2 lte and beyond in a sharing economy

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2 lte and beyond in a sharing economy

  1. 1. 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
  2. 2. Beyond LTE-A Networks without Borders Sharing and macro-cells: efficiencies in coverage and capacity Sharing and small cells: MU-MIMO The road ahead
  3. 3. 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
  4. 4. • 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) SHARING • Interference tolerance • Massive MIMO • CoMP
  5. 5. Core research 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 ability to learn distributed and autonomous decision making transient ownership of resources
  6. 6. 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
  7. 7. An evolution… Infrastructure Infrastructure Infrastructure Service Provider Service Provider Service Provider Mobile Provider A Mobile Provider B Mobile Provider C
  8. 8. An evolution… Infrastructure Infrastructure Infrastructure Service Provider Mobile Provider A Service Provider Mobile Provider B Service Provider Mobile Provider B
  9. 9. An evolution… Infrastructure Infrastructure Service Provider Mobile Provider A Service Provider Mobile Provider B Service Provider MVNO C
  10. 10. Networks without Borders Infrastructure Wholesaler Resource Aggregator SERVICE (Call, Game, Content, ....) Service Provider Virtual Architect Traditional Mobile Operator Business or Enterprise Infrastructure Provider Household Infrastructure Provider Individual Infrastructure Provider 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
  11. 11. 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
  12. 12. 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)
  13. 13. Sharing and macro-cells: coverage and capacity Network shaping: Architect a network that meets the service requirements at a minimum resource cost
  14. 14. Coverage optimization min *𝑥 𝑗 ,𝑧 𝑖 + s.t.: 𝑐𝑗 𝑥𝑗 𝑗∈S a 𝑖𝑗 𝑥 𝑗 ≥ 𝑧 𝑖 , ∀𝑖 ∈ 𝑃 𝑗∈𝑆 1 − 𝑧 𝑖 log(Pr 𝜉 𝑖 = 0 ) ≥ log(𝑝) 𝑖∈𝑃 𝑥 𝑗 ∈ 0,1 , ∀𝑗 ∈ 𝑆 𝑧 𝑖 ∈ 0,1 , ∀𝑖 ∈ 𝑃 𝑟 𝑖𝑗 Where 𝑎 𝑖𝑗 = 𝕀( 𝑟 ∗ ≥ 1), 𝑝 denotes pre-specified reliability level and 𝜉 𝑖 denotes service 𝑖 request coming from pixel 𝑖
  15. 15. GSM900 BS GSM1800 BS UMTS BS Intra-operator Co-located [%] Inter-operator co-located [%] 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 Area BS Case study
  16. 16. 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.
  17. 17. 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)
  18. 18. Correlation in time • 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 Autocorrelation – Meteor data
  19. 19. Correlation in space • Hourly usage • The periodicity has disappeared I N  w i j  w  i ij j ij xi  x x j  x i xi  x • 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
  20. 20. 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
  21. 21. 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
  22. 22. MU-MIMO UE1 eNB 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
  23. 23. In small cell scenarios… • Fewer UEs per cell • Fewer UEs for MU-MIMO pairing 10 m 10 m 10 m 10 m 10 m • Denser deployment so more cells within range • If we reassign UEs between neighbouring cells, can we increase UE throughputs by creating additional MUMIMO pairs?
  24. 24. Simulation and results • 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, 𝑆𝐸 𝑎𝑣𝑒 ) • To put this in perspective: 35% increase in MU-MIMO gains from in 𝑆𝐸 𝑎𝑣𝑒 case (with 4UEs/small cell, DR = 0.2) … and even higher increases with fewer UEs/small cell
  25. 25. 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)
  26. 26. 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
  27. 27. Questions and discussion Papers… luizdasilva.wordpress.com On email… dasilval@tcd.ie ldasilva@vt.edu
  28. 28. Backup slides
  29. 29. Coordination for heterogeneous and multi-hop networks • Distributed spectrum sharing for multihop 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

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