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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4

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

THANK YOU!

Optimal Distributed Data Collection for Asynchronous Cognitive Radio Networks

Zhipeng Cai, Shouling Ji, Jing (Selena) He, Anu G. BourgeoisGeorgia State University