Recent advance in communications


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Recent advancement in communication

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Recent advance in communications

  1. 1. Recent Advances in Wireless Small Cell Networks Mehdi Bennis and Walid Saad University of Oulu, Centre for Wireless Communications, Finland Electrical and Computer Engineering Department, University of Miami, USA 1
  2. 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. 3. Part I Introduction to Small Cell Networks 3
  4. 4. Outline 4/120
  5. 5. Outline 5/120
  6. 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. 7. Technology Convergence Wireless services Digital imaging TV and video Computing Gaming 7
  8. 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. 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. 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. 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. 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. 13. DOCOMO’s View (the CUBE) Reference: METIS
  14. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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. 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. 28. Part II Nework Modeling & Analysis in Small Cell Networks 28
  29. 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. 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. 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. 32. Coverage Probability (1-tier) Where Incredibly simple expressions 32 Source: J. Andrews, keynote ICC Smallnets, 2012.
  33. 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. 34. Moving on to K-tier Hetnets Aggregate interference at tagged receiver 34
  35. 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. 36. How accurate is the K-tier model? Source: J. Andrews, keynote ICC Smallnets, 2012. 36
  37. 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. 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. 39. Part III Interference Management 39
  40. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 57. Part IV Toward Self-Organizing Small Cell Networks 57
  58. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 72. CF vs. PF In Characteristic form: the value depends only on internal structure of the coalition 72
  73. 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. 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. 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. 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. 77. To Open or To Close? 77 FUE FUE Base Station Closed accessOpen access for one FAP
  78. 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. 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. 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. 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. 82 Simulation results (1) A mixture of closed and open access emerges at equilibrium
  83. 83. 83 Improved performance For the worst-case FAP (equilibrium is a more fair scheme than all-open) Simulation results (2)
  84. 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. 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. 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. 87 Simulation results Performance advantage increasing with the users density
  88. 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. 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. 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. 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. 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. 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. 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. 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. 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. 97. Basic Model Maximize the long-term transmission rate of every femtocell (selfish approach) SINR of MUE SINR of FUE 97
  98. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 115. © Centre for Wireless Communications, University of Oulu Numerical Results Convergence of the cross-system learning algorithm vs. standard independent learning Oscillations
  116. 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. 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. 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. 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. 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. 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. 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. 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. 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. 125. Part VI Release 12 and Beyond Open Issues 125
  126. 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. 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. 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. 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. 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. 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. 132 Finally…. Thank You Questions?
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