Capacity Estimation and Adaptation in Cognitive RadioNetworks: Demonstrating Software Defined Radios in

Action

Vinay Kolar, Antonio Gerco, Thomas Milcher, Marina Petrova, Petri MähönenDepartment of Wireless Networks

RWTH Aachen University, Aachen Germany{vko, agr, mil, mpe, pma }@mobnets.rwth-aachen.de

ABSTRACTIn this demonstration we suggest to exhibit some key compo-nents for the future cognitive and adaptive radio networksthat are enabled by low-cost software defined radios. Inmulti-hop cognitive radio networks estimation of availablecapacity is an important feature to go beyond simple sensingbased Dynamic Spectrum Allocation. We demonstrate ourcapacity estimation engine, which is a part of the larger cog-nitive resource manager framework. The engine estimatesavailable capacity of di!erent routes in a dynamic cognitiveradio network, which in our demonstration is build by us-ing USRP and WARP software defined radios. The demon-stration goes beyond the capacity estimation by showingthe various types of MAC interferences and e!ects of those.The demonstration is fully dynamic showing our capabilityto chose di!erent paths (or frequencies) between source anddestination based on real-time capacity estimation of avail-able radio links. Our tool demonstrates how the developedcapacity estimation engine works, and its applicability forhigher level applications such as routing, network provision-ing and dynamic channel allocation. The demonstration hasa graphical user-interface and we use video streaming as atest application to exhibit how the change of paths a!ectsto achieved capacity and video quality.

1. INTRODUCTIONNearly exponential growth of the number of wireless sys-

tems is pushing the wireless networking close to its capabil-ities. The last two decades have seen emergence of increas-ingly sophisticated physical layer techniques, which havenearly reached Shannon limit. More recently, the advancesin microelectronics have finally made it possible to developtrue Software Define Radios (SDR) that increase tremen-dously flexibility of execution platforms. Finally, cognitiveradios have been suggested as a solution to enable both Dy-namic Spectrum Access (DSA) and flexible cross-layer opti-mization of protocol stacks from radio hardware up to appli-

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cation layer. All this is directing us towards cognitive radionetworks, which would enable e"cient utilization of radiochannels.

A number of problems solved for building e"cient cog-nitive radio networks. A lot of work has been done espe-cially to enable e"cient DSA, but so far less focus has beenpaid for networking aspects. We are developing a generalCognitive Resource Manager (CRM) framework for overalloptimization and control of cognitive radios[4]. This demon-stration will exhibit with more details specific componentsand focus on a specific example scenario of cognitive radionetwork. The basic justification for the demonstration anddeveloped technology is the insight that many higher layerapplications should be able to chose the automatically max-imum capacity radio links. This is important especially inmulti-hop networks. However, simply using a link with max-imum bandwidth or most aggressive modulation technique isnot necessarily the most e"cient approach. Similarly mak-ing dynamic spectrum allocation based on simple informa-tion of vacant spectrum or low SNR (Signal-to-Noise-Ratio)therein, is not guaranteed to lead to optimal decisions. Forlong-living flows it is more e"cient to include into the infras-tructure itself a capability to evaluate and estimate availablecapacity of end-to-end route1.

First, we will show how a developed capacity estimationengine determines the available capacity of the routes in adynamic cognitive radio network using USRP (”GNU Ra-dios”) and WARP based software defined radios[8, 7]. Sec-ond, we demonstrate di!erent types of MAC interferencesand how those a!ect to available capacity. We present ourSDR-based interference monitoring module, and finally tiein all these aspects together to comprehensive demonstra-tion where we use scheduling aware routing algortihm tochoose between the best route over all the possible by tak-ing concurrently in account interference, MAC-interactionsand estimated capacity. In order to achieve this the sys-tem employs a two-phase measurement methodology, wherein Phase 1 it computes a coarse-gained interference met-rics through spectrum sensing. This interference metrics isshown interactively through our GUI. In Phase 2 the systemfinds out specific interference patters in a give spectrum.

1Note that a large part of the existing literature use theterm bandwidth to denote capacity in the sense of bit persecond. This is sometimes confusing in the wireless domain,where bandwidth also meands the bandwidth of communi-cation channel measured in Hz. Thus we have chosen torefer data transfer capability as capacity (bit/s) and havereserved term bandwidth for its original wireless meaning.

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Figure 1: The basic blocks of interaction detectionand capacity estimation engine (adopted from [2].

These modules are combined with popular bandwidth (ca-pacity) estimation methodologies such as WBest.

The general functionality of our interference and capac-ity estimation engine is shown in Figure 1. The figure in-cludes all the basic blocks of the tool, and our GUI is able toshow the functionalities of all the functional blocks and in-put/output parameters in real-time. The spectrum sensingmodule is using USRP-radio to scan the 2.4 GHz frequencyband for useful channels and records various interferencemetrics such as duty-cycle of the channel and power spec-tral density. This information is used to build interferencemaps and channel rating vectors for the best-channel or-dering per node. The channel rating vectors are sent in thedemonstration to central node (although also distributed op-erations could be supported), where the actual best-channelnegotiation and decision between source-destination pairsis done. This coarse gained best channel selection triggersa further interference measurement protocol, which gener-ated various interference matrices for actual computation ofscheduling e!ectiveness[1, 6]. Although these basic modulesare the real aim of our demonstration, we will also show ascheduling-aware of routing as a part of the system demon-stration. The best available routes are calculated based onthe channel rating vectors and interference matrices. Theoverall capacity estimation engine is the first fully func-tional implementation of these principles we are aware of.Although our demonstration is using specific hardware, webelieve that the demonstration and techniques have a widerinterested for the community, since in our implementationwe have carefully avoided using any deep hardware specifics.Thus it should be quite straightforward to port these mod-ules to almost any suitable SDR platform.

Overall the system demonstrates that capacity estimationcan be done with a reasonable accuracy in cognitive radionetworks. The key part of the demonstration is also to showhow this information can be used to enhance operationsof the network, and the chosen video-streaming applicationshows in real-time how perceived user quality of service is

indeed a!ected by these choices. The system demonstra-tion also shows pros and cons of interference and schedulingaware routing in Multi-Hop Wireless Networks. Due to in-evitable limitations of time, space and regulations, we limitour demonstration to work in 2.4 GHz ISM-band. Thus wemay comment some DSA issues and possibilities in a slide-show and material we would bring into workshop, we do notpropose to make full-scale dynamic spectrum allocation withour SDRs.

2. BASIC IDEA, GOALS AND NOVELTYThe basic idea of the demonstration is to show that mod-

ern software defined radios are now capable for support-ing e"cient estimation of link capacities and measure in-terference graphs in a distributed manner. The goal of thedemonstration is not only to prove this, but go beyond thatby showing how this information can be used to developscheduling-aware routing for multi-hop and multi-channelnetworks. The work is a part of our larger CRM Framework,and as such we have an added benefit that the demonstra-tion is built to support heterogeneous SDR platforms. Inthe demonstration we use both USRP and WARP hardwareand WiFi for control channel purposes. This enables us toshow full flexibility of SDR in this context instead of beingrestricted, e.g. by the interfaces of commercial WiFi-cards.Also as far as we are aware of this is the only demonstratorbased on heterogeneous networking technologies. This het-erogeneity of network and technologies should also make ademonstration highly interesting for the audience.

The demonstration session is an ideal for our presentation,since we will be able to interactively show how developedmodules are working. Moreover, we believe that for thissort of work, where a lot of implementation domain workhas been done, demonstration or poster session is ideal, asit allows more free interaction with audience and allows us toexplain in mode detailed fashion some of the implementationrelated complexities.

A part of the demonstration has been shown earlier inMobiHoc 2009 [3], but the current demo is a substantialextension of that work and we are planning to show morefully functional network in Beijing. The work itself and theextensions in this demonstration have not been yet publishedin other conference or journal papers.

2.1 Basic ScenarioThe basic example application is the transfer of data over

the wireless network, and as a specific visual application weshow the use of video. Data packets are transferred over thenetwork that is based on WARP-boards, which are config-ured to be OFDM-transceivers with high adaptivity. Thechannel sensing is performed by USRP-boards using GNURadio software framework. This part of the network is do-ing passive sensing in 2.4 GHz frequency channel. Finally,we use IEEE 802.11 based protocol to implement controlchannel. In order to keep demonstration complexity reason-able there is a single central co-ordinator that coordinatesthe operations of nodes and runs GUI which is used explaindemonstration to interested audience.

The basic premise of the scenario is to show how the sys-tem monitors di!erent interference patterns and link quali-ties, and then selects the appropriate high capacity links. Inthis sense we welcome some interference from other possi-ble wireless demonstrations and we do not require dedicated

Figure 2: A picture of one of the central controlnodes with both USRP and WARP boards.

frequency channels. However, just being safe we will bringenough equipments to generate our own interference sourcesif necessary. Thus we are certain that we can demonstratethe CSMA interaction detection and capacity estimation un-der almost any conditions in the room.

3. REQUIREMENTS AND LOGISTICSWe have capability to scale our demonstration based on

availability of space in the demonstration room. In maxi-mum configuration we would bring in 8 laptops or Mac minicomputers and set of 7 WARP and USRP radio boards. Wewould also bring in one IEEE 802.11 wireless router to buildup a control channel for the demonstration (this channeldoes not need to be dedicated for us). All the radios willoperate at 2.4 GHz ISM frequency below 100 mW transmis-sion power and as such are also following Chine’s frequencyregulations. We would request from conference organizersa dedicated table for our demonstration, one wired internetconnection (if possible), power extension cords (a total of 25sockets). The demonstration area required is approximately4 m ! 2 m. If it were possible to locate some of the radiosalso other to parts of the demonstration area (extra tablewith power cords) we could increase spatial capabilities ofthe demonstration, but this is not absolutely necessary justa nice addition. Finally, if one large TFT-screen (20!! - 24!!)could be provided by organizers to show GUI this would in-crease the user friendliness of our demonstration, otherwisewe will use laptop screen.

As mentioned earlier, we do not require any dedicatedWiFi channel, and in fact we hope to find some moderateinterference in demonstration room to show how dynamicsof our capacity and interference estimation engine.

Some of the authors are students, but they are not un-fortunately currently ACM members, hence we are not cur-rently eligible for the student demo competition. The prac-tical development of the demonstration is based in large partto two M.Sc. theses [2, 5].

4. FINAL NOTESIn the case the demonstration is accepted, we are planning

to ship equipments with air-freight (DHL) to Beijing and

demonstration team comprises two (or more) persons. Wehave cleared from the funding agencies of our projects thatsupport for travel would be available.

Furthermore, if the demonstration abstract were to bepublished in proceedings or other form, we are committedto polish it to acceptable publication level and to take intoaccount any possible comments from demonstration TPC.

AcknowledgementsThis work was in part financially supported by German Re-search Foundation (DFG) through UMIC Research Centrefunding and European Union through ARAGORN-project.

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[2] A. Greco. Scheduling-aware Routing in Multi-HopWireless Networks. In Diploma (M.Sc.) Thesis, RWTHAachen University, Department of Wireless Networks,2009.

[3] A. Greco, T. Milcher, V. Kolar, P. Mahonen, andM. Petrova. CSMA interaction detection and capacityestimation in cognitive radio networks. In MobiHoc ’09:Proceedings of the tenth ACM international symposiumon Mobile ad hoc networking and computing, pages321–322, New York, NY, USA, 2009. ACM.

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[7] Rice WARP-project. http://warp.rice.edu/.[8] The GNU Radio Project.

http://www.gnu.org/software/gnuradio.