optimal distributed data collection for asynchronous cognitive radio networks
DESCRIPTION
Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks. Zhipeng Cai , Shouling Ji , Jing (Selena) He, Anu G. Bourgeois Georgia State University. OUTLINE. 1. Introduction. System Model. 2. Distributed Data Collection. 3. 4. Simulation and Analysis. 5. - PowerPoint PPT PresentationTRANSCRIPT
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Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks
Zhipeng Cai, Shouling Ji, Jing (Selena) He, Anu G. BourgeoisGeorgia State University
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OUTLINE
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Introduction1
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System Model
Distributed Data Collection
Simulation and Analysis
5 Conclusion
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Introduction
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Cognitive Radio Networks (CRNs) The utilization of spectrum assigned
to licensed users varies from 15% to 85% temporally and geographically (FCC report)
Unlicensed users (Secondary Users, SUs) can sense and learn the communication environment, and opportunistically access the spectrum without causing any unacceptable interference to licensed users (Primary Users, PUs)
Introduction
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Why Distributed Algorithms? CRNs tend to be large-scale distributed systems
CRNs are dynamic Systems
Spectrum opportunities are dynamic with respect to time and space
Challenges How to guarantee secondary network activities do not hurt primary
network activities?
How to make decision based on only local information?
How to overcome problems induced by lack of time synchronization?
How to theoretically analyze the performance of distributed algorithms?
Introduction
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Contributions Derive a Proper Carrier-sensing Range (PCR) under the physical
interference model for Secondary Users (SUs)
Propose an order-optimal Asynchronous Distributed Data Collection (ADDC) algorithm
Simulations are conducted to validate ADDC
Introduction
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System Model
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Primary Network N independent and identically distributed (i.i.d.) PUs
Locally finite property
Working power
Network time is slotted with slot length
During each time slot, each PU transmits data with probability
System Model
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Secondary Network n SUs and one base station
Maximum transmission radius of SUs is r
The secondary network can be represented by graph
Conditions on communication between two SUs
System Model
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Data Collection At a particular time slot t, every SU produces a data packet of size B
The set of all the n data packets produced by SUs at time t is called a snapshot
The task of gathering all the n data packets of a snapshot to the base station without any data aggregation is called a data collection task
The data collection delay is the time consumption to finish a data collection task
The data collection capacity is the average data receiving rate at the base station during a data collection process
System Model
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Interference Model Physical interference model
For PUs
For SUs
System Model
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Distributed Data Collection
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Data Collection Tree Proper Carrier-sensing Range (PCR) Data Collection Algorithm Performance Analysis
Distributed Data Collection
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Connected Dominating Set (CDS) based Data Collection Tree
Data Collection Tree
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Objectives The secondary network does not cause unacceptable interference to the
activities of the primary network
All the SUs transmitting data simultaneously are interference-free
The carrier-sensing range is as small as possible, which implies SUs can obtain more spectrum opportunities
Proper Carrier-sensing Range
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Concurrent Set: a set of active nodes s.t. all the nodes in this set can conduct data transmission simultaneously.
:
Proper Carrier-sensing Range (PCR): the carrier-sensing range R is a PCR if for any R-set, it is a concurrent set.
Proper Carrier-sensing Range
si
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How to decide the proper carrier-sensing range (PCR)?
In a R-Set, to guarantee SUs will not cause unacceptable interference to PUs, it is sufficient to have
(Lemma 2)
In a R-Set, to guarantee SUs can transmit data simultaneously and interference-freely, it is sufficient to have
(Lemma 3)
We can set the PCR , where
Proper Carrier-sensing Range
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Proper Carrier-sensing Range
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Asynchronous Distributed Data Collection (ADDC) algorithm
Data Collection Algorithm
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The number of dominators and connectors within the PCR of an SU is upper bounded by , where is a function on x with
(Lemma 5)
The number of SUs within the PCR of an SU is upper bounded by
, and with probability 1.(Lemma 6)
The expected time for an SU to obtain a spectrum opportunity is
where . (Lemma 7)
Any SU having data for transmission can transmit at least one data packet to its parent within time .
(Theorem 1)
Performance Analysis
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The delay induced delay by the proposed Asynchronous Distributed Data Collection (ADDA) algorithm is upper bounded by
This implies the achievable data collection capacity of ADDC is
which is order-optimal. (Theorem 2)
Performance Analysis
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Simulation
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Network setting An i.i.d. primary network
An i.i.d secondary network
Please refer to the paper for detailed settings
Compared algorithm Coolest (ICDCS 2011): the path with the most balanced and/or the
lowest spectrum utilization by PUs is preferred for data transmission
Simulation
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Data Collection Delay vs. Network Size (n and N)
Simulation
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Data Collection Delay vs. and
Simulation
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Data Collection Delay vs. Transmission Power
Simulation
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We study the distributed data collection problem in CRNs
We propose an Asynchronous Distributed Data Collection (ADDC) algorithm for CRNs, which is order-optimal
Simulations are conducted to validate the performance of ADDC
Conclusion
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THANK YOU!
Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks
Zhipeng Cai, Shouling Ji, Jing (Selena) He, Anu G. BourgeoisGeorgia State University