Selforganization on Cellular Wireless Network and WLAN

1
Self-organization on Cellular Wireless Network
and WLAN

• Paul Lin
• March 20, 2006

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Contents

• Overview of self-organization
• Self-organizing on Cellular wireless network
• Topology generation and dynamic routing
• Issues of self-organization
• Conclusion

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Challenges

• The size and scope of mobile wireless networks
  continue to grow with more users and devices
  distributed from homes, businesses, to city and
  world-wide. This is adding to spatiotemporal
  complexity of the network topology and dynamics.
• Due to unpredictability of the network, static
  setting is insufficient.

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Features of Self-Organization

• Adapting to real-time situation
• Optimal resource planning
• Self-management and cooperation

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Features of Self-Organization (II)

• Self-organization is not just distributed and
  localized control it is about the relationship
  between the behavior of individual entities and
  resulting structure and functionality of the
  overall system.
• The application of rather simple behavior at the
  microscopic level leads to sophisticated
  organization of the overall system emergent
  behavior

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Design Paradigms

• 1.Design local behavior rules that achieve
  global properties
• 2.Do not aim for perfect coordination exploit
  implicit coordination
• 3.Minimize long-lived state information
• 4.Design protocols that adapt to changes
• Christian Prehofer and Christian Bettstetter,
  DoCoMo Euro-labs, IEEE Communication, July 2005

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Protocols according to levels of
locality/coordination
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Design Paradigms putting together
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Overview of Cellular Wireless Network
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Self-Org of Base Stations

• BS is able to operate in a standalone fashion.
• BS collaborate with its peers.
• Probing phase
• (1)auto-configures its IP connectivity, subnet
  and uplink interface.
• (2)channel scan to detect other base stations
  in its immediate neighborhood.
• (3)contact neighbor stations through uplink,
  and integrates itself into the network-wide
  information exchange.
• Periodically performs channel scan to detect
  changes in its environment.

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Self-organizing technologies
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Adaptive cell sizing

• By decreasing the cell radius from 500 to 200m, a
  capacity increase of 33 is achieved for voice
  service
• Revenue-based cell size control adjusts beacon
  transmit power. Under congestion the cell will
  limit its service area and reduce the
  inter-base-station interference. This will enable
  it to serve more users closer to the base
  station. In light traffic conditions, it will
  expand and improve the coverage with cells
  overlapping the same area.
• Power control Ensure a certain quality of
  service is used, as well as improving capacity

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Fixed relay node
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Complex Behavior of Nodes

• Small World refers to a phenomenon where the
  average path length between nodes is small, the
  nodes are highly clustered, and connectivity
  distribution peaks at an average value and then
  decays exponentially.
• - the hypothesis that everyone in the world
  can be reached through a short chain of social
  acquaintances.
• Scale-free connectivity distributions can be
  represented by power-law form, which is
  independent of the size or scale of the network.

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Random vs. Scale-free
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Parameters of complex network

• Average path length (L)
• -average number of hops(edges) in the
  shortest path between two nodes
• Clustering coefficient (C)
• -average fraction of pairs of neighbors of a
  node that are also neighbors of each other
• Degree (K)
• -number of links connecting that node to the
  neighboring nodes

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Small world concept

• A small world Average path length(L) is small,
  and clustering coefficient is high.
• It is shown that randomly rewiring a few edges
  reduces the average distance between nodes, but
  little effect on the clustering coefficient.
• The degree distribution is exponential. Nodes
  with high connectivity are practically absent,
  power-law property is not observed.

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Scale-free model

• Real networks expand continuously by addition of
  new nodes, and new nodes attach preferentially to
  nodes that are already well connected.
• Figure shows starting with 3 nodes, and each step
  adding new node with 2 edges.

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Applying the model

• It is anticipated that infrastructureless,
  deployable, wireless relay stations will be used
  the addition to the cellular infrastructure to
  improve service to mobile users.
• The objective is to design a scale-free overlay
  FRN network for QoS purposes.

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Topology generating
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Scale-free result

• Internet clustering coefficient is measured to be
  greater then 0.18, and web is 0.1078.

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Dynamic Routing

• Method 1 Load balancing among only BSs.
• Method 2 Load balancing among FRNs and BSs with
  no change in destination BS
• Method 3 Load balancing among FRNs and BSs with
  change in Destination BS

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Routing experiment

• The location of mobile users are generated
  according to a uniform distribution.
• BSs3 , FRNs 50, K1

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Discussion issues

• 1. Cell configuration
• 2. Efficient Planning
• 3. Coordination on different nodes and layers

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Cell configuration

• The backbone of the wireless mobile network is
  the entry points to the networking, especially
  the cell concept with BSs at the center.
• Cells should be able to flexibly adjust its
  topological coverage to facilitate the flow of
  signals or packets.
• For example Cell-Dimensioning Algorithm

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example Cell-Dimensioning Algorithm
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example Cell-Dimensioning Algorithm
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example Cell-Dimensioning Algorithm(II)

• Cell boundaries before and after BSR x removed

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Efficient Planning

• Adaptation to resources with potential aspects,
  ex. Cost, capacity, traffic..etc.
• Dynamic routing
• Advanced modeling of reinforcement learning,
  which configure service coverage and system
  capacity dynamically to balance traffic loads
  among cells by being aware of the system
  situation.

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Example Integrated Cellular and Ad-hoc Relay
System
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Coordination on different nodes and layers
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SOPRANO

• A wireless multihop network overlaid with a
  cellular structure base station(BS), router(R),
  and terminals(T)
• Self-Organizing Packet Radio Ad-hoc Networks with
  Overlay (SOPRANO), IEEE Communications June 2002

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Conclusion

• Self-organization reduces costs, improves
  robustness, enhances effectiveness and
  performance, facilitates automatically
  utilization of cellular wireless networks.
• Its overall goal is to enhance QoS.

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References

• Self-Organization in Communication Networks
  Principles and Design Paradigms, Christian
  Prehofer and Christian Bettstetter, DoCoMo
  Euro-Labs, IEEE Communications Magazine July
  2005, p 78-85
• Self-organization in future mobile
  communications, by A. G. Spilling, A. R. Nix, M.
  A. Beach and T. J. Harrold, ELECTRONICS Xr
  COMMUNICA'IION ENGINEERING JOURNAL JUNE 2000
• Self-Management of Wireless Base Stations, Kai
  Zimmermann, Lars Eggert and Marcus Brunner,
  www.ambient-networks.org
• On the Design of Self-Organized Cellular Wireless
  Networks, Sudhir Dixit, Evs, en Yanmaz, and Ozan
  K. Tonguz, IEEE Communications Magazine July
  2005
• Self-Organizing packet Radio Ad Hoc Networks with
  Overlay (SOPRANO), Ali N. Zadeh and Bijan
  Jabbari, Raymond Pickholtz and Branimir Vojcic,
  IEEE Communications Magazine June 2002
• Reinforcement-learning-based self-organization
  for cell configuration in multimedia mobile
  networks, Ching-Yu Liao, Fei Yu, Victor C. M.
  Leung and Chung-Ju Chang, EUROPEAN TRANSACTIONS
  ON TELECOMMUNICATIONS,Euro. Trans. Telecomms.
  2005 16385397
• Applying Emergent Self-Organizing Behavior for
  the Coordination of 4G Networks Using Complexity
  Metrics, Lester T. W. Ho, Louis G. Samuel,
  Jonathan M. Pitts, Bell Labs Technical Journal
  8(1), 525 (2003)