An Efficient Approach for Evaluating Performance in LTE ... · PDF file(RSRQ) can be defined...

An Efficient Approach for Evaluating Performance in LTE Wireless Networks

Yunus Egi, Engin Eyceyurt, Ivica Kostanic, and Carlos E. Otero

Abstract— for cellular network planning and optimization, one of the fundamental methods is to perform drive test by means of scanners, phones, and receivers. Since Long Term Evolution (LTE) is rapidly taking place in the world market, the importance of quality of service (QoS) becomes essential in terms of customer satisfaction. In LTE cellular networks, Block Error Rate (BLER), Reference Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ) can be defined as Key Performance Indicators (KPI). In the case of LTE, the QoS and coverage of cellular networks can be explained by Reference Signal Received Power (RSRP) of the dedicated control channel. Performing phone-based measurement requires tedious and costly procedures in terms of accessing network providers. In this study, the measured average RSRP is used to assess and eliminate detailed drive tests in LTE hardware performance assessments.

Keywords—Drive test; LTE; RSRP; VPL; Interpolation

I. INTRODUCTION The main goal of LTE is to expand capacity, obtain higher peak data rates, and minimize latency while being flexible in frequency and bandwidth usage in telecommunication systems [1]. LTE network architecture uses native IP network which creates a downlink speeds up to 150 Mbps and an uplink speeds up to 75 Mbps (Release 8) [2].During phone calls, user equipment (UE) changes its position from one LTE base station (eNB) to another eNB. In order to maintain the connection between UE and eNB, UE performs handover for cell selection or re-selection by means of measuring key performance parameters. These parameters can be classified as coverage and interference parameters. The coverage parameters consist of Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ),and Receiver Signal Strength Indicator (RSSI). RSSI is defined as average total received power taken by UE from all sources for instance serving eNBs and other interferers. RSSI parameters indicate only symbols that comprise the reference symbols for antenna port 0 that contains the power of other serving cells and other sources of noise [3]. RSRP and RSRQ are essential measurements for determining cell selection and making decisions on handover [4]. RSRQ is calculated by following formula .

The number of resource blocks that belong to RSSI’s bandwidth is represented by N [5]. The coverage information and interference occurrence within the location can be found by comparison of RSRP and RSRQ. Therefore, it is helpful to see the stability of Radio Frequency (RF) environment and

diminish the ambiguous part of the interference. The interference parameters can be grouped in two categories - natural and man-made interferences [6]. If interference happens naturally, it is hard to control by human beings. However, if the interference is man-made then there is a way to manipulate and mitigate the level of interference. Man-made interference parameters can classified as co-channel interferences, adjacent channel interferences, vehicle penetration loss (VPL), and thermal noise which will affect the efficiency of the mobile network services. Mobile communication services deal with a highly competitive market which requires customer satisfaction. Therefore, QoS investments will be the first priority for the service providers. The operators have different solutions for mobility issues which are related to insufficient bandwidth usage [7]. Some factors that are affecting the QoS are network accessibility, voice quality, handover, and coverage area. All these factors depend on RSRP which is an essential parameter in the highly interactive RF environment. With Technological advancement, the task of the RF engineers, which is to determine cellular network quality from the customer perspective by measuring received signal power, was made easier through drive tests. Basically, there are three elements (i.e., phones, receivers, and scanners) that are used in drive tests systems. These three elements embody different characteristics and related benefits and drawbacks. For instance, phone-based drive test measurements depend on the particular network service provider which is considered as a handicap. On the other hand, receiver- and scanner-based drive test measurements are independent of any operator. These systems monitor the RF activity of the survey areas without determining the precise problems in the network. However, scanner-based drive test systems are considered as a specialized receivers that can monitor the RF activities in different frequencies. The analysis of the raw data is evaluated by some specific software which maps and plots the RF activity or by some statistical analysis tools such as MATLAB or SAS. The service providers changed their focus from simple RF processes to end user application performance, since in first quarter 2014 the data traffic increased by 65% relative to the first quarter of 2013 [8]. Therefore, the importance of the data usage such as streaming videos and Voice over IP (VoIP) will be extremely important for the near future. RF engineers usually perform the detailed drive tests and some tools to see the RSRP performance on application level from the customer perspective. The detailed drive test include driving along all the major roads as well as sub-roads and alleyways. The main goal of this paper is to eliminate the need to traverse the detailed route by comparing detailed and major road drive

48 Int'l Conf. Wireless Networks | ICWN'17 |

ISBN: 1-60132-462-6, CSREA Press ©

tests via interpolation and statistical analysis techniques. This paper is arranged as follows: Section-2 Background work; Section-3 Data collection and drive test methodology; Section-4 Experiment 1 phone-based drive test; Section-5 Experiment 2 receiver based drive test; Section-6 Conclusion.

II. BACKGROUND WORK Many different methods are utilized to evaluate the cellular network performance. One of the essential methods used to see the robustness of the system is repeatability test among measurement tools. RF engineers use the drive tests in order to see the repeatability of the measurements. If the drive tests show the correlation, then this will be an indication of stability. Therefore, the mean of errors will be very significant for the drive test tools. In the case of this study, phone and scanner are used to see the repeatability of the drive tests. These two devices have different sensitivity and behavior with respect to their purpose of use. Many experts perform the drive test in different environments to analyze the received signal level for different frequencies. For instance, in his study, Young performed a drive test to demonstrate how location and different frequencies changed the coverage in terms of comparison of real and theoretical path loss in New York City [9]. Likewise, W. Mark Smith repeated the same drive tests in San Francisco on two different days space apart by a few months to monitor the effect of the large-scale fading [10]. In the last decade, I. Weissberger, I. Kostanic, and C. E. Otero performed some drive tests for evaluating the quality of service (QoS) for background services in UMTS network by means of Mean Opinion Score (MOS) as a methodology. In their paper, three different routes in the suburban area were chosen and QoS parameters such as accessibility, sustainability, average throughput, bit error rate, and jitter were found for each route [11]. On the other hand, the interpolation technique is used to determine unknown values between two data points. F. Weng, C. Yin, and T.Luo used linear and DFT based interpolations for channel estimation in a simulation of 3GPP LTE system. In that study, channel responses of data tones are derived from neighbor tones by means of interpolation. Then least square and linear minimum mean square error based channel estimators are compared [12]. This paper presents a methodology for assessing performance by eliminating detailed drive test for LTE. The proposed approach relies on repeatability of two drive tests and interpolation analysis. The drive tests were conducted in Melbourne, Florida as shown in Fig. 2. The study shows that the accuracy of these drive tests using different devices is significant enough to reveal the intricacies and unpredictability of environment response which cannot be determined by computer simulations.

III. DATA COLLECTION AND DRIVE TESTMETHODOLOGY

Drive test is the process of systematically collecting data from routes with the goal of characterizing the cellular network.

Hence, it has a tremendous importance for optimization and network planning. Under certain conditions, drive testing provides results that are reasonable for deploying or assessing the performance of networks in practical RF environments. Practically, the experiment area can be divided into major and detailed roads. In the detailed road test, data are collected from all the public roads which contain all the streets and avenues. On the other hand, only long and wide avenues which have a traffic density are used in the major road test. In this study, drive testing of the major and detailed road will be performed in order to visualize the differences between the major and the detailed roads. For these experiments, two different hard-ware, phone and scanner will be utilized. The real benefits of using both scanners- and phone-based drive tests is to avoid the error that the user will encounter with only one device [13].

A. Equipment Setup The phone and scanner were configured to take RSRP measurement within a predefined area as seen in Fig. 2. For this objective, the phone and scanner are located inside the 2006 Honda Civic Coupe. The GPS and RF scanner’s antennas are placed on the top of the car, whereas the receiver is located in the back seats as seen in Fig. 1. An external monitor is used to configure the scanner by means of an internal computer. The scanner is set to take the measurement from Channel 5110 which is belong to US Lower 700-A B C Blocks DL Top N Signal. In the spectrum, the channel 5110 corresponds to 739MHz.The LTE only option is chosen in order to get only LTE data. It should be emphasized that the scanner uses the fixed frequency parameters defined by RF engineers such as frequency range, channel number, and technology type while the phone uses the list of dynamic channels with respect to their C/I ratio values.

Fig. 1. Equipment Setup.

B. Data Collection and Drive Test Methodology In cellular systems, data collection can be defined as the process of collecting information from the survey area for the purpose of analysis and evaluation. From the perspective of wireless communication, the following steps are followed to get the best results from this experiments.

The survey area is fixed. The drive test route is planned to minimize the driving time. The equipment configuration is rigorously checked. The measurements are taken and saved.

For the survey area, West Melbourne—near Florida Institute of Technology, bounded by coordinates (Lat (28.068: 28.053),

Int'l Conf. Wireless Networks | ICWN'17 | 49


Lon (-80.650: -80.625)) -- for the sake of the drive tests as seen in Fig. 2. West Melbourne is considered as a suburban area where the terrain is relatively flat and rich with vegetation. The advantage of this type of drive tests is that the engineers will be able to see the real behavior of RF environment. In these experiments, the time of the day and weather conditions are kept constant to reduce the variation of fast fading effect that increases the signal path loss. The planned route is traversed on different days under the same conditions. Total area driven is approximately 9 square miles. The frequency band that is chosen for this experiment belongs to one of the most popular US cellular network providers (AT&T). There are 60 base stations which are in use within the survey area. However, 36 of them have communication with equipment that is used.

C. Geo-referencing and Unit conversion

Geo-referencing is a method of determining the geographic coordinate of the measurement points that are taken from scanner or phone. Inertial measurement unit (IMU) and GPS are the fundamental tools which are used to geo-reference the measurement data. In this paper, we will use the geo-referenced data in order to obtain Cartesian coordinate system. In Fig.2 shaded area shows the surveying area that will be driven. In that system, dlat and dlon represent the boundaries for latitude and longitude respectively. These distances are converted in meters by using orthographic projection technique [14]. The equations given below will be used in data binning processing.

Fig. 2 Orthographic projection technique

(2)

(3)

Where

(4)

(5)

D. Data Binning Data binning is used for minimizing the effect of the minor errors of data [15]. With this pre-processing technique, data sequences are placed into small groups called bins. In our study, the geographical area is divided into square-shaped bins which demonstrate the average values of the data inside the bins. Fig. 3 shows how the data binning technique works for the drive testing scenario. The black dots represent the measured data which falls into 50m x 50m bins. The average RSRP value is assigned to Bini+1 which indicates the bin value. By leveraging on the data binning technique, fast fading effects on measured RSRP values are eliminated.

Fig. 3. Driven Main and Detailed Roads in Melbourne Florida.

Bin values are estimated by means of the following logarithmic averaging method :

RSRP values are given in dBm indicates the average RSRP values of bin

represents data sequence within a bin represents the total number of values

The bin size is determined as 50m x 50m. Latitude and longitude values are also given for each bin.

Fig. 4. Representation of Binning Process.



The following pseudo codes explains data binning process. Algorithm 1: Data Binning Process

1: Install the data (Lat, Lon, RSRP) 2: Define R (Radius of earth) 3: Determine boundaries for (Lat, Lon) 4: Determine number of bins (Nlat, Nlon) 5: Form RSRP matrix6: Form bin-counter matrix 7: [row, col] =size (data) 8: For r=1: row 9: If isnan (data(r, 1) ==0 && isnan (data(r, 2)) ==0 10: Determine BinRSRP indexes I and J within the bin 11: BinRSRP (I, J) = BinRSRP (I, J) + data(r, 3) 12: Bncount (I, J) =bin-counter (I, J) +1 13: end14: end15: Reformatting 16: [K, L] =find non-zero bin-counter indexes 17: Form final RSRP matrix

18: For i =1: length (I) 19: Assign the average RSRP values to RSRP matrix 20: end

E. Natural Neighbor Interpolation Interpolation can be interpreted as a prediction of an unknown value that falls between two known values. In other words, it is a process of approximation of missing values within existing data. This technique is significant since it finds the correlation between spatially distributed data points. Moreover, it is considered that the points which are close to the reference point have more likely the same behavior [17]. Interpolation technique is performed for estimation of numerous solutions such as temperature maps, geographical measurements, and cellular network coverage. There are several extensive interpolation techniques that are currently in use in industry, such as Inverse distance Weighted (IDW), Kriging, Natural Neighbor, and Spline [17]. The one that is used in this paper is natural neighbor interpolation technique which determines the closest subset of sample data points to the reference point and weights them based on the corresponding area to interpolate a value [17]. For this method there are some assumptions used:

Any data points that fall into the outside of convex hull will not have any value. Even if some of the data points fall into convex hull but their center point is outside of the convex hull then no value will assign that data points too. Duplicate coordinates will not have any effect on output values. The projected coordinate system will be used instead of geographic coordinate system to visualize the data.

Where:

: The estimation of data at

: Weights (How much surrounding area is stolen when we insert (x, y))

: The corresponding data set

Delta interpolation (DI) is a technique that simply shows the differences of two interpolated datasets. In this technique, the interpolated datasets are linearly subtracted from each other as seen in the following formula .

In the following experiments, the natural neighbor interpolation and delta interpolation are used to make a comparison between major and detailed roads.

IV. EXPERIMENT 1 PHONE BASED DRIVE TEST In this section, two experiments, major road experiment and detailed road experiment, are performed in order to see the differences between them. The raw data are collected by phone within Melbourne, FL area and binned using 50 m frames. The natural neighbor interpolation technique is applied to binned data to obtain estimated RSRP value of the unknown information.

A. Major Roads The purpose of this experiment is to drive-test the predefined area only on the main roads and analyze the data by means of natural neighbor interpolation. After applying this interpolation technique, the results showed that the RSRP values are very strong near the the base station which is located on intersection between Eber road and Dairy road. The RSRP values fade away as the distance increases. Fig. 4 shows the binned and interpolated data of major roads.

Fig. 5. Phone based Major Roads Grid interpolation.



B. Detailed Roads The purpose of this experiment is to drive through the determined test area in detail and analyze the data with respect to interpolation. After applying interpolation technique, the results showed that the RSRP values are stronger nearby the base station. Since the detailed roads contain more information about unknown data, the interpolation results illustrate better outcomes with respect to RSRP values. Fig. 5 demonstrates the binned and interpolated data of major roads.

Fig. 6. Phone Based Detailed Roads Grid Interpolation.

C. Comparison of Major and Detailed Roads The comparison between two drive tests is done by using hypothesis testing. The null hypothesis (H0) is that all average RSRP values that are obtained from the major roads and detail roads are equal. The p-value is determined as 0.1 which correspond to 90 % of the time the RSRP values falling into the desired area. According to the results obtained from the correlation matrix, the two drive tests show 91 % correlation as it is shown in Fig. 6.

Fig. 7. Correlation Matrix between Detailed and Major roads of Phone based Measurements.

On the other hand, the differences between two drive tests can be seen from the differences between two interpolations. In Fig. 7, green color demonstrates that the average RSRP values between two drive tests are the same. In other words, the differences between means are equal to zero.

Fig. 8. Delta Grid Interpolation for Phone based Measurement.

Likewise, the p-value which is obtained from ANOVA in Table. I is approximately equal to 0.1 which means that 90 % of the time the two drive tests have the same average RSRP values. In other words, we cannot reject the null hypothesis (H0).

TABLE I ANOVA (PHONE BASED) SumSq DF MeanSq F p-value

X1 109.7 1 109.7 2.7488 0.098594Error 9857.6 247 39.909

V. EXPERIMENT 2 SCANNER BASED DRIVE TEST The purpose of this section is to repeat the same experiments with a scanner in order to consolidate the results that are obtained from both scanner- and phone-based experiments. The raw data are collected under the same conditions via scanner. The data binning and the interpolation process remain the same as experiment 1.

A. Major Roads

The natural interpolation is applied to the scanner-based data taken from major roads. The Fig. 8 illustrates that RSRP values—which are taken from scanner—are stronger than the RSRP values recorded from the phone-based approach. This makes sense since scanners are known to have higher performance relative to phones. However, the characteristic of the signal shows the same behavior in terms of response to the environment.

Correlation Matrix

-100 -90 -80 -70var2

Corrolation of Phone Based Drive tests

-100 -90 -80 -70

-100

-90

-80

-70

var1

var2

-100

-90

-80

-70

var1

0.91

0.91



Fig. 9. GAR based Major Roads Grid interpolation.

B. Detailed Roads

In terms of signal strength, the scanner demonstrated a better performance. In Fig. 9, the detailed drive test results show the average RSRP values more precisely. The location of the base station and how the signals fade away can be seen in detail. The unknown information can be lesser related to the data collected by the phone since the amount of data that is collected by the scanner (per second) is much more than the amount of data collected by phone.

Fig. 10. GAR Based Detailed Roads Grid Interpolation.

C. Comparison of Major and Detailed Roads

The comparison of two drive tests shows a better correlation in terms of average RSRP. The correlation matrix shown in Fig. 10 indicates that the correlation between main and detailed

drive test is 0.92 which means that 92 % of the time the RSRP values show similar outcomes.

Fig. 11. Correlation Matrix between Detailed and Major roads of Phone based Measurements.

Moreover, as seen in Fig. 11, the delta interpolation of the average RSRP shows that the green and light blue areas cover almost the entire map which suggests that both drive tests have almost the same result.

Fig. 12. Delta Grid Interpolation for GAR based Measurement.

According to ANOVA in Table. II, the p-value is approximately equal to 0.03 which means that 97 % of the time the two drive tests have the same average RSRP values. In other words, we cannot reject the null hypothesis.

TABLE II ANOVA (GAR BASED) SumSq DF MeanSq F p-value

X1 235.8 1 235.8 4.6667 0.031367Error 19554 387 50.528

Correlation Matrix

-100 -90 -80 -70 -60var2

Corrolation of Scanner Based Drive tests

-90 -80 -70 -60-100

-90

-80

-70

-60

var1

var2

-90

-80

-70

-60

var1

0.92

0.92



VI. CONCLUSION In this paper, elimination of detailed drive tests in LTE performance assessments is evaluated. Two main hard-ware, phone and scanner, are utilized to perform the experiments. Major and detailed roads in Melbourne, FL are selected as survey areas. The natural interpolation technique is used to determine unknown information on non-driven areas. As aresult, phone and scanner based drive tests demonstrate that major roads and detail roads give 90 % and 97 % accuracy in terms of average RSRP respectively. These results propose a significant contribution since it suggests that the detailed drive testing can be replaced by the more efficient, relatively inexpensive, and effortless major road-based drive test. The proposed approach is expected to improve the efficiency of performance evaluation techniques in practice. However, to further improve the process, the following areas of work are suggested. Since LTE will be tuned for Narrow-Band IOT which will allow the cellular systems to connect and exchange the information with low-battery depended on devices that only send a small amount of information, this experiment may be applied for the coverage optimization of NB-IOT systems. It may be beneficial to apply this method on an urban area in order to see how much the diffraction losses affect the received signal power. This will allow the cellular systems to connect and exchange the information with low-battery devices that only send a small amount of information level. This technique is also applicable for next generations of cellular network technologies such as LTE-Advanced and 5G.

REFERENCES

[1] M. Nohrborg, "LTE," 3GPP, 2008. [Online]. Available: http://www.3gpp.org/technologies/keywords-acronyms/98-lte. [Accessed 2015].

[2] Ya’acob, Norsuzila Muhammad Salihin Ahmad Azmil1,2, Khairul Rahim Khairudin1 and Suzi Seroja Sarnin1, "Coverage and Quality Radio Analysis for Single Radio Access Network Technology in GSM," in International Conference on Electrical, Electronics and System Engineering (ICEESE), Kuala Lumpur, 2014.

[3] A. Technologies, "LTE and the Evolution to 4G Wireless," in Design and Measurement Challenges, John Wiley & Sons, 2013, 2013, p. 168.

[4] P. Staroulakis, "Cochannel interference," in Interference Analysis and Reduction for Wireless Systems, Artech House, 2003, p. 407.

[5] Sevindik, V. Jiao Wang ; Bayat, O. ; Sevindik, V. ; Weitzen, J., "Performance evaluation of a real long term evolution (LTE) network," in Local Computer Networks Workshops (LCN Workshops), 2012 IEEE 37th Conference on, Clearwater, FL, 2012.

[6] Cesidio Bianchi, Antonio Meloni, "Natural and man-made terrestrial electromagnetic noise: an outlook," in ANNALS OF GEOPHYSICS, Rome, 2007.

[7] G. Horvath, "End-to-end QoS signaling for LTE," in Telecommunications Forum (TELFOR), Belgrade, 2013.

[8] M2M.World.News, "Ericsson Mobility Report," Rima Qureshi, Global, 2014.

[9] M. Patzold, Mobile Radio Channels, WILEY, 2012

[10] W. Smith, " the repeatability of largescale signal variations in an urban propagation environment," in Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, Cox, D.C., 2003.

[11] Ira Weissberger, Ivica Kostanic, and Carlos E. Otero, "Background Service QoS in a UMTS Network," in IEEE SoutheastCon, Charlotte, NC, 2010.

[12] Fanghua Weng, Changchuan Yin, Tao Luo, "Network Infrastructure and Digital Content," in 2nd IEEE International Conference on Date of Conference, Bejing, 2010.

[13] M. Rumney, "LTE and the evolution to 4G wireless," in design and measurement challenges, Chichester, West Sussex, United Kingdom : John Wiley & Sons, 2013, 2013.

[14] Erik W. Grafarend, Rey-Jer You, Rainer Syffus, Map Projections Cartographic Information Systems, Verlag Berlin Heidelberg: Springer, 2014.

[15] PCTEL, "Effects of Multi-Technology Scanning on Data Density," PCTEL, 2014. [Online]. Available: http://rfsolutions.pctel.com/artifacts/DataDensityApplicationNoteRevA.pdf.

[16] R. F. G. J. L. H. Robert L. Mason, Statistical Design and Analysis of Experients, New Jersey : John Wiley&Sons, 2003.

[17] A. Resources, "Understanding interpolation analysis," 11 2012. [Online]. Available: http://resources.arcgis.com/en/help/main/10.1/index.html#//009z0000006w000000. [Accessed 2014].

[18] Rafael C. Gonzalez ,Richard E. Woods, Digital Image Processing (3rd Edition), Upper Saddle River, NJ: Pearson Education, 2006.



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