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Dynamic frequency allocation in femtocells-based systems: algorithms and performance analysis

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  • 1. UNIVERSITÀDEGLI STUDI DI ROMA
    “TOR VERGATA”Corso di Dottorato in Ingegneria delle Telecomunicazioni e Microelettronica
    “Dynamic frequency allocation in femtocells-based systems: algorithms and performance analysis”
    Advisor:
    Prof. Francesco Vatalaro
    Co Advisor:
    Prof. Franco Mazzenga
    Ph. D. candidate:
    Remo Pomposini
  • 2. Summary
    • 3GPP and 4G femtocells
    • 3. Problem statement
    • 4. Dynamic Frequency Selection Algorithms
    • 5. Results
    • 6. Conclusions
    • 7. Other activities
    • 8. Publications
    2
  • 9. 3GPP and 4G femtocell
    • Smallcellular base station (BS) designed for use in a home or small business environment
    • 10. Works on licensed band(s)
    • 11. Typically supports 2 to 4 active mobile phones
    • 12. Allows service providers to extend service coverage indoors
    • 13. Extends BS to allow a simpler, self-contained deployment
    • 14. Provides improvements to both coverage and capacity
    3
  • 15. DSM and Cognitive radio
    • Dynamic Spectrum Management (DSM) is a set of techniques that is being researched and developed to improve performance of a communication network
    • 16. Cognitive radio
    • 17. The wireless node changes its transmission and/or reception parameters to communicate efficiently
    • 18. Executes active monitoring of several factors in the external and internal radio environment
    • 19. Radio frequency spectrum
    • 20. User behavior
    • 21. Network state
    4
  • 22. Problem statement and proposedsolutions
    5
  • 23. Problemstatement
    • Spatially Dense femtocell deployment
    • 24. High bit-rate nodes
    • 25. No accurate radio planning is possible
    • 26. Small number of channels areavailable
    • 27. Macro-to-femtointerference
    • 28. can be avoided by allocating separated channels
    • 29. Interferenceamongfemtocells
    • 30. …even if a dedicated frequency band is allocated to the femtocell network.
    • 31. harmful in highly dense HNB’s environments!
    • 32. can compromise the opportunity of enjoying high data rate services.
    6
  • 33. Toolsforfemtocellinterferencemitigation
    • Starting from local measurements on:
    • 34. Noise power
    • 35. Interference power on different bands
    • 36. Interference can be reduced by:
    • 37. Properly controlling the transmitted power (e.g. power control)
    • 38. Change of femtocell operating frequencies
    • 39. Idea: available operating frequencies for each femtocell can include all the bands allocated to the (several) mobile operators (e.g. mutual frequency exchange)
    7
  • 40. Cognitive Femtocells
    • Allow interaction/coordinationamongfemtocells to maximize the number of activefemtocellsfor a giveninterferenceenvironment
    • 41. Adoption of algorithms for intelligentfemto-BSs to dynamically set theiroperatingfrequencies
    • 42. Algorithmsrequirements:
    • 43. Shallconsider:
    • 44. CooperatingFemtocells
    • 45. No cooperationamongfemtocells
    • 46. Algorithmsshall be simple to implement i.e. nodrasticalchangesto existing standard shall be required
    • 47. Shall be based on the available data the single femtocell can measure locally
    8
  • 48. On the feasibilityof the proposedapproach
    9
    • Investigation on the actual radio mobile standards to infer on the modifications required in the femtocells in order to implement cognitive features
    • 49. Two operating modes of the femtocells needs to be considered:
    • 50. Femtocell startup phase
    • 51. Femtocell operating cycle (after startup)
  • Femtocell START-UP procedures
    10
    START-UP
    self-optimizationphase
    • On Start-Up Femtocell
    • 52. connects to the own operator network
    • 53. sets its RF parameters
    • 54. core network assisted
    • 55. self-setting RF parameters based on monitoring of the radio channels
    • 56. self-optimization phase
    • 57. Femtocell dynamically adapt its parameters to the changing environment conditions
    • 58. Simply to adapt to our scopes
  • DynamicFrequencySelectionAlgorithms
    • Twodifferentapproachesforfemtocellshavebeenproposed
    • 59. Greedy DFS algorithm (GDFS):
    • 60. femtocells are absolutelygreedy and aimtoimmediatelymaximizetheirSignal-to-InterferenceRatio (SIR) carelessofwhichoperatoroffers the lessinterferedchannel
    • 61. Operator-oriented DFS algorithm (ODFS):
    • 62. eachfemtocellusesitsown band until the QoSconditionisverified (SIR≥ρ0)
    • 63. SIR
    11
  • 64. DFS algorithms with Power Control
    • Each femtocell perform these tasks:
    • 65. If SIR > threshold
    • 66. Decrease Ptx in order to reach the target quality level
    • 67. Else
    • 68. Increase Ptx up to Pmax
    • 69. If Ptx>Pmax
    • 70. Interference measurement on f1,f2,…,fN
    • 71. If SIRop> threshold
    • 72. Select the i-th frequency (belonging to own operator)
    • 73. Else if SIRext > threshold
    • 74. Select the j-th frequency (belonging to a different operator)
    • 75. Else
    • 76. Sensing mode
    • 77. Sensing mode: programmed standby feature envisaged in order not to damage the other femtocells.
    • 78. Since the interference conditions prevent the femtocell from transmitting, it temporarily disables transmission and just performs spectrum sensing.
    12
  • 79. Results
    13
  • 80. ReferenceScenarios
    • 4 network topologies
    • 81. Random
    • 82. Regular grid
    • 83. Perturbedgrid
    • 84. Building
    • 85. Maximumtrasmittedpower, Pmax = 20 dBm
    • 86. DistanceFemtoBS-Terminal, DFBS-Terminal = 4 - 10 m
    • 87. Outdoor pathlossexponent = 3.2
    • 88. Indoor pathlossexponent = 2.8
    • 89. Downlinktransmission direction isconsidered
    • 90. Greedy data source
    • 91. SINR threshold : 9,4 dB – 16,4 dB
    • 92. Carrierfrequency, F = 1800MHz
    • 93. Band frequencyspacing,Δfc = 10 MHz
    14
  • 94. Network topologies 1/2
    15
    • Random
    • 95. Femtocells are random distributed in a area of 100 x 100 m2
    • 96. Regular grid
    • 97. Baseline for comparing the behavior and performance of DFS algorithms
    • 98. Perturbed grid
    • 99. Femtocells are positioned over a regular grid with random 2D displacement around their original point
  • Network topologies 2/2
    16
    • Building
    • 100. 6 floor residential building
    • 101. Floor area = 200 m2
    • 102. Floor height = 3 m
    • 103. Femtocells randomly located inside the apartments
    • 104. 1 femtocell per apartment
    • 105. low-medium density scenario
    • 106. 2 apartments per floor
    • 107. high density scenario
    • 108. 4 apartments per floor
  • Interference
    • Indoor pathloss
    • 109. Outdoor pathloss
    • 110. SIR in the case of regular grid topology with optimal frequency assignment
    17
  • 111. SIR PDF
    18
    • Regular grid with optimal frequency assignment
    • 112. Break of equilibrium
    • 113. Femtocells simultaneously fall below the threshold
    • 114. SIR distribution could be approximated as a delta function
    • 115. Femtocells at the borders spread the SIR distribution
    • 116. Regular gridrandom network
    • 117. Larger variance
  • Average SIR per femtocell
    • Low density scenario
    • 118. Random topology provide the worst performance
    • 119. High density scenario
    • 120. SIR in random topology is slightly better then the SIR in the perturbed grid
    19
    • DFS algorithms perform better than the initial random assignment
    • 121. GDFS outperform ODFS due to the greedy nature of femtocells aiming at maximizing their throughput
    • 122. Random topology (solid line)
    • 123. Perturbed Grid (dashed line)
    Average SIR per femtocellishigheruntil the twovaluesof the Poutbecamecomparable
    • With GDFS algorithm the throghput is very near to the optimal curve
    • 124. Regular Gridwith optimal frequency assignment (solid line)
    • 125. Regular Grid (dashed line) , Perturbed Grid (dotted line)
  • SpectrumSharingGain
    20
    • K number of active femtocells
    • 126. FOP set of frequencies assigned to one operator
    • 127. F set of frequencies shared by operators
    • 128. Simulation assumptions:
    • 129. Fixed Outage Level
    • 130. Constant number of
    femtocells per frequency
    Franco Mazzenga, Marco Petracca, Remo Pomposini, Francesco Vatalaro, Romeo Giuliano. Performance Evaluation of Spectrum Sharing Algorithms in Single and Multi Operator Scenarios. IEEE VTC2011-Spring International Workshop on Broadband evolved Femtocell Technologies, 15-18 May 2011, Budapest, Hungary.
  • 131. Reusedistance
    21
    • DFS algorithm
    • 132. performs an autonomous redistribution of the minimum frequency reuse distance
    • 133. tends to shrink the variance of the obtained distributions
    • 134. Reduce the effect of the most harmful interferers for the considered femtocell
  • Conclusions
    • The problemofinterferencebetweenfemtocells in a dense scenario hasbeenconsidered
    • 135. Oneeffectivesolutionbased on the adoption of algorithms for cognitive femtocellshasbeenproposed and their performance analyzedthorughsimulation
    • 136. Prosof the proposedsolution:
    • 137. Significantincreaseof the numberofactivenodes in the considered area
    • 138. Verysimpleimplementationof the algorithm due to the usageofelementarystrategiesnotrequiring the coordinationbetweenfemto-BSs
    • 139. Robustness against failure and defections from the algorithm
    • 140. GDFS algorithm allows to reach performance very close to the optimal case
    • 141. Future works:
    • 142. DFSA withcooperationbetweenfemtocells
    • 143. Studyofcomplexfemtocells network topologies and the rulesthatleadstothem
    22
  • 144. Educationactivities
    Courses
    Modulazione Numerica - Prof. Franco Mazzenga - Università Tor Vergata a.a. 2007/2008
    Writing a ScientificPaper - UniveritàTor Vergata 14, 15 e 17 Aprile, 2008
    Corso inter-dottorato su sistemi embedded, Aprile-giugno 2009, docenti vari, numero ore X
    Seminars
    Next Generation Networking: le tecnologie per l'accesso ultrabanda - Villa Mondragone, Università Tor Vergata - 31 Gennaio 2008 – Seminario
    Le nuove frontiere della qualità nei media digitali - TouradjEbrahimi - Fondazione Ugo Bordoni - 4 Novembre 2008
    Nuove frontiere nella gestione dello spettro radio – Joseph Mitola III – Fondazione Ugo Bordoni – 18 Giugno 2009
    School
    Scuola di Dottorato in Ingegneria dell'Informazione - Università di Napoli Federico II 25-29 Febbraio 2008 - 40 ore  
    Lipari Summer School on "Mobile Computing and Communications: Towards the Next Generation of Networks" - 17-24 Luglio 2010
    Didattica
    Cultore della materia “Software per le Telecomunicazioni”
    OFDM seminar within the course “Sistemi di radiocomunicazioni”
    23
  • 145. Researchactivities
    • WiMAX system
    • 146. Cognitive Radio
    • 147. Opportunistic Spectrum Access
    • 148. Dynamic Spectrum Management
    • 149. 3GPP and 4G Femtocellsytems
    24
  • 150. Publications 1/2
    25
    • International Journal
    • 151. Andrea Detti, Pierpaolo Loreti, Remo Pomposini, “On the Performance Anomaly in WiMAXnetworks”, Wiley Journal on Wireless Communications and Mobile Computing, SpecialIssue on Wireless Technologies AdvancesforEmergency and RuralCommunications, 2008, ISSN: 1530-8669, doi: 10.1002/wcm.677
    • 152. Book chapter
    • 153. Franco Mazzenga, Marco Petracca, Remo Pomposini, Francesco Vatalaro. ImprovingQoSofFemtocells in Multi-operatorEnvironments. Springer Book on Trustworthy Internet
    • 154. International Conference
    • 155. Franco Mazzenga, Marco Petracca, Remo Pomposini, Francesco Vatalaro, Romeo Giuliano. Performance Evaluation of Spectrum Sharing Algorithms in Single and Multi Operator Scenarios. IEEE VTC2011-Spring International Workshop on Broadband evolved Femtocell Technologies, 15-18 May 2011, Budapest, Hungary.
    • 156. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano. AlgorithmsforDynamicFrequencySelectionforFemto-cellsofDifferentOperators. In: 21st IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2010), Istanbul, Turkey, September 26-29 2010
    • 157. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro. Impact on QoSofFemtocellsDefectingfromDynamicFrequencySelectionAlgorithms. In: 21st International Tyrrhenian Workshop on DigitalCommunications (ITWDC 2010): Trustworthy Internet, Ponza, Italy, September 2-8
    • 158. F. Mazzenga, M. Petracca, R. Pomposini, R. Giuliano, M. Vari. An AlwaysAvailableControlChannelfor Cooperative Sensing in Cognitive Radio Networks. In: IFIP Wireless Days 2010, Venice, Italy, October 20-22 2010. DOI: 10.1109/WD.2010.5657704
    • R. Giuliano, F. Mazzenga, M. Petracca, R. Pomposini. Wireless Opportunistic Network Based on UWB forPreservingEnvironment. In: wetice, pp.192-196, 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructuresfor Collaborative Enterprises, 2010. DOI: 10.1109/WETICE.2010.36
    • 159. Marco Petracca, Franco Mazzenga, Remo Pomposini, Francesco Vatalaro, Romeo Giuliano. Impact of Control Channel Design on Cooperative Spectrum Sensing in Opportunistic Spectrum Access Networks. First International Conference on Advances in Cognitive Radio (COCORA 2011). 17-22 April 2011, Budapest, Hungary.
    • 160. Marco Petracca, Remo Pomposini, Francesco Vatalaro. Dynamic Spectrum Access Techniques for Preservation of Environment and Cultural Heritage. 20th IEEE International Conference on Collaboration Technologies and Infrastructures. June 27th - 29th, 2011, Paris, France.
    • 161. Andrea Detti, Claudio Loreti, Remo Pomposini, “OverlayBorůvkabasedAd-hocMulticastProtocol – Demonstration”, in ProceedingsofFifthAnnualMediterranean Ad Hoc Networking Workshop (Med-Hoc-Net 2006), Demo Session, Lipari, Sicilia, Italy, 14-17 Giugno 2006
    • 162. Andrea Detti, Remo Pomposini, Roberto Zanetti, “A cross layerversionof OBAMP based on a proactiverouting: description and MANET test-bed”, in Proceedingsof First IEEE conference on Wireless Rural and EmergencyCommunications (WRECOM 2007), Roma, 1-2 Ottobre 2007
    • 163. SubmittedPapers
    • 164. Franco Mazzenga, Marco Petracca, Remo Pomposini, Francesco Vatalaro, Romeo Giuliano. Opportunistic Spectrum Access based on Underlay UWB Signalling. 2011 IEEE International Conference on Ultra-Wideband. 14-15 September 2011, Bologna Italy.
    26
    Publications 2/2
  • 165. Regulatoryaspects
    27
    • Assumption:
    • 166. network operators make arrangements one with each other to allow the reciprocal exchange of operating frequency channels
    • 167. the simultaneous mutual interchange of spectrum bands among network operators is not currently permitted
    • 168. Policy programmefor the use of the European Union’s radio spectrum foresees an efficient and flexible spectrum management as well as the promotion of collective use of spectrum.
    • 169. Radio Spectrum Policy Programme (RSPP) encourages the development of standards able to avoid harmful interference by means of efficient spectrum usage techniques, especially when high density of radio devices occurs
  • Pout with 2 and 3 frequency bands
    • Random network
    • 170. GDFS and ODFS show similarperformances
    • 171. Perturbedgrid
    • 172. GDFS slightlyoutperforms the ODFS
    • 173. PC leadsto the best performance
    • 174. Pout= 5%, activefemtocells
    • 175. Random network: 14  68 femtocells
    • 176. Perturbedgrid: 66120 femtocells
    • 177. Increasing fiincrease the number of active femtocells per frequencies
    • 178. Random Network, Pout= 5%
    • 179. 2 freq: 34 served femtocells per frequency
    • 180. 3 freq: 47 active femtocells per frequency
    • 181. Regular grid
    • 182. On-off behavior of the outage
    28
    • Random topology (solid line) , Perturbed Grid (dashed line)
  • Defectionfrom the DFSA
    29
    • 2 cases
    • 183. Femtocells can or can not adopt the DFS algorithm
    • 184. Some femtocells are selfish
    • 185. They do not turn into sensing mode
    • 186. The selfish behaviour of femtocells does not cause a marked decrease of performance
    F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro. Impact on QoS of Femtocells Defecting from Dynamic Frequency Selection Algorithms. In: 21st International Tyrrhenian Workshop on Digital Communications (ITWDC 2010): Trustworthy Internet, Ponza, Italy, September 2-8