ICUMT2009 Rama Paper

8/6/2019 ICUMT2009 Rama Paper

1/7

Hybrid Approach to

Cognitive Radio Test BedRamachandra Budihal#, H S Jamadagni*

#ANRC, Wipro Technologies, INDIA

[email protected]

*CEDT, Indian Institute of Science, [email protected]

Abstract Cognitive Radios are the advanced wireless systems that

are used to intelligently access the scarce radio resource the

spectrum. Cognitive radios have ability to sense the ambience,

spectrum usage, geo conditions and fuse these information to

arrive at a spectrum decision which helps to access the network in

a smart, controlled manner. However, the incumbent licensed

systems are not sure on the robustness and accuracy of the sensing

systems of these Cognitive radios and have serious concern on the

interference caused by false detection of spectrum holes. It is very

much important to practically verify the advanced algorithms,

theoretical frameworks for Cognitive radios both in terms of

sensing and switching so as to ensure that the incumbent systems

are fully convinced about its co-existence. Generally, performance

evaluation of these algorithms is done using simulators, test beds

and expensive protocol testers. While each of these has their own

merits and demerits, we propose an architecture that combines the

best of both simulators/emulators and real-time test beds for

scaling up the experiments while keeping testing as realistic as

possible with the harsh wireless environments. We have also

included a sample of early results in a preliminary implementation

of this test bed

KeywordsRealtime, Cognitive radio, performance, scalability,emulation, simulation, spectrum sensing, spectrum decision,

control channel, co-operative communication, Cognitive mesh

networks, multi-hop, clusters, co-simulation

I. INTRODUCTION

Currently, the wireless systems are characterized by static

allocation of spectrum through the means of auctions, where the

radios that use them have fixed and limited functions without

much required network co-ordination. While there has been

drastic raise in the usage of spectrum in unlicensed bands, these

systems though designed to operate in these bands have had

achieved higher efficiency, are losing out due to increased

interference which is causing a major hurdle in increasing the

network capacity and scalability. Cognitive radio is a new

innovative paradigm to solve the problem through efficient

spectrum utilization. Cognitive radios sense the spectrum before its usage and detect the holes that are then smartly

utilized without causing harmful interference to the primary

incumbent system which has license to use the spectrum. The

expected behavior of the cognitive radios is not completely

characterized in the real-time environments, thereby causing a

serious concern for the regulators and primary licensed users.

There has been an ample amount of solutions/algorithms to

prove the point; however, the concern cannot be really solved in

a theoretical framework. Their concerns can be addressed only

through practical demonstrations, measurements in the real life

scenarios that use these systems and to prove to the point that

usage of these systems is not causing any harmful interference

for primary users and also for cognitive users the appearance of

primary users doesnt deteriorate its performance or assured

QoS of the overall network. Performance evaluations of the

network protocols and algorithms/applications that are used to

build the smart radio are typically done through the network

simulators, small test beds or some more expensive network

emulators/protocol testers. Network simulators are very well

suited for simulating the large scale networks and some real-

time emulations are also possible; however, there are few

important drawbacks in this, firstly, the abstractions that are

made as a part of the network stacks to make the simulation

feasible hide important issues that can be visualized only when

it is implemented and deployed in large scale. Secondly, the

real-time emulation features that are provided by simulators

such as NS2[1][2], Qualnet [3], OPNET[4], GloMoSim[5] etc.,

are mostly in the network and upper layers[6], and study made

by [7][31][32] shows that even the simple protocol simulations

using different simulators may give diverging results. Discrete

event simulators are very well suited for deploying of large

scale networks in simulated environments, drawing new

scenarios rapidly, but they certainly will lack the accuracy of areal-time emulation using the real stacks running on physical

and/or virtualized environment and/along with the COTS

(commercially off-the-shelf) based real-time hardware in loop.

Our approach is a two pronged co-simulation/emulation

approach where we propose to use the real stack for accurate

and more realistic, reliable and repeatable emulations, with a

flexibility to use the discrete event based simulators either

running on generic Linux (user mode) in virtualized

environment or on physical machines and the hardware in loop

COTS based Software radios such as GNU/USRP radios and

in cases where no real radio is not attached it will be replaced by

a PHY layer radio abstraction layer. Our approach not only gets

the best of both worlds but also is helpful for in reducing thedevelopment time and costs involved in transitioning the system

into a future product line with little rework/redesign.The main difference between our approach and the existing

simulation environment are firstly, that we have kept our

priority in using the real-time framework using RT (real-time)

extension for Linux [8], we use a fully pre-emptable HRT (hard

real-time) for scheduling processes that require cycle level

precision especially with the hardware in loop control in a

virtualized/Real environment that has to abide by timing for


2/7

real-time framing constraints while giving the flexibility to

scaleup large network through the distributed simulation. In

some of the virtual machines we run the simulators models such

as Qualnet/ NS2. Secondly, we use the real MAC and in places

where hardware in loop cannot be afforded we plan to use

accurately modelled PHY layer devices using SystemC [9], etc.,

This paper is organized as follows: section II provides

overview of the architecture and network elements, section III

gives some descriptions on the virtualized system and hardwarein loop implemention and section IV on the applications and

spectrum sensing operations experiments executed in this

environment, and finally we present our conclusion and future

work in section V

II.ARCHITECTURALELEMENTS

Overall architecture of the Testbed is as explained in detail in

section III and is shown in fig 2, which consists of emulated,

simulated and real physical networks controlled by a control

panel. The emulator and simulated systems are running on

VMs(virtual machines) and/or on the real physical RT enabled

Linux platforms. We have chosen a mesh network topology for

experimental purpose; Fig 1 (mesh architecture) shows thetypical setup of the network generic co-operative mesh

broadband network architecture. The network comprises of

clusters and each cluster has a Cluster Head called Cluster

Spectrum Fusion Centre (CSFC), which takes care of the local

spectrum decision and send the same to the Global Spectrum

Decision System (GDSS) in a single hop if it is in the vicinity

or use neighbourhood CSFCs to Slingshot the local spectrum

decision [10]. The CSFC in a sort acts as inter-cluster level

mesh router passing on information from neighbourhood

clusters to next level. Currently, we are using an ideal command

and control channel and plan to make these channel also a

cognitive with multiple redundant channels or use some sort of

dynamic cognitive sync channel.

A.Architectural Network Elements

1) Cluster Spectrum Fusion Centre (CSFC): This is adynamically designated [12],[13] cluster head and its main role

is to manage the radio resources in their cluster like a typical

base station in a cellular network. The cluster is a set of

Cognitive Sensing Nodes (CSN) that is characterized by one-

hop connectivity with the CSFC and use a Cluster Command

and Control Channel (C4) which is not designated with

redundant channels like the GDSS. Between two CSFCs they

use GDSS. Since the temporal resources differ from one cluster

to another the GDSS and C4 cannot have the same frequency

carriers. C4 comprises of two channels one for uplink (CSN CSFC) another for downlink (CSFC CSN). The uplink is

used to relay the bandwidth requirements, spectrum sense

measurements, channel quality measurements confidence level

etc., from CSN to CSFC. CSFC fuse these parameterized data

sent from various CSNs to generate a cluster level spectrum

decision (CLSD) and pass this on through the GDSS to the next

level or the neighbourhood to arrive at a negotiated global

spectrum decision. The global spectrum decision is a network

level agreed decision that is then used by each of the CSFCs to

schedule transmission of labels (time, frequency and geo

mappings of the radio resource) and intimate to the Cognitive

Radio transceiver nodes (CRN). This negotiation process

description is beyond the scope of this paper.

2) Cognitive Sensing Node (CSN): This is a simplespectrum sensing node which uses one or many of the various

algorithms [13][14][15][16] used for spectrum sensing. The

algorithms that run on each of these CSNs are the algorithmsthat are being evaluated or tested for functionality and

performance on the test bed while we scale up the network and

its elements. The CSNs can be powered by batteries or it can

use a Energy harvesting mechanism to get self power [17].

3) Cognitive Radio Transceiver Node (CRN): This theactual CR transmitting and receiving node, which also has a

CSN functionality builtin in most of the cases, however, for

simplicity, we can make this as just a parasitic Spectrum agile

radio, which depends on other CSNs to sense the spectrum and

use the network based on the agreed QoS and spectrum decision

passed on to it by its respective CSFC. The functionality and

internal architecture of the CRN has be derived from many

interesting papers [18][19][20].

Fig 1 Organization of the network elements

B.Hardware Architecture

Though the system that we propose is hardware agnostic, at

present the hardware that are used for radio is COTS platform

USRP Universal Software Radio Platform 2.0 which isavailable from Ettus Research, there are 5 of them. The RF

boards are addons to the main FPGA board and it comes in

range of frequencies. We are at present using 2.4 GHz ISM

band and plan to use 5 GHz and other unlicensed spectrum in

future. More details can be seen in [21]. These boards are

interfaced to the PCs (Dual Xeon Quad Core with 16GB DDR2

RAM, 1 TB HDD) via USB 2.0 interface. All PCs use Ethernet

for inter-PC communication, which serve as an ideal channel


3/7

for communication between the nodes for testing purpose. The

USRP provides an interface between four high speed analog to

digital converters, four high speed digital to analog converters

and a USB 2.0 high speed interface. Daughter boards available

for the USRP provide an interface from the baseband signals

present at the data converters to several useful frequency bands.

The USRP uses two Analog Devices AD9862 Mixed-Signal

Front End (MxFE) processors for analog to digital conversion

(and the reverse). In addition they provide gain control in theanalog path and some signal processing in the digital path. An

Altera Cyclone series FPGA does signal processing for the

transmit and receive paths. The receive path has a mixer and a

decimation unit and the transmit path contains an interpolater,

implemented in the FPGA. Finally a Cypress FX2 interfaces

between the FPGA and a USB 2.0 port. The USRP connects to

a USB port on the host computer where modulation and

demodulation is performed.

C. Software Architectures and options

There are many options for the software interface to the

USRP:

1) OSSIE (Open Source SCA Implementation::Embeddedframework): An SCA compatible component that providescontrol and data ports to the USRP. There are several key

sections of the software; the radio control interface, main

program, device object and port implementations[22].

2) GNU Radio toolkit: GNU Radio is a free software

development toolkit that provides the signal processing runtime

and processing blocks to implement software radios using

readily-available, low-cost external RF hardware and

commodity processors. [23].

3) Tools4SDR: This is a toolbox to interface USRP with

windows and matlab [24].

4) Virtualization of nodes: Virtual machines execute within

special boundaries called Partition in single physical machinewhich have different level of access to various hardware

components. Partitions can be provided with virtualized

hardware, share a piece of physical hardware among

themselves, or be the sole owner of a particular hardware

component. A special purpose virtual machine provides

management functionality and is sufficiently privileged to

create and manipulate other virtual machines called the VMM

(virtual machine manager) or Hypervisor [25].Currently, we are using the GNU radio toolkit but plan to

evaluate all in near future. The PHY is implemented in C++

with the help of GNU Radio toolset and in combination of

USRP hardware to give the necessary air interface to the test

bed. The MAC is full real time implementation. Forvirtualization we are using Xen. The provisioning and de-

provisioning of the VMs and the management is taken care by

the designated Xen Hypervisor and our own VM Manager.

III.THESYSTEMARCHITECTUREAND

PRELIMINARYIMPLEMENTATION

The system is organized as shown in the fig 2. Currently, the

systems can be configured as realtime PC based CSNs

comprising of USRP based Radios and RT extension enabled

Linux and in usermode Linux running in Virtualized

environment initiated using Xen [26]. Xen uses a virtualization

architecture called Para-Virtualization, in which the guest OS

are aware that is, designed to take advantage of the fact

that they are running in a virtualized environment. With this the

OS is modified to make hypercalls to the hypervisor for

privileged operation, instead of regular system calls in a

traditional OS. The virtual machines hosts the real applicationssuch as a traffic generator a streaming application or a

network simulator instance such as a model of Qualnet/NS2

running to simulate a scenario of a large scale deployment.

CSNs in the network communicate via direct-memory transfer

when they are part of the same physical machine and via

multicast IP over Ethernet when they are in different machines.

The communication between CSNs and CSFC allows for the

exchange of transport data at the PHY and MAC interface, the

so-called C4 channels. CSNs running in the virtualized

environment filter MAC-layer PDUs on reception based on

radio measurements which are locally simulated, in the sense

that channels that are not destined for a particular receiving

node are dropped. The presence of a particular channel is

potentially used, however, in the calculation of interference in

the PHY abstraction entity discussed in detail in [27][9].

``

`

CRN Simulation on VMs

1. Network Models

2. Simulated stacks3. Traffic models4. Channel models5. Mix and match withother networks andapplications

CRN Emulation (physical)

Control Panel (physical)

1.Real application2.Real Transport, Networkand MAC3.SystemC PHY model4.RT linux extension

1.VM Control and

configuration UI2.Status monitor

3.Complete N/W scenariocreation and visualization

4.Timing, sync and schedulingcontrol

Highspeed Ethernet backbone

Physical N/W and Hardware in loop

Fig 2 High-level architecture of the testbed platform

The real-time version of the emulator is designed to represent

the behavior of the wireless access technology in a real network

setting while obeying the temporal frame parameters of the air-

interface. It makes use of the open-source real-time operating

system extension to Linux, RTAI [8] to guarantee hard real-time

behavior. With virtualization of the protocol stack, many

instances (approximately about 25 of them Dual Quad-core

Xeon) can reside in the same physical machine. A typical setup

for a large-scale emulation would consist of several PCs in a

cluster network each housing tens of virtual nodes. In

ANRC(Aerospace Network Research Consortium) Lab we are

deploying 15 Dual-Quad-core Xeon servers for connecting the

wired/wireless and other traffic generator applications. The

layer 3 networking protocols reside in the standard linux kernel


4/7


5/7

machine. The traces and logs are collected through text files for

final analysis. The overall operation flow of the traffic on the

hardware-in-loop is as shown in fig 3c. This Spectrum

snapshots are continuously taken for about 2 hours at regular

intervals in the 2.4GHz ISM band that is captured with a USRP

at 16M I/Q samples per second (approx.1.8 M samples within

100ms) and then up-sampled to a bandwidth of 20MHz to match

the channelization of IEEE 802.11, i.e., the bandwidth

occupancy of an 802.11g user. A FFT based ED (energydetector) is used to detect the start and duration of the PU

(primary user or interferer) channel access. The laptop user

performing an FTP bulk download at 54MBit/s on the observed

frequency channel is set-up to provide a representative main PU

(this is just a representative test case, even short video clips with

duration of 3-5 secs were played through wireless for simulating

steady and bursty traffic). There are other undetermined

bluetooth and wifi access users in the vicinity but their powers

are randomly varying as seen in the snapshot. Our cognitive

transmitter PC1 and USRP Tx acts as a CSN also and does the

operation of LBT (listen before talking) ie., senses the spectrum

in the band of 5 MHz starting from 2.4GHz and transmits the

data if spectrum is vacent it streams the video in the designated

ISM channel and will change the frequency dynamically with a

latency of approximately 20ms after the spectrum decision is

made. PC2

Select nth

channel,

acquire

samples

Windowing

and FFT of

samples

PSD Pow[ i] >

Streaming

Video

traffic

Send packets

through USRP

Tx for t secs by

OFDM[i]

Sync the

USRP Rx

through an

ideal channel

Play

received

Video

Sync to

channel and

receive

packets

n=n+1 (switch to

next channel)

Dynamic threshold

Spectrum

occupied

Spectrum

Vacant

Fig 3c Cognitive Transmission operation flow

and USRP Rx is configured as a simple spectrum sensing node

(similar to the CSN) and receiver. The spectrum decision holes

are then transmitted through an ideal control channel ethernet

to PC2 which will correlate its decision with that of the

transmitters each peak in correlation indicates a hit or else it

will be categorized as a miss or false detection. Although the

observed frequency band is heavily used by this set-up, it is

easily recognized from fig 3d that a large number of rather short

temporal opportunities are still available.

Fig 3d Experiment setup on Testbed

The distribution shown in fig 3d is drawn from a sample

snapshot and is characteristic for the traffic generated by best-

effort IP-based wireless communications consisting of short

datagrams for signalling and random low-profile traffic as wellas large packets for bulk data transfers (x axis represents the

frequency ranging from 2380 MHz to 2520 MHz, y axis

represents sensed signal power in dB, z axis represents time in

secs) . From the preliminary observations we have found that

there are combinations of short and long gaps in the

distribution of the temporal opportunities while short gaps being

about 10 s and long ones being 80-100 s. there are also very

long gaps which give substantial opportunity for spectrum usage

in order of few seconds too. The detail spectrum profiling is a

work in progress and this experiment is just to demonstrate the

simple spectrum sensing and decision process, we have shown

results of the spectrum occupancy for about 15 thousand

consecutive readings of the sensing algorithms and initialobservations are made based on this. It is evident from this that

our initial observation indicate that depending upon the type of

user traffic, the temporal opportunities are quite often

deterministic to an extent and are stationary in short term, which

can be easily predicted if we can observe, understand and learn

the environment initially. So we devised a simple decision

making process that is rather based on the short term

observation mapped over the aprior long term spectrum plan

which was assumed in our experiment case. The long term

spectrum plan is driven by the spectrum policy at a higher level

and the further finetuning of the same is based on the contextual

data that is based on the continuous spectrum sensing and

subsequent spectrum decision.Traces of simulations is collected along with the data of real-

time hardware-in-loop such and analysed according to the

schedules that were planned such as SNR variation with the

change of power at the receiver end; channel hopping after

sensing a channel that is occupied by the interferer; the traces

showed clearly the match in most of the times at the transmitter

and receiver; sync between the transmitter and receiver at-times

had variation and some misses at receiver whenever the

interferer was moved away from it. Overall we exercised the


6/7

functionality of the VMM to provision, deprovision VMs and

configure, schedule the applications on VMs running on PC3

and PC1 and PC2, collect traces and data logs from simulations

and emulations for analysis simultaneously. During analysis we

observed that with the lower data rate (low bit rate video) the

simulation and emulation results had correlation and with

increase in data rate and nodes in network- scaled up network,

the results were diverging, by using our architecture we can

easily create a co-operative simulation by using different tools(qualnet and NS2) along with emulation helped to better the

accuracy of the results as the network scales up especially the

radio network.

V CONCLUSION AND FUTURE WORK

We have presented early results about the ANRC Cognitive

Radio testbed. We have been successfully able to demonstate

the initial proof of concept of incorporting the co-

simulation/emulation methodology for scaling up of the network

environments to real life scenarios which will help not only to

model but also measure the network behavior more reliably and

accurately. This platform helps in collecting the simulation datathrough the VMM monitored Qualnet simulations and also the

realtime-real-hardware-in-loop results for a given scenario. This

data can be further analysed to improve the accuracy of large

scale simulations by fusing the real-time hardware-in-loop data

into the specific simulation readings during the analysis.

In longer term we are developing protocols, algorithms that

incorporate a variety of channel feedback schemes and we are

looking at using OFDMA to allow the MAC to schedule

concurrent transmissions of potentially interfering nodes on

orthogonal subcarriers. We feel our testbed is Flexible, as it

provides user several options to provision and deprovision the

VMs (computing, network and storage) at an ease through a

single GUI based control, Scalable, as there is virtually no limit

in deploying the VMs, applications on that and network

elements/simulations, Reliable as it runs on more deterministicreal stacks on real hardware and Repeatable as the behavior of

systems and its dynamics do not change or vary drastically. It

will help to devise, model rather big scenarios and network

experiments by mixing and matching different adaptions of

radio PHY, MAC and network layers. We are going to scale

up by incorporating complex modes in QualNet and NS2 which

inturn can give a average realtime performance because of the

high end machines we use; these results or models can be

plugged onto the virtual machines which are connected through

IPNE in a distributed environment to the hard-real-time linux

VMs connected to the real hardware RF frontends such as

USRPs, wireless access devices. With our successfulimplementation of an experimental testbed it will go a long way

in synthesizing the designs and accurately measure the network

behavior and performance in real-life scanarios such as in a

world-wide aerospace networks etc.,

ACKNOWLEDGEMENTS

I thank our principal sponsors Wipro Technologies, Boeing,

HCL technologies and IISc who have set up the Aerospace

Network Research Consortium (www.anrc.in) to conduc

cutting edge research in wireless networks and systems that will

leapfrog the next generation Aerospace network into a new

paradigm with a vision of looking an airplane as node in the

sky. I thank my advisor Prof. H S Jamadagni for his valuable

guidance and advice, my research colleagues Sheshanandan,

Rajdeep, Shruthi, who helped in setting up of the USRP

platform and Kamal Mistry, Devesh for setting up of the Virtual

Machine platforms on with RT extensions. I must also thank TV Prabhakar for his constant push in conducting the

experiments and motivating me to publish the paper.

REFERENCES

[1] D. Mahrenholz, S. Ivanov, Real-Time Network

Emulation with ns-2, Proceedings of The 8-th IEEE

International Symposium on Distributed Simulation and Real

Time Applications, Budapest Hungary, October 21-23, 2004.

[2]ns-2. http://www.isi.edu/nsnam/ns/.

[3] QualNet User Manual Scalable Network Technologies,Culver City, CA. http://www.qualnet.com.

[4]OPNET. http://www.opnet.com/.

[5]GloMoSim. http://pcl.cs.ucla.edu/projects/glomosim/.[6] Thomas R. Henderson, Sumit Roy, Sally Floyd and GeorgeF. Riley, ns3-Project Goals, Workshop on ns-2: the IP network

simulator, Pisa, Italy October 10, 2006.

[7]D. Cavin, Y. Sasson, and A. Schiper, On the accuracy ofMANET simulators, Second ACM international Workshop on

Principles of Mobile Computing, Toulouse, France, October 30

- 31, 2002.

[8]Real-Time Application Interface, www.rtai.org.[9] Thorsten Grotker, Stan Liao et.al System Design withSystem C Kluwer Academic publishers 2002

[10] V. Sharma and A. Jayaprakasam, "An Efficient Algorithmfor Cooperative Spectrum Sensing in Cognitive Radio

Networks", National Conference on Communications, 2009[11] Hirohito Taniguchi, et.al, Clustering algorithms in ad hocnetworks Electronics and Communications in Japan (Part II:

Electronics) vol 88 pg 51-59 , 2005

[12] Dario Bottazzi et.al, Context-Aware GroupCommunication in Mobile Ad-Hoc Networks WWIC 2004,

LNCS 2957, pp. 3847, 2004.

[13] Spectrum sensing: A distributed approach for cognitiveterminals M Gandetto, C Regazzoni - IEEE Journal on Selected

Areas in Communications, 2007

[14]Experimental study of spectrum sensing based on energydetection and network cooperation

D Cabric, A Tkachenko, RW Brodersen , Proceedings of the

first international workshop on Technology and policy foraccessing spectrum, 2006

[15] Cooperative spectrum sensing in cognitive radionetworks G Ganesan, Y Li - New Frontiers in Dynamic

Spectrum Access Networks, 2005[16] Implementation issues in spectrum sensing for cognitiveradios - D Cabric, SM Mishra, RW Brodersen 38th AsilomarConference on Signals, Systems and Computers, 2004

[17] Optimal energy management policies for energyharvesting sensor nodes, Vinod Sharma, Utpal Mukherji, Vinay
http://www.isi.edu/nsnam/ns/http://www.qualnet.com/http://pcl.cs.ucla.edu/projects/glomosim/http://www.rtai.org/http://www.rtai.org/http://pcl.cs.ucla.edu/projects/glomosim/http://www.qualnet.com/http://www.isi.edu/nsnam/ns/


7/7

Joseph, and S. Gupta, Submitted to IEEE Transactions on

Wireless Communications, 2009.

[18] A real time cognitive radio testbed for physical andnetwork level experiments, SM Mishra, D Cabric, C Chang, D

Willkomm, et.al - First IEEE Intl Symposium on Dynamic

Spectrum Access, 2005

[19] Cognitive radio testbed: further details and testing of adistributed genetic algorithm based CJ Rieser, TW Rondeau,

CW Bostian, TM Gallagher - IEEE Military CommunicationsConference, 2004. MILCOM 2004

[20] Cognitive Radio Experiments using ReconfigurableBEE2, A Tkachenko, D Cabric, RW Brodersen - Asilomar

Conference on Signals, Systems, and Computers, 2006

[21] www.ettus.com/usrp[22]ossie.mprg.org[23]E. Blossom, GNU Software Radio. [Online]. Available:http://www.gnu.org/software/gnuradio/

[24] www.tools4sdr.com[25] www.xen.org[26] http://www.xen.org/about/paravirtualization.html[27]OpenAirInterface, www.openairinterface.org.[28]VLC Media Player. http://www.videolan.org/vlc/.[29] http://www.qstream.org[30]Resources tab in http://www.anrc.in[31] S. Kurkowski, T. Camp, and M. Colagrosso, MANETsimulation studies: the incredibles, SIGMOBILE Mob.

Comput. Commun. Rev.,vol. 9, no. 4, pp. 5061, October 2005.

[32] G. Judd and P. Steenkiste, Using emulation tounderstand and improve wireless networks and applications,

in Proceedings of NSDI, Boston, MA, 2005.
http://www.ettus.com/usrphttp://www.tools4sdr.com/http://www.xen.org/http://www.xen.org/about/paravirtualization.htmlhttp://www.openairinterface.org/http://www.videolan.org/vlc/http://www.qstream.org/http://www.qstream.org/http://www.videolan.org/vlc/http://www.openairinterface.org/http://www.xen.org/about/paravirtualization.htmlhttp://www.xen.org/http://www.tools4sdr.com/http://www.ettus.com/usrp

ICUMT2009 Rama Paper

Documents

Transcript of ICUMT2009 Rama Paper