In this work, we show how to set up a cellular LTE network for wireless experimentation and measurement, relying on commodity hardware and open-source software. We first deploy a complete LTE network on a single laptop and an SDR hardware. Then, we use it to evaluate the reception performance of a commercial smartphone. In the end, we propose a calibration technique which allows using a smartphone as a wireless power measurement tool by reducing the root-mean-square error of the RSSI, with respect to a reference power measured with a specialized spectrum analyzer, from 6.4 dBm to 2.4 dBm

Boussad, Yanis

Dabbous, Walid

Ferrero, Fabien

Legout, Arnaud

Lizzi, Leonardo

Naoufal Mahfoudi, M

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HAL Id: hal-02445762https://hal.inria.fr/hal-02445762v2Submitted on 14 Apr 2020HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Open-Source 4G Experimental Setup (Report)Yanis Boussad, M Naoufal Mahfoudi, Arnaud Legout, Walid Dabbous,Leonardo Lizzi, Fabien FerreroTo cite this version:Yanis Boussad, M Naoufal Mahfoudi, Arnaud Legout, Walid Dabbous, Leonardo Lizzi, et al.. Open-Source 4G Experimental Setup (Report). [Research Report] Inria & Université Cote d’Azur, CNRS,I3S, Sophia Antipolis, France. 2020. ￿hal-02445762v2￿Open-Source 4G Experimental SetupYanis Boussad, Arnaud Legout,and Walid DabbousUniversité Côte d’Azur, InriaSophia Antipolis, Franceyanis.boussad@inria.frLeonardo Lizzi, Fabien FerreroUniversité Côte d’Azur, CNRS, LEATSophia Antipolis, Franceleonardo.lizzi@unice.frM. Naoufal MahfoudiUniversity of California San DiegoSan Diego, USm1mahfoudi@eng.ucsd.eduAbstract—In this work, we show how to set up a cellular LTEnetwork for wireless experimentation and measurement, relyingon commodity hardware and open-source software. We firstdeploy a complete LTE network on a single laptop and an SDRhardware. Then, we use it to evaluate the reception performanceof a commercial smartphone. In the end, we propose a calibrationtechnique which allows using a smartphone as a wireless powermeasurement tool by reducing the root-mean-square error ofthe RSSI, with respect to a reference power measured with aspecialized spectrum analyzer, from 6.4 dBm to 2.4 dBm.I. INTRODUCTIONCommodity hardware in general and mobile smartphones inparticular are getting more and more attention in the researchcommunity to replace the specialized equipment, either forsignal generation by using Software Defined Radio (SDR) [1]or for spectrum measurements [2][3][4].With the proliferation of SDR technologies, several softwareimplementations of the cellular network protocols are freelyavailable as open-source programs. They allow emulating acellular network on commodity hardware that would otherwiserequire a specialized and expensive hardware. For instance,OpenAirInterface (OAI) [5], is an open-source, complete,software implementation of an LTE cellular network that canrun on general-purpose processors. In addition, smartphonesare seen as a possible replacement for specialized hardwarefor wireless measurement. Tan et al. proposed Snoopy [2],a spectrum analyzer that uses commodity Wi-Fi cards withfrequency translators to sense a wide range of frequencies.Ana et al. [3] used a portable SDR (RTL-SDR) dongle thatsenses a continuous spectrum range from 52MHz to 2200MHz, which they connect to a smartphone through USB.In contrast to the two aforementioned works that rely onexternal hardware, CrowdREM [4] relies only on smartphonesfor spectrum analysis. The authors used an open-source mo-bile phone (OpenMoko) with a modified Linux system andOsmocomBB as the baseband firmware. They showed thatsmartphone accuracy is within 3 dBm while the device is still,but very sensitive to orientation with respect to the source, upto 10 dB difference.As opposed to previous works [2][3], the solution proposedin this paper relies only on a smartphone without any externalhardware. Moreover, the solution doesn’t require hardwareor software modification on the smartphone. In addition, wemitigate the inaccuracy of smartphones [4] with a calibrationLTE Network DuplexerUSRPOpenAirInterface𝜑θxyzyxzFig. 1. Experimental setupalgorithm that uses the Inertial Measurements Units (IMUs)of the mobile device to determine the correction power offsetto apply.II. EXPERIMENTAL SETUPWe developed a framework for wireless cellular experi-mentation based on commodity hardware and open-sourcesoftware. The framework allows signal generation, devicetesting, and wireless power measurement.1) Cellular signal generation: Instead of using specializedhardware for generating cellular networks, we use OAI asa cheap and working alternative. We proceed as follows togenerate the signal. The Core Network (CN) and the RadioAccess Network (RAN) components of OAI should run on twodifferent machines to ensure real-time performances. To reducethe deployment costs of our setup, mobile data should bedeactivated which will considerably reduce the computing loadon the processor. Hence, both the CN and RAN componentscan run on the same machine. We use an HP Zbook laptoprunning Ubuntu 16.04 LTS with Intel i7-6th-gen processor and32 GB of RAM. We connected the laptop to an Ettus B210Universal Software Radio Peripheral (USRP). The USRP isthen connected through a duplexer to the transmitting antenna(Figure 1). The band 7 duplexer is used to connect both theRX and TX channels of the USRP to the same antenna. Weuse an ETS-Lindgren’s 3115 double-ridged horn antenna asa transmitting antenna. It is a directional antenna with linearpolarization having a gain of 10 dB at 2.5 GHz, and supportinga wide range of frequencies ranging from 750 MHz to 18 GHz.2) Device Under Test: For the device under test, we use aNexus 5X smartphone running Android 7. In order to attachthe smartphone to the network, we program a SIM cardwith the necessary authentication parameters that we definedbeforehand in the database of OAI. This allows the smartphone-180-160-140-120-100 -80-60-40-20 0 20 40 60 80 100120140160 (degrees)-90-80-70-60-50-40-30-20-100102030405060708090 (degrees)726864605652RSSI(dBm)Fig. 2. Reception pattern of the smartphone at 684 (19x36) differentorientations along ϕ and θto discover and attach to the network.3) Controlled environment: We perform our experimenta-tion in an anechoic chamber that has a programmable roboticequipment both at the transmission and the reception sides.As shown in Figure 1, the reception platform is a two-axispositioning system that rotates along two axis: ϕ and θ. ϕ canrotate 180◦ (from -90◦ to +90◦) whereas θ can make 360◦rotation. The reception and transmission are separated by adistance of 4 meters. The two platforms are connected to acontroller system outside the chamber. We can program therotation of the platforms by defining the rotation range, thestep, and the time duration it remains at each orientation.III. RECEPTION PERFORMANCE OF A SMARTPHONEWe use our experimental setup to evaluate the receptionperformance of the Nexus 5X smartphone. We first attach thesmartphone to the LTE network on band 7 FDD at 2.68 GHzcreated using OAI. We mount the smartphone on the receptionplatform as shown in Figure 1 and put it in 684 differentorientations in front of the horizontally polarized transmittingantenna, by varying θ from -180◦ to +170◦ and ϕ from -90◦to +90◦, with a step of 10◦ each time.1 At each orientation, wecollect the Received Signal Strength Indication (RSSI) and thesmartphone orientation in space from the orientation sensorsby making calls to Android APIs. The resulting receptionpattern is depicted in Figure 2. As shown, the received poweris not uniform across the 684 different orientations.IV. SMARTPHONE CALIBRATIONIn this section, we propose a technique to improve theaccuracy of the smartphone measurements which allows usingthe phone to replace specialized hardware such as a spectrumanalyzer for accurate wireless power measurements. We startby measuring the received power at the reception point usinga spectrum analyzer. We obtain the reference power to be -54dBm. In order to increase the accuracy of the smartphone mea-surements, we use two calibration matrices P and Q. P containsthe gain correction (in dB) w.r.t. the reference power in the684 tested orientations. Q contains these 684 orientations. Thematrices P and Q are then embedded inside the smartphoneas an Android application2. The calibration works as follows.1Results for the vertical polarization can be obtained by rotating thetransmitting antenna by 90◦ along the x-axis.2The Calibration matrices P and Q of 7 different smartphone models willbe made publicly available for the camera-ready version of the paper.Orientation matrix QPower matrix POrientation qi (θ,φ)Raw power pigain offset at (θ,φ) Calibrated power pcAntennasOrientation sensorSmartphoneFig. 3. Calibration process-168 -127 -108 -78 -62 52 94 122 147 173 angle (degrees)706050Power (dBm)Calibration for differente  anglesmeasuredcalibratedReference power-82 -53 -25 9 17 26 43 59 73 84 angle (degrees)706050Power (dBm)Calibration for differente  anglesmeasuredcalibratedReference powerFig. 4. Boxplot of thecalibration results. Each of the 10 shown values ofϕ is tested with 10 random values of θ. A total of 100 orientations wereevaluated. The calibrated signal (in orange) is less variable, and close to thereference power than the raw signal (in blue).Whenever the smartphone is in a given orientation, we readthe raw RSSI power and the device orientation in space. Wesearch in the matrix Q the closest tested orientation. Then, weread from P the corresponding gain correction to be applied.This gain correction is then added to the raw power to obtaina ”calibrated” power corresponding to the value that would bemeasured with a specialized hardware. The calibration processis summarized in Figure 3. We evaluated the calibrationtechnique on 100 random orientations as indicated in Figure 4.The calibrated signal is less variable and very close to thereference power measured with a spectrum analyzer. The root-mean-square error of the RSSI for the 100 random orientationw.r.t the reference power is reduced to 2.4 dBm compared to6.4 dBm for the uncalibrated signal.V. CONCLUSIONIn this work, we showed how to realize an experimentalsetup for wireless cellular networks. We used commodity hard-ware and open-source software to emulate an LTE network.We demonstrated how this setup can be used to evaluate thereception performance of a commercial smartphone. Then,we showed the feasibility of using a commercial smartphonefor wireless power measurement by proposing a calibrationtechnique that improved the accuracy of the measurementsfrom 6.4 dBm to only 2.4 dBm of root-mean-square error3.VI. ACKNOWLEDGEMENTThis work has been supported by the French government,through the UCAJEDI Investments in the Future projectmanaged by the National Research Agency (ANR) with thereference number ANR-15-IDEX-01.REFERENCES[1] W. Tuttlebee, Software-defined radio: facets of a developing technology,IEEE Personal Communications, vol. 6, no. 2, pp. 3844, 1999.[2] T. Zhang, A. Patro, N. Leng, and S. Banerjee, A Wireless SpectrumAnalyzer in Your Pocket, Proceedings of the 16th International Workshopon Mobile Computing Systems and Applications - HotMobile 15, 2015.[3] A. Nika, Z. Zhang, X. Zhou, B. Y. Zhao, and H. Zheng, Towardscommoditized real-time spectrum monitoring, Proceedings of the 1stACM workshop on Hot topics in wireless - HotWireless 14, 2014.3The methodology can be applied to study any LTE device and thecalibration process is adaptive to the antenna performance.[4] A. Achtzehn, J. Riihihjrvi, I. A. B. Castillo, M. Petrova, and P. Mhnen,CrowdREM, Proceedings of the 16th International Workshop on MobileComputing Systems and Applications - HotMobile 15, 2015.[5] N. Nikaein, R. Knopp, F. Kaltenberger, L. Gauthier, C. Bonnet, D.Nussbaum, and R. Ghaddab, Demo: OpenAirInterface, Proceedings ofthe 20th annual international conference on Mobile computing andnetworking - MobiCom 14, 2014.

Open-Source 4G Experimental Setup (Report)

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Open-Source 4G Experimental Setup (Report)

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