation…

• Mechanisms and APIs to enable aggregation of resources

• Pricing and market models for a fully virtualized wireless network

• Incentives for continued investment in infrastructure

• Public interest rationales for regulation, to ensure competitive pricing and service level outcomes

L. E. Doyle, J. Kibilda, T. K. Forde, and L. A. DaSilva, “Spectrum without Bounds, Networks without Borders,” Proceedings of the IEEE, 2014 (submitted)

Network shaping: Architect a network that meets the service requirements at a minimum resource cost

Sharing and macro-cells: coverage and capacity

Coverage optimization

Where 𝑎𝑖𝑗 = 𝕀(𝑟𝑖𝑗

𝑟𝑖∗ ≥ 1), 𝑝 denotes pre-specified reliability level and 𝜉𝑖 denotes service

request coming from pixel 𝑖

min*𝑥𝑗,𝑧𝑖+ 𝑐𝑗𝑥𝑗𝑗∈S

s.t.: a𝑖𝑗𝑥𝑗𝑗∈𝑆

≥ 𝑧𝑖, ∀𝑖 ∈ 𝑃

1− 𝑧𝑖𝑖∈𝑃

log(Pr 𝜉𝑖 = 0 ) ≥ log (𝑝)

𝑥𝑗 ∈ 0,1 , ∀𝑗 ∈ 𝑆

𝑧𝑖 ∈ 0,1 , ∀𝑖 ∈ 𝑃

Case study

Are

a

BS

GSM

90

0 B

S

GSM

18

00

BS

UM

TS B

S

Intr

a-o

per

ato

r C

o-l

oca

ted

[%

]

Inte

r-o

per

ato

r co

-lo

cate

d [

%]

Warszawa 514 423 174 337 54.9 4.5

Wrocław 273 207 122 229 66.3 8.1

Olsztyn 74 56 37 68 79.7 5.4

Świdnica 29 27 13 20 65.5 6.9

Coverage sharing – efficiency results

Efficiency gain through infrastructure sharing for uniform deployment and Polish

case study; a) homogeneous power allocation, b) heterogeneous power allocation

J. Kibiłda and L. DaSilva, “Efficient Coverage through Inter-operator Infrastructure Sharing in Mobile Networks,” in Proc. of Wireless Days, November 2013.

Traffic dynamics

• Dataset from Irish operator (Meteor)

• Data sessions (2G/3G)

• Voice call records (2G/3G)

• More than 10.000 transmitters to be analyzed

• Better understanding of traffic dynamics in cellular networks

• Temporal characteristics

• Spatial characteristics

• Spatio-Temporal characteristics

• Correlation in demand

• Assess correlation in demand combining datasets from different operators (e.g. Meteor and O2) and other publicly available data (e.g. demographic data on population density)

Correlation in time

Autocorrelation – Meteor data

• Hourly usage • Clear daily trends – high peaks at 24h interval and low peaks 12h offset • The aggregated network traffic shows good temporal correlation • Individual base stations do not show the same good correlations, but they keep the periodicy

Correlation in space

• Hourly usage • The periodicity has disappeared • Binary weight coefficient (ij) to consider only base stations that are relatively close (distance < 2.5 km) • Overall correlation is small when considering the network as a whole • Smaller areas (e.g. Dublin) show higher correlation, but still relatively low

Morans I statistic –

Meteor data

I N

wijji

wijji xi x x j x

xi x i

Current areas of investigation…

• Quantifying the expected efficiency gain from increased resource sharing and its relationship to correlation in demand experienced by infrastructure providers

• Stochastic models of infrastructure deployment and study of the impact of different infrastructure density and distribution on the potential efficiency gains from sharing

• Game theoretic models of incentives and preferences from the different players in this architecture, capturing the geographic nature of wireless access resources

Sharing and small cells: MU-MIMO

Examples of sharing in small cells:

• Open subscriber groups • Small cells as infrastructure contributors to virtual

networks (Networks without Borders) • Small cells operating in shared spectrum (e.g., 3.5

GHz) • MU-MIMO across small cells

eNB

UE1

UE2

• With MU-MIMO, multiple UEs are spatially multiplexed on different beams within the same time/frequency resource block

• Co-scheduled users must have orthogonal precoders

MU-MIMO

• Fewer UEs per cell • Fewer UEs for MU-MIMO pairing

• Denser deployment so more cells within range • If we reassign UEs between neighbouring cells, can we

increase UE throughputs by creating additional MU-MIMO pairs?

10 m

10 m

10 m

10 m

10 m

In small cell scenarios…

• System level simulation for different small cell and user densities

• 3GPP Dual Stripe small cell scenario

• We found that: 4% of UEs get reassigned 9.6% gain to reassigned UEs 13.1% gain to target UEs

(DR = 0.2, 𝑆𝐸𝑎𝑣𝑒)

• To put this in perspective: 35% increase in MU-MIMO gains from in 𝑆𝐸𝑎𝑣𝑒 case

(with 4UEs/small cell, DR = 0.2)

… and even higher increases with fewer UEs/small cell

Simulation and results

Current areas of investigation…

• Refinements to the mechanism based on different UE scheduling methods

• Extension for energy efficiency applications • MU-MIMO-based UE reassignment

used to intelligently create empty cells which can be temporarily switched off

• Consideration of outdoor scenarios

D. Finn, H. Ahmadi, A. Cattoni, and L. A. DaSilva, “Multi-User MIMO across Small Cells,” IEEE ICC, 2014 (submitted)

The Road Ahead

• “The new status symbol isn’t what you own – it’s what you’re smart enough not to own” – Lynn Jurich

• We want to develop the resource

management mechanisms, interfaces, and economic models to enable sharing across technologies and ownership models

• We are evaluating our resource management approaches using real wireless deployment and usage data

On email… dasilval@tcd.ie

ldasilva@vt.edu

Papers… luizdasilva.wordpress.com

Questions and discussion

Backup slides

Coordination for heterogeneous and multi-hop networks

• Distributed spectrum sharing for multi-hop topologies and HetNets (relays, coexistence between small and large cells) • Adaptations: channel selection, transmit power • Goals: network-wide spectrum efficiency, fairness, network connectivity, coverage • Cooperative game theory, coalition formation

Types of coalition in equilibrium as a

function of link range

Z. Khan, S. Glisic, L. A. DaSilva, and J. Lehtomaki, “Modeling the Dynamics of Coalition Formation Games for Cooperative Spectrum Sharing in an Interference Channel,” IEEE Trans. on Computational Intelligence and AI in Games, 2011

J. E. Suris, L. A. DaSilva, Z. Han, A. B. MacKenzie, and R. S. Komali, “Asymptotic Optimality for Distributed Spectrum Sharing Using Bargaining Solutions,” IEEE Trans. on Wireless Communications, Oct. 2009