Transcript Slide 1

Self-organization on Cellular
Wireless Network and WLAN
Paul Lin
March 20, 2006
Contents
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Overview of self-organization
Self-organizing on Cellular wireless
network
Topology generation and dynamic
routing
Issues of self-organization
Conclusion
Challenges
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The size and scope of mobile wireless
networks continue to grow with more
users and devices distributed from
homes, businesses, to city and worldwide. This is adding to spatiotemporal
complexity of the network topology and
dynamics.
Due to unpredictability of the network,
static setting is insufficient.
Features of Self-Organization
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Adapting to real-time situation
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Optimal resource planning
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Self-management and cooperation
Features of Self-Organization (II)
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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
Design Paradigms
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#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
Protocols according to levels of
locality/coordination
Design Paradigms putting
together
Overview of Cellular Wireless
Network
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.
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Periodically performs channel scan to detect changes
in its environment.
Self-organizing technologies
Adaptive cell sizing
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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
Fixed relay node
Complex Behavior of Nodes
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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.
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Scale-free: connectivity distributions can be
represented by power-law form, which is
independent of the size or scale of the
network.
Random vs. Scale-free
Parameters of complex
network
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Average path length (L)
-average number of hops(edges) in the shortest path between
two nodes
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Clustering coefficient (C)
-average fraction of pairs of neighbors of a node that are also
neighbors of each other
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Degree (K)
-number of links connecting that node to the neighboring nodes
Small world concept
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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.
Scale-free model
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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.
Applying the model
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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.
Topology generating
Scale-free result
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Internet clustering coefficient is measured to be
greater then 0.18, and web is 0.1078.
Dynamic Routing
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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
Routing experiment
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The location of mobile users are generated
according to a uniform distribution.
BSs:3 , FRNs: 50, K=1
Discussion issues
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#1. Cell configuration
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#2. Efficient Planning
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#3. Coordination on different nodes and
layers
Cell configuration
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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
example: Cell-Dimensioning
Algorithm
example: Cell-Dimensioning
Algorithm
example: Cell-Dimensioning
Algorithm(II)
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Cell boundaries before and after BSR x removed
Efficient Planning
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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.
Example: Integrated Cellular
and Ad-hoc Relay System
Coordination on different nodes
and layers
SOPRANO
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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
Conclusion
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Self-organization reduces costs,
improves robustness, enhances
effectiveness and performance,
facilitates automatically utilization of
cellular wireless networks.
Its overall goal is to enhance QoS.
References
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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
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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
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Self-Management of Wireless Base Stations, Kai Zimmermann, Lars Eggert and Marcus Brunner,
www.ambient-networks.org
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On the Design of Self-Organized Cellular Wireless Networks, Sudhir Dixit, Evs, en Yanmaz, and
Ozan K. Tonguz, IEEE Communications Magazine • July 2005
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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
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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; 16:385–397
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), 5–25 (2003)