An introduction to Wireless Small Cell Networks


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An introduction to Wireless Small Cell Networks

  1. 1. An I t d ti t A Introduction to Wireless Small Ce Ne wo s W e ess S Cell Networks Mehdi Bennis and Walid Saad University of Oulu, Centre for Wireless Communications, Finland Electrical and Computer Engineering Department, University of Miami, Miami USA 1
  2. 2. Outline• Part I: Introduction to small cell networks – Introduction and key challenges• Part II: Network modeling – Baseline models and key tools (stochastic geometry)• Part III: Interference management – Interference in a heterogeneous, small cell environment – Emerging techniques for interference management• P IV: T Part IV Toward self-organizing small cell networks d lf i i ll ll k – Introduction to game theory and learning – Applications in small cells• Part V: Conclusions and open issues 2
  3. 3. Part IIntroduction to Small Cell Networks 3
  4. 4. Outline4/120
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  6. 6. What happens in one  hour? Around the globe, in one hour: – 685 million sms messages – 128 million Google searches – 9 million tweets pp – 1.2 million mobile apps downloaded – 2880 hours of YouTube videos uploaded – 50,000 smart phones activatedWe need innovative network designs to handle all of this! 6
  7. 7. Technology ConvergenceTechnology Convergence Computing C iWireless servicesDigital imaging Gaming TV and video 7
  8. 8. Main Implications 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? 8
  9. 9. Small Cell Networks – A Necessary Paradigm Shift Facts F Consumer behaviour is changing• Operators face an unprecedented increasing demand for mobile data traffic - More devices, higher• 70-80% volume from indoor & hotspots already now bit bi rates, always active l i - Larger variety of• Mobile data traffic expected to grow 500-1000x by 2020 traffic types e.g. Video, • 1000-times mobile traffic is expected in 2020 to 2023 MTC• Sophisticated devices have entered the market• Increased network density introduces Local Area and Small Cells• 2011, an estimated 2.3 million femtocells were already deployed globally, and this is expected to reach nearly 50 million by 2014 Ultimately, the only viable way of reaching “the promised land” is making cells smaller, denser and smarter Macrocell 9 Small Cells/Low power Nodes
  10. 10. In a nutshell….• 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 l i i d lf d i i i 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 @ London’s Hotpost Lamp Post Solar panel Olympics Games 10
  11. 11. In a nutshell…. D2D Femto-BS Characteristics • Wired backhaul Relay R l Characteristics • Resource reuse • User-deployed • Closed/open/hybrid • Operator‐assistedCharacteristics Major Issues access• Wireless backhaul • Neighbor discovery Major Issues• Open access • Offloading traffic • Femto-to-femto•OOperator‐deployed d l d interference and femto-to-Major Issues macro interference• Effective backhaul design• Mitigating relay to macrocellinterference D2D backhaul b kh l Macro-BS Femto-BS Relay Pico-BS Characteristics • Wired backhaul • Operator‐deployed • Open access Macrocells: 20 40 watts ll 20-40 Major Issues M j I •Offloading traffic from macro to picocells (large footprint) • Mitigate interference toward macrocell users 11
  12. 12. Standardization Efforts• 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 solutionsNext 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 12
  13. 13. Small Cell Access Policies• Three access policies • Closed access:  only registered users b l l i d belonging to a closed subscriber group ( i l d b ib (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 j• 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. 13
  14. 14. Small Cells vs. WiFi Friends or Foes? • Recent trials using a converged - Deployed to improve network coverage and gateway Wi-Fi/3G architecture improve capacity (closed access) showed how the technologies g - There i considerable planning effort f Th is id bl l i ff t from th the could be combined and exploited operator in deploying a femtocell network - Prediction: there will be more small cells than • Several companies are likely to devices! (Qualcomm CTW 2012) simultaneously introduce both y technologies for offloading. - A cheap alternative for data offloading - Availability f Wi-Fi A il bili of Wi Fi networks, hi h d k high data rates and lower cost of ownership has made it attractive for catering to increasing data demand  Small cells vs. Wi-Fi: - However, seamless interworking of Wi-Fi and - Managed vs. Best effort mobile networks are still challenging bil t k till h ll i - Simultaneously push both technologies for offloading Open ProblemHow to combine and integrate 3G/4G/Wi-Fi in a cost effective manner? 14
  15. 15. The Backhaul – a new bottleneck• The backhaul is critical for small cell base stations • Low-cost backhaul is key!• What is the best solution?• Towards h d heterogeneous small cell b kh l options ll ll backhaul i• 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• Fib (l Fiber (leased or b ilt) d built) •  high capacity •  recurring charges, availability and time to deploy• Non-Line of sight (NLOS) multipoint microwave g ( ) p •  good coverage, low cost of ownership •  low capacity, spectrum can be expensive+ possibly TV White Space... Milimeter wave backhaul currently a strong potential Milimeter-wave Proactive caching ~30-40% savings (source: Intel) 15 Sub 6 GHz Point-to-Multipoint Backhaul Links
  16. 16. Summary of Challenges Summary of Challenges Radio resource management and Inter-cell interference coordination i t f di ti Modeling and analysis Self-organization, self-optimization g p Security Self-healingAnd many more.. Backhaul-aware RRM for small cell networks Handover and mobility management Energy Efficiency and power savings (green small cells) Intra-RAT offloading, inter-RAT offloading ( g (tighter coordination) ) Cell association and load balancing 16
  17. 17. Summary of Challenges 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, Ultimately can we have a multi-tier wireless network that is multi tier built in a plug-and-play manner? 17
  18. 18. Challenges in SCNs – Radio Resource Management and Inter-cell interference coordination Macro-BS DL Macro-BS UL Macro UE  Macro UE Small cell UE Small cell UE  Small cell BS Small cell BS Aggressor/Victim: small cell/macro Aggressor/Victim: macro/small cell• DL interference from the small cell BS to nearby Macro UE • UL interference from nearby macro UE to small cell BS• A Macro UE far from its MBS will be affected the most • A macro UE far from its MBS causes interference toward the small cell Macro UE inside / near femto coverage 18
  19. 19. Challenges in SCNs – Radio Resource M R di R Management and I t t d Inter-cell i t f ll interference coordination di ti Macro-BS DL Macro-BS UL Small cell BS  Small cell BS  Small cell UE Small cell UE Macro UE Macro UE Aggressor/Victim: macro/small cell Aggressor/Victim: small cell/macro• DL interference from nearby Macro-BS to small cell UE • UL interference from small cell UE to nearby Macro-BS• Interference from nearby Macro-BS can lower SINR of • Many active small cell UEs can cause severe interference to the small cell UE Macro BS Macro-BS Small cell very close to Macro base station 19
  20. 20. Challenges in SCNs – Radio Resource M R di R Management and I t t d Inter-cell i t f ll interference coordination di ti Macro-BS DL Macro-BS UL Small cell BS Small cell BS Small cell BS Small cell BS Macro UE Macro UE Aggressor/Victim: small cell/small cell Aggressor/Victim: small cell/small cell• DL interference among nearby small cell networks • UL interference among nearby small cell networks (co-tier) interference among small cell networks 20
  21. 21. Challenges in SCNs – Mobility management and handover Mobility h M bilit enhancement for tf 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 the mobility with SON features needs to be studied?  How long to wait ? What is the threshold? etc  disruptive to standard scheduling LPN LPN Macro LPN Too late HO• UE mobility is faster than the HO parameter settings Too early HO Wrong cell HO 21• HO triggered when the signal strength of the source cell is too low
  22. 22. Challenges in SCNs – Self-Organizing Networks (SONs) S lf O i i N t k (SON )• Traditional ways of network optimization using manual processes, staff monitoring KPIs, maps, trial and errors unreasonable i SCNs! i l d i bl in• Self-organization and network automation is a necessity not a privilege. Why?• Femtocells (pico) are randomly (installed) deployed by users (operators) need fast d lf  d f t and self-organizing capabilities i i biliti• Need strategies without human intervention• Self-organization helps reduces OPEX• Homogeneous vs. Heterogeneous deployments g g p y every cell behaves differently Individual parameter for every cell SON is crucial for enhanced/further enhanced-ICIC, mobility management, load balancing, etc.. 22
  23. 23. Challenges in SCNs – Energy Effi i E Efficiency • 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 Dynamic neighboring cell expansion ll i Dynamic cell ON/OFF Switch OFF Macro-BS Macro-BS Small Cell range expansion cellSwitch OFF for power savings Small cell Active Mode Energy harvesting is also a nice trait of HetNets! e.g., autonomous network configuration properties 23 converting ambient energy into electrical during sleep mode
  24. 24. Part IINework Modeling inSmall Cell Networks 24
  25. 25. Current Cellular Models Developing analytically tractable models for cellular systems is very difficult • Stochastic Geometry (StoGeo) has been used in i cellular networks with h ll l t k ith hexagonal b l base station model, i.e., macrocell base stations (grid-based).Wyner model was predominantly used in the 1990’s • Too idealized; used in Information Theory (IT) • used in Academia for tractability and analysisWith advent of heteregeneous and dense small cellnetworks, random and spatial models are needed• HHexagonal models f i l obsolete l d l fairly b l• Need to model HetNets to characterize performance metrics (Operators want pointers!!) Operators pointers • Transmission , rate, coverage, g, outage g probability • Ease of simulation 25 Source: J. Andrews, keynote ICC Smallnets, 2012.
  26. 26. Current Cellular Architectures Nuts and Bolts • 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? 26
  27. 27. Baseline Downlink Model (1-tier) coverageprobability Aggregate interference at tagged receiver ......First, let us look at the coverage probability in a 1-tier setting 27
  28. 28. Coverage Probability (1-tier)Where Incredibly simple expressions 28 Source: J. Andrews, keynote ICC Smallnets, 2012.
  29. 29. How accurate is this model?• Fairly accurate, even for traditional di i l planned l d cellular networks.• Industry is somewhat reluctant to use these models due to possible difficulty in system level simulations 29
  30. 30. Moving on to K-tier Hetnets Aggregate interference at tagged receiver 30
  31. 31. K-Tier Small Cell NetworksTheorem 2 [Dhillon, Ganti, Bacelli ’11]: The coverage probability for a typicalmobile user connecting to the strongest BS, neglecting noise and assuming Rayleighfading: 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, Interestingly same as K=1 tier 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 31 Source: J. Andrews, keynote ICC Smallnets, 2012.
  32. 32. How accurate is the K-tier model? Source: J. Andrews, keynote ICC Smallnets, 2012. 32
  33. 33. 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 • Pi Picocells and macrocells: questionable since b h are operator d l d ll d ll i bl i both 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) 33
  34. 34. 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• Most importantly, operators want pointers for their network deployments. 34
  35. 35. Part IIIInterference Management g 35
  36. 36. 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 36
  37. 37. 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• LTE A (Rel 10 11) LTE-A (Rel. 10-11) •Multiple CCs available •Frequency domain ICIC over multiple CCs is possible Frequency •Time domain ICIC within 1 CC is also possible •Much greater flexibility of interference management Source: Ericsson 37
  38. 38. ICIC in LTE-A: Overview• Way to get additional capacity  cell splitting is the way to go about it• 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?  I R l In Release-8 LTE picocells are added where users 8 LTE, i ll dd d h associate to strongest BS.  Inefficient   Release-10 techniques with enhanced solutions are proposed  Cell range expansion (CRE)  AAssociate t cells th t ” k sense” i t to ll that ”makes ”  Slightly weaker cell but lightly loaded e.g., Why not offload the UE to the picocell ? 38 Source: DOCOMO
  39. 39. Inter-cell Interference Coordination• ICIC and its extensions are study items in SON A combination Orthogonal Orthogonal g thereof + transmission, transmission, coordination Almost Blank Carrier beamforming, Subframe, Cell aggregation, coordinated Range R Cell Range C ll R scheduling, scheduling joint Expansion, etc Expansion, etc transmission, DCS, etc Time-Domain Frequency- Spatial Domain ICIC Domain ICIC ICIC 39
  40. 40. Inter-cell Interference Coordination - Time Domain Increased footprint of pico p p When macro frees up resources• Typically, users associate to base stations with strongest SINR • BUT max-SINR is not efficient in SCNs Pico • Cell range expansion (CRE) ? Pico Macro • Mandates smart resource partitioning/ICIC solutions• Bias operation intentionally allows Subframes reserved for picocell transmission UEs to camp on weak (DL) pico cells • RSRP = Reference signal g Limited footprint of p p pico due received power (dBm) To macro signal • Pico (serving) cell RSRP + Bias = Macro (interfering) cell RSRP•Need for time domain subframe partitioningbetween macro/picocells Pico Pico Macro• In reserved subframes, macrocell does nottransmit any data •Almost Blank Subframes (ABS) + duty cycle Subframes reserved for macrocell transmission 40
  41. 41. Inter-cell Interference Coordination - Time Domain (Static) Time-Domain Partitioning• (St ti ) Ti D i P titi i 50% Macro and Pico; Semi-Static • Negotiated between macro and picocells via backhaul (X2) Macro DL 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 • Macro cell frees up certain p Pico DL Pi 0 1 2 3 4 5 6 7 8 90 1 2 3 4 5 6 7 8 9 subframes (ABS) to minimize Data interference to a fraction of UEs No transmission time transmission served by pico cells • All picocells follow same pattern Inefficient in high loads with non- #1 uniform Ues #1 Macro Pico • Duty cycle: 1/10,3/10,5/10 etc • Reserved subframes used by multiple small cells 25% Macro and Pico; Adaptive • Increases spatial reuse Macro DL 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9• Adaptive Time-Domain Partitioning Pico DL 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 • Load balancing is constantly time performed in the network Possible Data • Macro and picocells negotiate No transmission transmission transmission partitioning based on spatial/temporal traffic distribution. 41
  42. 42. 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) New • Issues with the UEs who should know device those patterns + detect weak cells cells. Macro Pico • Common reference, sync and primary broadcast Legacy should be protected device • 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 DL FBS DL Macro UE Victim Femto BSSmall Data Aggressor No TXABScell UE Macro-BS transmission Example of macro/femto ICIC through ABS 42
  43. 43. Inter-cell Interference Coordination• Further enhanced ICIC (feICIC) for non-CA based deployment • Some proposals under discussion X2 X2 Macro Pico • At the transmitter side in DL  combination of ABS + power reduction (soft-ABS) • At the receiver side in DL use of advanced receiver cancellation • 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 Cross scheduling g interference management How to distribute the primary and secondary CCs to optimize the overall network p p performance?? 43
  44. 44. Inter-cell Interference Coordination - Frequency Reuse• Several configurations exist (full hard soft fractional) frequency reuse (full, hard, soft,• 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. HFR X2 X2 FFR X2 X2 X2 SFR X2 44 Static FFR vs. Reuse 1 Protecting cell edge users using FFR
  45. 45. Inter-cell Interference Coordination - Frequency Reuse • From operators’ viewpoint a co channel deployment is operators viewpoint, co-channel highly desirable due to limited and scarce bandwidth • Co-channel deployment  high interference • Assigning reuse-1 in the macrocell and femtocell networks yields high interference • Interference mitigation is crucialfractional frequency reuse (FFR) is one (potential) solution • In terms of spatial reuse, it is not better than reuse-1 but improves cell edge conditions in the outer region • Sniffing carried out by femtocells Dual stripe Source: SAGEM Interference mitigation scheme FFR in the macro increases with higher antenna configurations 45 FFR at macro is beneficial to both macro and femto tiers
  46. 46. Inter-cell Interference Coordination - Carrier Aggregation• C i aggregation i used i LTE A via C Carrier ti is d in LTE-A i Component t 100 MHz Carriers (CCs) CC1 CC2 CC3 CC4 CC5• Macro and Pico cells can use separate carriers to freq. avoid strong interference g• Carrier aggregation (CA) allows additional flexibility to manage interference  Macrocells transmit at full power on anchor CC1 CC2 CC3 Macro carrier (f1) and lower power on second carrier (f2), etc CC2 CC1 CC3 Pico  Picocells use second carrier (f2) as anchor carrier freq.  Partitioning ratio limited by number of carriers But trend is changing aggressor  (in some cases) Aggressor is victim and victim is aggressor macro UE CC1 pico victim victim How/when to swap victim/aggressor roles? CC2 macro UE S pico 46 aggressor
  47. 47. Co-tier Interference Management• I d In dense network d l t k deployments, f t t t femto-to- femto interference can be severe • especially for cell edge users• Assigning orthogonal resources among g g g g neighboring femtocells protects cell edge UEs albeig low spectral efficiency Macro-BS FBS-1• Need dynamic ICIC techniques which are scalable to accommodate multiple Ues• Key: Assign primary CCs and secondary CCs depending on interference map, dynamic FBS-2 interference mitigation through resource FBS-3 partitioning Macro UE• Centralized vs. Decentralized approaches Aggressor/Victim: small cell/small cell Resources are assigned by a central controller More efficient resource utilization than thedistributed approach  Resources are assigned autonomously by BSs Needs extra signaling between the BSs and the  Less complexitycontroller  High signaling overhead g g g Highly computational  Requires long time period to reach a stable resource allocation  Low resource efficiency 47
  48. 48. Co-tier Interference Management (Centralized Approach) Interfering Neighbor Di I t f i N i hb Discovery Feedback - UE makes measurement - Identifies its interfering neighbors according to a predefined SINR #3 Interference #3 threshold • BSs send cell IDs of the interfering neighbors to the central FBS-1 #2 controller (through the backhaul) • The central controller maps this information into an interference graph where each node corresponds a BS, and an edge connecting #1,3 #2 FBS-3 two nodes represents th i t f t d t the interference b t between two BSs t BS FBS-2 5x5 grid case Centralized DL controller Focus on F2F #1,3 Graph Coloring- GB‐DFR attains a significant capacity improvement forcell‐edge UEs, at the expense of a modest decrease forcell‐centre users- Nearly all UEs achieve an SINR exceeding 5 dB 48“Graph-Based Dynamic Frequency Reuse in Femtocell Networks,” IEEE VTC 2011
  49. 49. Co-tier Interference Management (Distributed Approach)Dynamic interference environment 3 CC CCs A B C- Number and position of neighbors change during theOperation- Fixed frequency planning is sub‐optimal FBS-1 A CDynamic assignment of resources! FBS-2 BMulti‐user deployment- Users in same cell experience different interference FBS-3 Cconditions- Resource assignment should depend on UE measurementsto maximize resource utilization Classify resources according to their foreseen usages CReserved CC A – Allocated to cell edge UEs – Protected regionBanned CC: – Interfering neighbors are restricted to use the RCC B C FBS-3 allocated to the victim UE FBS-2 – This guarantees desired SINR at cell edge UEsAuxiliary CC: Example – Allocated to the UEs facing less interference – Neighbors are not restricted – Increases resource efficiency, especially, for the multi‐user deployments “Decentralized interference coordination via autonomous component carrier assignment ,” IEEE GLOBECOM 2011 49
  50. 50. Co-tier Interference Management (Distributed Approach) • 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 users. SINR i improvements f users at the cost of lower capacity for h fl i  Extensions:  Issues with convergence and scalabilities yet to be addressed  Multi-antenna extension“Decentralized interference coordination via autonomous component carrierassignment,” in proc. IEEE GLOBECOM 2011 50
  51. 51. Part IIIToward Self-Organizing Small Cell Networks 51
  52. 52. 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 Small picocell picocell footprint footprint with fewer with more users users 52
  53. 53. Toward Self‐Organization: Tools Game Theory & Learning The intelligence Physics and protocol The dynamics foundations The physics foundations foundations Evolutionary Biology Random Matrix Theory The economic The large and legal system foundations foundations Micro‐economics Free Probability Future Communication The statistical The traffic Networks inference foundations f d ti foundations Network Queuing Theory Information theory The security The uncertainty foundations foundations The feedback The coding Wireless foundations foundations Discrete Mathematics Cryptography Control Theory We focus on game-theoretic/learning aspects 53
  54. 54. Introduction• What is Game Theory? – The formal study of conflict or cooperation h f l d f fli i – 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” game – Rational Players with conflicting interests or mutual benefit – Strategies or Actions – Solution or Outcome• Two types – Non-cooperative game theory No coope at ve ga e t eo y – Cooperative game theory• Close cousins: Reinforcement learning 54
  55. 55. Heard of it before? Heard of it before?• In Movies• Childhood games – Rock, Paper, Scissors: which one to choose? – Matching pennies: how to d id h t decide on heads or tails? h d t il ?• You have witnessed at least l t one game-theoretic th ti decision in your life  55
  56. 56. 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 p p y communication, these decisions could be on cooperation! 56
  57. 57. Nash Equilibrium• Definition: A Nash equilibrium is a strategy profile s* with the property that no p y i can do better by p p y player y 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 57
  58. 58. Example: Prisoner s dilemma 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 d l f h fi k h ill b f d d d i 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 58
  59. 59. Prisoner’s Dilemma Pareto optimal Nash Equilibrium P2 Not Confess ConfessP1 Not Confess -1,-1 -4,0P1 Confess 0, 4 0 -4 -3 -3 3, 3 • P1 chooses NC P2’s best response is C NC, P2 s • P1 chooses C, P2’s best response is C • F P2 C i a d i For P2, is dominant strategy t t t 59
  60. 60. Design Consideration• Existence and Uniqueness -Convexity/concavity of payoff function - Best response is standard function (positivity, monotonicity, scalability) -Potential game Utility of p y player 2 given strategies Nash equilibrium? Pareto optimality of players 1 and 2 Utility of player 1 given strategies of players 1 and 2 60
  61. 61. Non‐cooperative Games• Pure vs. mixed strategies – Existence result for Nash in mixed strategies (1950) ste ce esu t o Nas ed st ateg es ( 950)• Complete vs. incomplete information• Zero-sum vs. Non zero-sum Zero sum 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 vs – Evolutionary games – Differential games – ….. 61
  62. 62. 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 y g , p q• ….. 62
  63. 63. Cooperative Game Theory• Non-cooperative games describe situations where the p aye s players do not coo d ate their strategies ot coordinate t e st ateg es• Players have mutual benefit to cooperate• Namely two types – Nash Bargaining problems and Bargaining theory – Coalitional game• Bargaining theory g g y• For both – A li ti Applications in wireless networks are numerous i i l t k 63
  64. 64. Bargaining Example  Bargaining theoryI can give you 100$ if and the Nashand only if you agree d l bargaining solution!on how to share it CanMight be a unsatistifactory be deemed Given each Man’s ! better scheme ! wealth!!! Rich Man Poor Man 64
  65. 65. Coalitional Games 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 – vd depends only on the i t d l th internal structure of the coalition l t t f th liti• 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 65
  66. 66. CF vs. PF CF vs. PFIn Characteristic form: thevalue depends only oninternal structure of thei t l t t f thcoalition 66
  67. 67. Cooperative Games Cooperative Games - Players’ interactions are governed by a communication graph structure. Players -Key network structure that forms depends on gains and costs from cooperation. - The question “How to stabilize the grand coalition or form a network structure The grand coalition of communication graph?” -Key question “How to formthe all users is an optimal structure. (topology) and - taking into account an appropriate coalitional structure how to study question “How combine concepts from coalitions, and non- Solutions are complex, to stabilize the grand coalition?” -Key its p p y properties?” - More complexl than Class I, with ti formal solution concepts. cooperative games fi d solution concepts exist. -SSeveral well-defined l no ll d t it67/124
  68. 68. Learning in Games• 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 i l ih ( f i l f 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. 68
  69. 69. 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 Observe - Q-learning, fuzzy Q-learning -Evolutionary based learning Analyze andAdapt Cognitive cycle g y learning - Non-regret learning - Best response dynamics - Gradient update Optimize• Di ib d Implementation/Algorithm Distributed I l i /Al i h – Convergence? Speed? Efficiency? – O erhead and complexity Overhead comple it (communication/computation/storage) 69
  70. 70. Examples:Access Control in Small Cell Networks (Nash game)User Association in Small Cell Networks (Matching game) Cooperative interference management (Coalitional game) 70
  71. 71. To Open or To Close?To Open or To Close? Base Station OpenClosed access FAP access for one 71
  72. 72. To Open or To Close? 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. h h b fi f• 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• S O So, Open or Cl d? Closed? – Neither: Be strategic and adapt the access policy –NNoncooperative game! ti ! 72
  73. 73. Formally…the Femto Problem 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• N Noncooperative game i – Players: FAPs – Strategies: close or open access (allocate subbands) – Objective: Maximize the rate of home FUE (under a constraint) Fraction of subband Coupling of actions in SINR allocated by FAP m to MUE n (next slide) 73
  74. 74. Formally…• Zoom in on the SINR: - Coupling of all FAPs actions - Only MUEs not absorbed by others are a source of interference - Discontinuity in the utility function• Game solution: Nash equilibrium• Does it exist? – Oh not again  74
  75. 75. Existence of Nash equilibrium Existence of Nash equilibrium• Common approaches for finding a Nash equilibrium mostly deal with nicely behaved functions (e.g., in power control, (e g 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 h ili i i – The game is better-reply secure, i.e., Non-equilibrium vector qStrategy of anarbitrary FAP m• O game satisfies both properties => Pure strategy Nash Our i fi b h i P N h exists 75
  76. 76. Simulation results (1)A mixture ofclosed l dand open accessemerges atequilibrium 76
  77. 77. Simulation results (2)ImprovedperformanceFor the worst-caseFAP (equilibriumis a morefair hf i schemethan all-open) 77
  78. 78. Access point assignment in  small cell networks ll ll k  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 78
  79. 79. Access point assignment 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• C we address th problem using a f h small cell-oriented Can dd the bl i fresh ll ll i t d approach? 79
  80. 80. Access point assignment as a matching  game 1- Student B 1- Student A 2- Student 2 St d t A 2- Student BHow to match students (workers) to colleges (employers)?How to assign wireless users to access points (SCBS and macro) ? 1- U Miami 1- FIU 2- FIU 2- U Miami Student A Student B 80
  81. 81. Simulation resultsPerformanceadvantageincreasingwith theusers density 81
  82. 82. Notes and Future Extensions• Adapts to slow mobility by periodic re-runs as well as to quota changes and users l i or returning h d leaving i• 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 – Diff Different classes of matching games to exploit l f hi l i 82
  83. 83. 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 Coalition S1 Macro m1 users m2 Coalition S2 Macro base station f1 Femto access point f2 f4 f3``Cooperative Interference Alignment in Femtocell Networks,‘ IEEE Trans. on Mobile Computing, toappear, 2012 83
  84. 84. Cooperative Interference Management• Coalition formation game model – Players: Femto access points – Strategy: Form coalitions – V l of any coalition Value f li i Transmission rate Interference from Interference from femto access points macrocell not in the same coalition 84
  85. 85. Cooperative Interference Management• 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) Coalition S1 Macro m1 users m2 Coalition S2 Macro base station f1 Femto access point f2 f3 f4 85
  86. 86. Cooperative Interference ManagementChance of cooperation is small(information cannot be exchanged Many femto access pointsamong f t access points) femto i t) can f form coalition liti Too congested Solution is co-opetition... 86
  87. 87. Learning how to self-organize in a dense small cell network?”Decentralized Cross-Tier Interference Mitigation in Cognitive Femtocell Networks," IEEE International Conference on Communications (ICC), Kyoto, Japan, June 2011. 87
  88. 88. Toward Evolved SONFemtocell 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 localinformation, 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 !!) 88
  89. 89. Solution (in a nutshell)Due to their fully-decentralized nature, femtocells need to:- Estimate their long-term utility based on a feedback (from their UEs) long term- 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 (i) (ii) sequentially - Inefficient - Model-based learning. 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 f E femtocell i d ll independently optimizes i own metric and there i no d l i i its i d h is coupling between femtocell’s strategies (correlation-free); • for correlation/coordination  other tools are required 89
  90. 90. ..”Behavioral” Rule.. FBS - History Ultimately,- Cumulated Play a given maximize the rewards action ... long-term performance Should i explore? Should i exploit? 90
  91. 91. Basic Model SINR of MUE SINR of FUE fMaximize the long-term transmission rate of every femtocell (selfish approach) 91
  92. 92. Game Model• 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 t, set of actions following a probability distribution: 92
  93. 93. Information Aspects• 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 ( p y y)• 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)  Maximize the long-term performance utility + Entropy/Perturbation perturbation 93
  94. 94. Proposed SON Algorithm• At every time t, every FBS k jointly estimates its long-term utility function and updates its transmission probability over all carriers: Utility estimation Strategy optimization Learning parameters Other SON variants can be derived in a similar way Both procedures are p done simultaneously! !!! This algorithm converges to Players learn their utility faster than the the so-called epsilon-close Nash Optimal strategy 94
  95. 95. Numerical Results First scenario Parameters 2 MUEs, 2 RBs, K=8 FBSs Macro BS TX power Femto BS TX power •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  Convergence of SON 1 learning algorithms with respect to the Best NE. The temperature parameter has a considerable impact on the performance Altruism vs. Selfishness 95 Myopic vs. Foresighted
  96. 96. Numerical ResultsSecond scenario SON1 SON-RL• 6 MUEs, 6 RBs, K=60 FBSs 2.2 SON2: SON1(+imitation) 2.1 Average Spectral Efficiency (bps/Hz) SON1 2 SON2 SON3: Best response y SON3 1.9 - no history 1.8 - myopic (maximize performance at every S 1.7 time instant) 1.6 1.5 15 0 1 2 3 4 5 6 7 8 9 10 Convergence Time x 1000 Average femtocell spectral efficiency vs. time for SON and best response learning algorithm SON1 outperforms SON2 and SON3 Being foresighted yields better performance in the long term 96
  97. 97. Now, let us add some implicit coordination among small cells”Learning Coarse correlated equilibria in small cell networks," IEEE International Conference on Communications (ICC), Ottawa, Canada, June 2012. 97
  98. 98. The Cross‐Tier GameThe cross-tier interference management problem is modeled as a normal-form gameAt each time instant, every small cell chooses an action from its finite set of action following a probability distribution: © Centre for Wireless Communications, University of Oulu
  99. 99. (Classical) Regret-based learning procedure e.g., Player k would have obtained a higher performance y g p By ALWAYS playing action © Centre for Wireless Communications, University of Oulu
  100. 100. Regret‐based Learning Given a vector of regrets up to time t, Every small cell k is inclined towards taking actions yielding highest regret, i.e., g g , ,..From..From perfect world to reality... reality...In classical RM, each small cell knows the explicit expression of its utility functionand it observes the actions taken by all the other small cells  full information Impractical and non scalable in HetNets © Centre for Wireless Communications, University of Oulu
  101. 101. Regret‐based Learning• Remarkably, one can design variants of the classical regret matching procedure which requires no knowledge about other p y players’ actions, and yet yields closer performance. How? , y y p• (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!) © Centre for Wireless Communications, University of Oulu
  102. 102. Exploration vs. ExploitationThe 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. p gThe solution that maximizes the behavioral rule is: Boltzmann distribution Always positive!! Decision function mapping past/history + cumulative regrets into future © Centre for Wireless Communications, University of Oulu
  103. 103. Numerical Results 0.8 bps/Hz] 0.7 ocell spectrall efficiency [b 0.6 0.5 0.4 2X increase 0.3Average femto reuse 1 0.2 reuse 3 0.1 SON-RL; [Bennis ICC11] regret-basedA 0 0.2 0.4 0.6 0.8 1 femtocell density in % Average femtocell spectral efficiency versus the density of femtocells for SON learning algorithms. © Centre for Wireless Communications, University of Oulu
  104. 104. Take Home Message• 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 - N synchronization i required unlike some other l No h i i is i d lik h learning algorithms! i l ih !•Numerous tradeoffs are at stake when studying self-organization•Open Issues: Open •How to speed up convergence? •Introduce QoS-based equilibria? •Optimality i not always what operators want!! O i li is l h !! 104
  105. 105. Part VIRelease 12 and Beyond Open Issues 105
  106. 106. Release 12 and beyond • 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 f2 f1/booster Macro-BS FUE LTE multiflow / inter site CA Small cell BS Soft-Cell concepts Non-fiber based connection TDD Traffic Adaptive DL/UL Configuration• Depends on traffic load and distribution DL is dominant i d i t• Interference mitigation is required for alignment Macro-BS Of UL/DL • Flexible TDD design DL UL UL UL UL is dominant DL DL DL UL 106