throughput and delay scaling of cognitive radio networks...

UCLA ENGINEERING Computer Science

Throughput and Delay Scaling of Cognitive Radio Networks with Heterogeneous Mobile Users

Pengyuan Du*, Mario Gerla*, Xinbing Wang†

*Department of Computer Science, UCLA, USA

†Department of Electronic Engineering, Shanghai Jiao Tong University, China


Related Work

The research of ad-hoc net scaling law starts with the seminal

work of Gupta and Kumar in 2000 [1].

Consider a static network with users.

A multi-hop transmission scheme achieves per-node

throughput of , with theoretical upper

bound .

Mobility allows to trade-off throughput with delay [8]

[1] Gupta, Piyush, and Panganmala R. Kumar. "The capacity of wireless networks." Information

Theory, IEEE Transactions on 46.2 (2000): 388-404.

[8] Grossglauser M, Tse D N C. Mobility increases the capacity of ad hoc wireless networks[J].

Networking, Ieee/Acm Transactions On, 2002, 10(4): 477-486.

n

(1/ log )n n

(1/ )n


Related Work

Shortage of radio spectrum drives the research on Cognitive

Radio Network (CRN).

Primary (PU) and secondary users (SU) operate at the same

time, space and share the spectrum.

SU use spectrum opportunistically.

PU and SU can achieve the same throughput and delay

scaling laws as stand-alone wireless networks [3][4].

[3] Jeon S W, Devroye N, Vu M, et al. Cognitive networks achieve throughput scaling of a homogeneous network[J]. Information Theory, IEEE Transactions on, 2011, 57(8): 5103-5115.

[4] Yin C, Gao L, Cui S. Scaling laws for overlaid wireless networks: a cognitive radio network versus a primary network[J]. IEEE/ACM Transactions on Networking (TON), 2010, 18(4): 1317-1329.


Related Work Moreover, recent research showed that Mobility in CRN can

bring even further benefits [5-7].

The movement of secondary users will facilitate possible co-

operations between PU and SU.

Secondary users are willing to relay for primary users.

Throughput and delay scaling is improved to near-optimal for

both PU and SU.

[5] Gao L, Zhang R, Yin C, et al. Throughput and delay scaling in supportive two-tier

networks[J]. Selected Areas in Communications, IEEE Journal on, 2012, 30(2): 415-424.

[6] X. Wang, L. Fu, Y. Li, Z. Cao and X. Gan, “Mobility Reduces the Number of Secondary

Users in Cognitive Radio Network” in Proc. of IEEE GLOBECOM, 2011.

[7] Li Y, Wang X, Tian X, et al. Scaling laws for cognitive radio network with heterogeneous

mobile secondary users[C]//INFOCOM, 2012 Proceedings IEEE. IEEE, 2012: 46-54.


Motivation & Objective

The mobility models in previous works are not general and

representative (only SU are mobile while PU are static).

We propose a General Heterogeneous Speed-Restricted Mobility

(GHSM) model.

PU and SU possess heterogeneous mobility patterns.

Exploit the mobility of PU and SU to improve throughput and

delay performance.


Outline Introduction

Scaling Laws of Mobile Cognitive Network

Network Model

Routing and Scheduling Protocols

Throughput and Delay Performance

Conclusion


PU and SU co-exist in a unit torus

SU are denser than PU.

Source and destination pairs are randomly grouped.

The unit area is divided into primary and secondary cells for

connectivity [3].

Network Model

[3] Jeon S W, Devroye N, Vu M, et al. Cognitive networks achieve throughput scaling of a homogeneous network[J]. Information Theory, IEEE Transactions on, 2011, 57(8): 5103-

5115.


GHSM Model

At the beginning, all

users are uniformly

and randomly

distributed over the

network.

Then they would

move within a

circular area

according to a i.i.d.

mobility model.

Network Model


Multiple PU grids overlaid on the network A similar setting for SU nodes Movements restricted to cells


GHSM Model

To determine the cells in which PU and SU move, we define:

where denotes the i-th cell layer.

Users moving in layer have moving area

and radius , where is a random

variable.

Network Model

{ | 0 }iT i h

iT

0i

hiA n

iT

0

2= ( )

i

i hi

AR n

0


where denotes the i-th moving pattern. The moving area

becomes smaller and smaller as i increases.

covers the whole network, covers an area of .

0T hT

0

1

n

GHSM Model

To determine the moving area of PU and SU, we first define:

Network Model

{ | 0 }iT i h

iTMoving patterns of primary users are selected as follows: Moving patterns of secondary users are selected as follows:


GHSM Model

For the primary network, we assign nodes to each moving

pattern (ie each cell layer)

For the secondary network, we assign nodes to each

moving pattern.

Network Model

n

n


Network Model

Comparison

Previous Mobility

Models

GHSM Model in this

work

Number of PU

Number of SU

Moving Pattern of PU Static Mobile

Moving Pattern of SU Mobile Mobile

( 1)h n

n

( )p

( )s

( 1)pN h n

( 1)sM h n


Primary Routing Scheme

Primary routing scheme utilizes the heterogeneous mobility of both

PU and SU to propagate packets to the destination.

Packets are delivered hierarchically to the destination.

Network Protocols

Delivered!


Network Protocols

Secondary Routing Scheme

Secondary routing scheme only utilizes the mobility of SU to

propagate packets to the destination.


Network Protocols

Different from Gupta and Kumar’s model, for CRN to work,

the following scheme is introduced from work [2]:

64-TDMA scheduling scheme

Preservation region

On this basis:

PU are scheduled to relay packets from every primary moving

pattern;

SU are scheduled to relay packets from every primary and

secondary pattern.

[2] Yin C, Gao L, Cui S. Scaling laws for overlaid wireless networks: a cognitive radio network versus a primary network[J]. IEEE/ACM Transactions on Networking (TON), 2010, 18(4): 1317-1329.


Performance Analysis

Impact of Different Moving Pattern

Achieve near-optimal throughput regardless of different

moving patterns.

Packet delay will decrease when moving pattern increases.

with , which indicates a

similar result as in [8], i.e., where .

Heterogeneous mobility introduces transmission diversity

into the network.

3

0 ( ) ( log )D N n n 0 3

1( ) ( )

logN

n

[8] Grossglauser M, Tse D N C. Mobility increases the capacity of ad hoc wireless

networks[J]. Networking, Ieee/Acm Transactions On, 2002, 10(4): 477-486.

( ) ( )D n n n ( ) 1n


Li’s scheme Our scheme

Throughput

of PU

Delay

of PU

Performance Analysis

Optimal Performance

Comparison with Li’s scheme in [7]:

0 3

0

4

0

3log log( log ), 0

log( )

3log log(log ),

log

optimal

nn n

nD n

nn

n

3

1( ) ( )

logN

n

01 3

0

4

0

log log( log ), 0 1

log( )

log log(log ), 1

log

optimal

nn n

nD N

nn

n

1( ) ( )

logn

n

[7] Li Y, Wang X, Tian X, et al. Scaling laws for cognitive radio network with heterogeneous

mobile secondary users[C]//INFOCOM, 2012 Proceedings IEEE. IEEE, 2012: 46-54.

For SU, we have similar results as [7]


Outline Introduction

Scaling Laws of Mobile Cognitive Network

Conclusion


Propose a GHSM model

Cooperative routing strategy

Better optimal performance

We also prove that the secondary network can still achieve the

same performance as in previous works even though secondary

network may suffer from the moving ability gaps..

Conclusion

This paper studies the throughput and delay scaling laws of a

cognitive radio network with the General Heterogeneous Speed-

restricted Model.


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