Estimation of Network Load and Downlink Throughput Using RF Scanner Data for LTE

Networks

Dr. Kwangrok Chang Motiv Research co.

6-28-11 Minamioi, Shinagawa, Tokyo, Japankwangrok.chang@motiv-research.com

Mr. Ragil Putro Wicaksono Motiv Research co.

6-28-11 Minamioi, Shinagawa, Tokyo, Japanragil.wicaksono@motiv-research.com

Abstract— This paper provides a methodology of estimating downlink throughput using the drive test data collected by an RF scanner device rather than obtaining the downlink throughput via FTP downloading from UE. The throughput test using the UE’s FTP application to perform the throughput test has a merit of presenting the packet data speed from the end user perspective however, it also has significant shortcomings such as multiple RF layers cannot be tested simultaneously as UE can have only a single radio connection to eNB and no analytic performance benchmarking between different LTE networks is possible. The proposed algorithm utilizes the simple measurement quantities collected by an RF scanner in order to estimate the LTE network’s traffic load and the available PRBs without relying on network parameters or site information. Furthermore, the proposed algorithm calculates the potential downlink throughput at each location at which the RF scanner measured RSRP, RSRQ and SINR assuming that the available PRBs of the LTE cell are fully utilized at the location. Since the RF scanner is capable of measuring the multiple RF layers simultaneously, the proposed algorithm enables whole LTE networks to be benchmarked comprehensively. In addition, the proposed algorithm helps in analytically determining the root causes of the superiority or inferiority of a LTE network whether it is due to traffic load, system bandwidth, or spectrum efficiency.

Keywords— LTE network load, throughput, RF scanner, RSRP, RSRQ, SINR, PRB and benchmark.

I. INTRODUCTION

The downlink throughput performance is often regarded as the significant metric defining the end user satisfaction on the LTE network performance because it affects straightforwardly the data downloading speed of the various packet applications – streaming, data downloading through File Transfer Protocol (FTP), and HTTP web browsing, etc. It can be said from this perspective that the improvement of downlink throughput is the top priority of the LTE network optimizations [1].

It is specified in 3GPP [2] that the Transport Block Size (TBS), which is immediately related to the downlink throughput, is determined by the Physical Resource Blocks (PRB) allocated to the UE and the MCS (Modulation and Coding Scheme) index of the PRB. The MCS to be used in the PRB is determined by the eNB according to the UE’s reported

SummerSim-SPECTS, 2017 July 9-12, Bellevue, Washington, USA;©2017 Society for Modeling & Simulation International (SCS)

Channel Quality Indicator (CQI), which is not immediate interpretation of SINR but represents the quality of channel at the UE. UE’s reported CQI is directly proportional to the received SINR at the UE but how received SINR is converted to CQI is UE manufacturer specific [3].

It is noted from the above-mentioned fact that the downlink throughput of the UE can be obtained without the UE’s FTP downloading test if the number of remaining PRBs after the usage of common channels and the traffic channel at the eNB and the received SINR at UE’s location are known. A new algorithm is proposed in this paper to estimate the LTE network load, in other word, PRB usage ratio at the eNB accurately in terms of only RF scanner’s measurement entities, RSRP, RSRQ, RSSI, which are defined in 3GPP [4] and SINR [5]. Once the PRB usage ratio is calculated using the proposed algorithm from the RF scanner’s measurements, the downlink throughput is also estimated. The proposed algorithm unleashes the field tests and the performance benchmarking from the high requirements of complex test configurations and the unforeseen problems caused by FTP servers’ delayed responsiveness or UE’s temporary abnormal behaviors that are faced during the field test but in fact not directly relevant to LTE network’s performances. This algorithm is applicable for both FDD LTE and TDD LTE systems.

II. RELATED LTE THEORY

A. Downlink cell-specific reference signal

Cell-specific reference signal (RS) is a special signal thatexists only at physical layer. The purpose of RS is to deliver the reference point for the downlink power to be used by the UE for channel estimation. RS’s are carried by multiples of specific resource element (RE) in each slot. The number of RS and the location (symbol and frequency) depends on the number of transmit antenna ports and the physical layer cell identity (PCI). 3GPP [6] defines the mapping of downlink RS for one, two, and four antenna ports.

B. Available RE for Traffic and Control Channel

Depending on the number of antenna ports, the number ofRE used for RS and discontinuous transmission (DTX) is different, which will affect the number of the available non-RS RE that can be used for traffic and other control channels, or in

other words network load. For one antenna port, two REs per PRB are allocated for RS and the remaining 10 non-RS REs per PRB are available for traffic and other control channels in symbols where RS symbol present, while all 12 REs are available in the other symbol. The detail number of RE can be seen on Table 1.

TABLE I. NUMBER OF RE ALLOCATION PER PRB.

Antenna Ports

Symbol where RS0 present

Symbol where RS0 not present

RSRE DTXRE NRSREB RSRE DTXRE NRSREA

One 2 N/A 10 N/A N/A 12

Two 4 4 16 N/A N/A 24

Four 4 12 32 4 or N/A

12 or N/A

44.8 in average

Where, RSRE(n) is the total number of RE per PRB used for RS signal depending on the number of antenna ports. NRSREB(n) and NRSREA(n) are the total number of RE per PRB that can be used for non-RS signals at the symbol where RS present or not present, respectively.

C. Downlink Power Allocation

The eNB determines the downlink transmit energy per resource element (EPRE) for RS RE and the other signals (non-RS RE). The downlink reference-signal transmit power is defined as the linear average over the power contributions of all resource elements that carry cell-specific reference signals within the operating system bandwidth. The ratio of non-RS EPRE to cell-specific RS EPRE among non-RS REs for each OFDM symbol is denoted by either ρA or ρB according to the OFDM symbol index [6] as shown in Fig. 1. The cell-specific ratio, ρB/ρA is given by 3GPP in Table 2 according to cell-specific parameter PB signaled by higher layers and the number of configured eNB cell specific antenna ports.

There are no power control implemented on the non-RS REs, hence eNB sends either some RE with the aforementioned power, or not sending that RE at all.

Fig. 1. EPRE for OFDM symbol A and B for one antenna port case.

D. Cell Synchronization

In TD-LTE case, uplink and downlink signals are sent at the same frequency; hence, a strict time (frame) and phase synchronization are required to avoid the inter-symbol interference. In FDD LTE of 3GPP Rel.8 and Rel.9, the time synchronization is not required due to the frequency duplex separation for downlink and uplink frequencies. However it is noted that in Rel. 10 LTE and beyond, time and frequency synchronization are also required for the carrier aggregation (CA) and the coordinated multi-point transmission (CoMP) [7].

Cell synchronization becomes an important issue for RSRQ and SINR calculations since it will affect the type of interference generated by the neighbor cells. In synchronized network, as shown in Fig. 2 (left) the interference or RSSI component from the neighbor cells consists of both RS RE and non-RS RE, however for non-synchronized network as shown in Fig. 2 (right), there is a probability, prA that the interference only consist of Non-RS RE component. In case of normal cyclic prefix, both RS elements and non-RS elements are transmitted at the same time in 2 symbols out of 7 symbols in a slot [6]. It means that there are 5 symbols transmitting only non-RS elements in a slot, which result in prA = 5/7.

RS RE is always broadcasted in the specific locations for the whole LTE band. The load on that specific RS RE will always be 100%. However, the non-RS RE interference varies depending on the traffic at the eNB. Moreover, in the synchronized network, there is 1/6 probability that the RS RE of the different cells are broadcasted in the same locations as shown in Fig. 3 (right); in this case SINR is dependent only on the overlapping level of different cell’s RS signal, not on the interfering cell load. It is noted that the cells with the same RS RE locations can be detected from their PCIs.

Fig. 2. Reception time difference in synchronized and non-synchronized networks in single antenna port case.

TABLE II. CELL SPECIFIC RATIO FOR 1, 2 OR 4 ANTENNA PORTS.

P

B

ρB/ρA

One antenna port Two or four antenna ports

0 1 5/4

1 4/5 1

2 3/5 3/4

3 2/5 1/2

Fig. 3. RS and RE locations in synchronized network.

E. Non-reference signal received power

The RS signal average received power per RE is defined by 3GPP as RSRP [4]. In the case of flat fading, RSRP can be used to estimate the average received power per RE for resource element that is used for other than RS signal. The non-RS RE is transmitted using two different powers, EPRE_A or EPRE_B depending on its location in time domain

hence, the NRSRP can be defined in two different values accordingly. In case RS is not present within the same OFDM symbol index, NRSRP is expressed as follow in Eq. (1).

AA RSRPNRSRP ρ×= (1)

If RS is present within the same OFDM symbol index, NRSRP is defined as shown in Eq. (2).

BB RSRPNRSRP ρ×= (2)

It is noted that the parameter ρA and ρB are configurable by the network operator; however, the values are highly dependent of the number of transmit antenna ports and the precoding matrix used because the transmission power per layer is reduced as the number of transmission layer increases. As a rule of thumb, network vendors and operators use the default value of ρA and ρB to be reciprocal to the number of transmission layer. For example, in the case of single layer transmission, the default value of ρA and ρB is 1, while for two layer transmission the default value of ρA and ρB is 1/2.

III. CALCULATION OF OWN CELL LOAD

Fig. 4 shows the components constructing SINR such as the signals from the own cell, interference from the neighboring cells, and the thermal noise power. The interference generated from the own cell is ‘0’ because the sub-channels in OFDMA system are orthogonal [8]. The interference from the neighboring cells can come from RS elements or from non-RS elements. By excluding the interference coming from the RS elements, it is possible to estimate the interference coming only from neighboring cells’ loads, which is denoted by LN. In other words, LN is subtracted from the total interference, RSSI to obtain the own cell’s load denoted by LO.

Fig. 4. Instantaneous RSSI component at a single measurement sample for one PRB SISO case in synchronized network.

IV. OWN CELL LOAD IN NON-SYNCHRONIZED NETWORK

A. Signal and interference components

In a non-synchronized network there are three components constructing SINR as shown in Fig. 6. The own cell signal is always calculated at the RS elements as its received signal power is RSRP however, for a neighbor cell’s interference, there are three cases determining its interference power.

Fig. 5. SINR component at a single measurement sample for one PRB SISO case in synchronized network.

1) Case A The neighbor cells’ interference can come from the RE

comprising of non-RS elements of which OFDM symbol index is different from own cell’s symbol index for RS. It happens with the possibility, prA of 71.4%. The average power of the received interference is expressed as Eq. (3).

NANANRSRP LiRSRPnLiNRSRPniIA

⋅⋅⋅=⋅⋅= ρ)()()( (3)

where, i is the cell index. When i=0, it stands for the own cell and when i ≥1, it stands for the neighboring interfering cells. n is the number of antenna ports, e.g. 1, 2, or 4.

2) Case B.1 The possibility, prB is for the case when the neighbor cell

interference contributing to the RSSI comes from the REs located in the same OFDM index as own cell’s and its value is 28.6%. In addition, the neighbor cell interference in this scenario is attributed to the RS elements of the neighbor cells. The received power of this interference is denoted by RSRP(i) for the i-th neighbor cell. In other words, this is the case when the neighbor cells have similar RS locations with the serving cell’s RS in time and frequency domains. It can be also said that when the number of antenna port is one, the own cell’s PCI mod 6 is equal to the neighbor cell’s PCI mod 6. If the neighbor cell has different RS locations than the own cell does, the next case mentioned below needs to be taken into account.

3) Case B.2 In this scenario, the neighbor cell interference

contributing to the RSSI is caused by non-RS RE of which OFDM index is same as that of own cell’s for RS however, PCI mode 6 is different from own cell’s PCI mod 6 with the condition that the number of antenna port is one. The average received power of this non-RS element is expressed as Eq. (4).

NBNBNRSRP LiRSRPnLiNRSRPniIB

⋅⋅⋅=⋅⋅= ρ)()()( (4)

Where n is the number of antenna ports, e.g. 1, 2, or 4.

B. Thermal noise component

Noise component from the thermal noise power depends on measurement bandwidth denoted by BW. Since it is considered the average value per LTE sub-carrier in the SINR calculation, the BW is fixed to 15 kHz.

dBmNFfTkN Bo 24.125−=+Δ⋅⋅= (5)

The noise figure denoted by NF is in the range of 7dB.

Fig. 6. SINR components in case of non-synchronized network.

C. Average SINR and average neighbor cell load

Since there are no accurate way to find out whether case A or case B occurred at an instantaneous time. The best method to get the accurate result is by using probability. In this paper, for the interference component calculation, each probability is used as a weighting factor for average interference, I calculation.

≠

++=+==+0

)2()1()(i

oo NiIiINiI (6)

In case the number of antenna port is one and own cell’s PCI mod 6 is equal to neighbor cell’s PCI mod 6, the average interference is re-written as below. If the number of antenna port is 2, the PCI mod 6 needs to be replaced with PCI mod 3.

( ) )()6mod( iRSRPprLnprPCIiI BNAA ⋅+⋅⋅⋅=∈ ρ (7)

Else ( ) NBBAA LiRSRPnprpriI ⋅⋅⋅⋅+⋅= )()( ρρ (8)

Then, the SINR can be expressed as shown in Eq. (9).

≠+

=0

)(i oNiI

SSINR

( )

⋅+⋅⋅⋅== ∈ 6mod

)()0(PCIi

BNAA iRSRPprLnpriRSRP ρ

( )

+⋅⋅⋅⋅+⋅+ ∉ 6mod

)(PCIi

oNBBAA NLiRSRPnprpr ρρ (9)

The detailed procedure of SINR calculation is articulated in other study [9].

It is noted that other than the neighbor cell load, LN, all above variable are either known from LTE RF scanner data, or dependent only on the number of antenna ports. Therefore, by knowing the antenna configuration, the neighbor cell load, LN can be derived as Eq. (10).

⋅−−===

∈ 6mod

)()0(

)0(

PCIiBoN iRSRPprN

iSINR

iRSRPL

( ){ }

⋅⋅⋅+⋅+⋅⋅⋅ ∉∈ 6mod6mod

)()(PCIi

BBAAPCIi

AA iRSRPnprpriRSRPnpr ρρρ

(10) The own cell load can be obtained by subtracting LN

from RSSI as shown in Eq. (11). The detailed calculation is articulated in other study [9].

( ){ )0()(][ =⋅⋅−−= iRSRPNnRSRSSIRSSIL RENoiseO

=

⋅⋅⋅⋅⋅−Ncells

iARENA iRSRPNnNRSLpr

A1

)()( ρ

( ) })()()( iRSRPNnNRSLnRSpr BRENREB B⋅⋅⋅⋅+⋅+ ρ

{ })0()( =⋅⋅⋅ iRSRPNnNRS BREBρ (11)

It is noted that the neighboring cell load may not be derived with very small odd. It may happen in a poorly planned network where the serving cell and the neighboring cells have the same RS-elements location in both time and frequency domain. In this case, the approach introduced in this section is no longer valid and LN shall be assumed to be similar to LO.

V. OWN CELL LOAD IN SYNCHRONIZED NETWORK

A similar approach can be used to calculate the own cell load of the synchronized network as in the non-synchronized network. The difference is only the probability, prA and prB values. The SINR calculation in the synchronized network becomes simpler because prA is 0% and prB is 100%. With the updated prA and prB values, the neighboring cell load in the synchronized network is expressed as Eq. (12).

∉

∈

⋅⋅

−−==

=

6mod

6mod

)(

)()0(

)0(

PCIiB

PCIio

N iRSRPN

iRSRPNiSINR

iRSRP

Lρ

(12)

The own cell load can be obtained by subtracting LN from RSSI as shown in Eq. (13) [9].

( ){ )0()(][ =⋅⋅−−= iRSRPNnRSRSSIRSSIL RENoiseO

( ) }=

⋅⋅⋅⋅+−Ncells

iBRENRE iRSRPNnNRSLnRS

B1

)()()( ρ

{ })0()( =⋅⋅⋅ iRSRPNnNRS BREBρ (13)

VI. VERIFICATION OF THE OWN CELL LOAD CALCULATION

IN NON-SYNCHRONIZED NETWORK

In order to verify the accuracy of the load estimation, the measurement quantities - RSRP, RSRQ and SINR of a serving cell, the cell with the best RSRP level are collected using RF scanner in stationary over 2 ~ 5 hours and the serving cell’s load is estimated using the proposed algorithm. The actual cell load of the serving cell is also obtained from the eNB KPI (Key Performance Indicator) for the same time period when the RF scanner measurements are carried out. The proposed algorithm is verified in the Softbank’s FDD LTE network where MDMS service and eICIC feature is not enabled so that the network is considered non-synchronized network. The verification of cell load estimation in the synchronized LTE network was not made because the cell load information in eNB side could not be obtained due to the mobile operators’ information security policy. The authors appreciate Softbank mobile for providing the cell load information in eNB for the verification purpose.

The verification of the own cell load estimation was performed throughout the four different train stations in Tokyo metropolitan area. The station names are not disclosed for the

reason of information security. The traffic conditions and the estimation deviations for the four different stations are presented in Table 3.

TABLE III. CELL SPECIFIC RATIO FOR 1, 2 OR 4 ANTENNA PORTS.

Test area ID Traffic Level Load by test

UE at the neighbor cell

Estimation deviation

Station A High No 2.7% Station B High No 3.3% Station C Medium No 4.4% Station D Low Yes 2.2%

The column of traffic level in Table 3 represents the

traffic load levels at each station during the RF scanner measurements. The column of estimation deviation stands for the accuracy of the serving cell load estimation at the four stations, which is the average of the discrepancies of the estimated cell load from the actual cell load obtained from the eNB KPI over the measurement period. The detailed comparison between the estimated cell loads and the actual cell loads in the four stations are presented in the Fig. 7 to Fig.

10.

Fig. 7. Estimated serving cell load and actual cell load at station A.

The eNB cell load KPI is updated in every 15 minutes,

which is the averaged cell load over last 15 minutes. Meanwhile, the RF scanner collects RSRP, RSRQ and SINR in every second so that the serving cell load is estimated in every second too. The estimated serving cell load is then averaged over 15 minutes in order to compare to eNB’s cell load KPI.

In the verification tests at station A and station B, a test UE was used to impose additional traffic load onto the serving cells. It is noted in Fig. 7 and Fig. 8 that the time stamp with the vertical dotted line is the moment when the test UE started FTP downloading to generate the additional traffic load at the serving cell. The test UE load is the ratio of the number of PRBs allocated to the test UE to the total number of system PRBs, e.g. 50 PRBs in 10MHz LTE system. It is found in Fig. 7 and Fig. 8 that the proposed algorithm estimates the serving cell loads with a negligible deviation over the whole verification period. The accuracy of the load estimation is well maintained even when the test UE generated the additional traffic load

onto the serving cell.

Fig. 8. Estimated serving cell load and actual cell load at station B.

Fig. 9. Estimated serving cell load and actual cell load at station C.

Fig. 10. Estimated serving cell load and actual cell load at station D.

Fig. 9 shows the estimated serving cell load and the actual

serving cell load obtained from eNB KPI for the station C where the traffic load is observed medium level during the verification test. This verification test is for the most usual traffic load scenario that the most mobile networks operate. The estimated serving cell load for this scenario is also well in line with the actual cell load over the whole measurement period.

The last scenario, station D is special that the test UE is connected to the neighbor cell rather than the serving cell and is downloading the properly sized files through the FTP sessions to create the additional traffic load onto the neighbor

cell. Then the neighboring cell, geographically close to the serving cell introduces higher interference to the serving cell because more PRBs are transmitted due to the test UE’s FTP downloading at the neighboring cell.

It is observed in Fig. 10 that the proposed algorithm estimates the serving cell’s load accurately and not negatively affected by the sudden increase of neighbor cell’s interference due to the test UE’s FTP downloading. This scenario proves that the proposed algorithm differentiates the RSSI contributed by the neighbor cell and excludes it correctly in order to calculate the net transmit power of the serving cell, which is the key to estimate the serving cell load.

VII. DOWNLINK THROUGHPUT ESTIMATION FOR LTE

NETWORK

The LTE downlink throughput at a UE is determined by three factors, TTI allocations to the UE in time domain, PRB allocations per TTI to the UE, and MCS of the allocated PRB [1]-[3]. If the RF condition in terms of the received SINR at the UE is favorable, higher order MCS such as 64QAM can be selected by eNB to the allocated PRB to the UE. The PRB and TTI allocations to the UE depend on the traffic load of the eNB serving the UEs within its coverage area. In case the traffic load at the eNB is too high, the sufficient downlink throughput is not achievable even if the received SINR at the UE is good enough.

In 3GPP [2], it is specified how large TBS is selected by eNB in accordance with the MCS index and the number of allocated PRBs to the UE. The downlink throughput is then calculated from the TBS and the number of MIMO streams as expressed in the Eq. (14).

( )ms

streamsofNumTBSThroughput sizeDL 1

sec1××= (14)

The MCS index determined by eNB is selected based on the CQI reported by UE, which indicates the quality of the received SINR at the UE. It can be also said that downlink throughput can be re-expressed by the allocated PRBs to the UE and the received SINR at the UE since the SINR is proportional to the reported CQI. There are studies done using the link level simulations to obtain the curves of downlink throughput for the received SINR with the variance of the number of PRBs allocated to the UE [10],[11]. In this paper, instead of using the link level simulation, UE’s measurement data in the live LTE network is used to obtain the curves of downlink throughput upon the received SINR and the number of PRBs allocated to the test UE. The UE’s measurement data is collected using a diagnostic monitoring tool for Qualcomm chipset UE [12].

In order to obtain the downlink throughput curves proportional to the received SINR, it is configured that the test UE downloads a huge data file, e.g. 1GBytes through FTP session continually to ensure the RLC buffer at the eNB is always filled with user data provisioned by the FTP server. While the test UE is downloading the data from the serving cell, the UE moves around to be exposed to the different RF coverage conditions so that the serving cells with different

traffic loads will schedule MIMO and MCS dynamically according to the reported CQI by UE. The UE’s data collection was performed in a dense urban area of Tokyo and all the eNBs equipped with 2X2 MIMO and 10MHz LTE system bandwidth.

The allocated MCSid and MIMO ratio curves shown in Fig.11 and the downlink throughput curves shown in Fig. 11 may differ in accordance with the system vendor of the LTE network because the packet scheduler of a LTE vendor is different from others [1],[3]. However, the pattern of the curve stays the same even if the network topology is different, e.g. rural area or urban area as long as the LTE system vendor and its system configurations are remained same; MIMO and scheduler metric such as proportional fair or round robin. The number of available PRBs is estimated using the algorithm developed in the previous section.

Fig. 11. Average MCSid and MIMO ratio allocated to UE at different SINR level using UE measurement data.

0

5

10

15

20

25

30

35

40

45

50

-6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

SINR vs DL Throughput

50 PRB 40 PRB 30 PRB 20 PRB 10 PRB

5 PRB 15 PRB 25 PRB 35 PRB 45 PRB

SINR (dB)

DL

Th

rou

ghp

ut

(Mb

ps)

Fig. 12. DL throughput upon received SINR for the various PRB allocations using UE measurement data.

VIII. VERIFICATION OF DOWNLINK THROUGHPUT ESTIMATION

This section compares the estimated downlink throughput based on the received SINR and the estimated PRB load of the non-synchronized network serving cell using the RF scanner’s measurement data to the actual UE’s downlink throughput by downloading a file via FTP. The estimation algorithm of the downlink throughput at a UE assumes all the remaining PRB resources of the serving cell are fully allocated to the UE. Therefore, in order to verify the accuracy of the estimated downlink throughput fairly to the test UE’s actual

downlink throughput, a large file size, e.g. 1GBytes, needs to be used for FTP downloading at the test UE to ensure that the serving cell allocates most of its remaining PRB resources to the test UE so that eNB’s RLC buffer is filled enough with the user data provisioned by the FTP server. It is noted, however, that if the test UE downloads the big sized data file continually, the RSRQ received at the UE will be significantly deteriorated because the RSSI of the serving cell will increase by the REs transmitting the FTP downloading data to the test UE. This will result in a situation that the LTE load estimation algorithm shows almost ‘0’ availability of the PRB resource since most of the PRB resources are occupied by the test UE, which is downloading the big sized data file via FTP. As a result, the estimated downlink throughput will become nearly 0kbps, which is not absolutely correct way to verify the algorithm.

Therefore, it is necessary to compensate the additional deterioration of RSRQ level by the test UE’s FTP downloading for the correct verification of downlink throughput estimation. While the UE is downloading the data file, the remaining available PRBs of the serving cell are allocated to the UE and those allocated PRBs cause the increase in RSSI and the deterioration in RSRQ. Since the number of allocated PRBs to the test UE is known via the diagnostic monitoring tool of the UE, it is possible to calculate the RSRQ in the absence of the UE’s FTP downloading operation.

UEUEScannerScanner RSSIRSSIRSSI −= + (15)

Where, RSSIScanner is the estimated RSSI emulating the measurement condition where only RF scanner is used without UE’s FTP downloading. RSSIScanner+UE is the RSSI when both RF scanner and UE’s FTP download are performed simultaneously so that total RSSI shall be increased by the received power of PRBs allocated to the test UE, which is denoted by RSSIUE.

REUEBUE PDSCHPRBRSRPRSSI ⋅⋅⋅= ρ (16)

Where, RSRP is the value measured by RF scanner. PRBUE is the number of PRBs allocated to the test UE when RSRP and RSSIUE are measured. PDSCHRE is the number of resource elements belonging to PDSCH, which depends on the number of Tx antenna ports. For example, if 2X2 MIMO is used, PDSCHRE becomes 16. With the re-calculated RSSIScanner, the compensated RSRQ denoted by RSRQScanner is expressed as Eq. (17).

ScannerScanner RSSI

RSRPNRSRQ ⋅= (17)

Where, N is the number of PRBs existing in the LTE system bandwidth, i.e. 50 for 10MHz LTE bandwidth.

The verification of the estimated downlink throughput is performed along a train line in Nagoya by measuring the RF scanner and UE’s FTP downloading at the same time. The accuracy of downlink throughput estimation is greatly influenced by the accuracy of PRB load estimation. The accuracy of PRB load estimation is well verified in section 6 throughout the various traffic load conditions – high traffic, medium traffic and low traffic so that the accuracy of

downlink throughput estimation can be proved by any specific traffic load condition. In this paper, the verification is made in high traffic condition which is between 8pm and 9pm. The FDD LTE network covers the train line is 10MHz system bandwidth and 2X2 MIMO for the antenna configuration. The test UE downloads 1GBytes data continually via FTP while the RF scanner is collecting RSRP, RSRQ and SINR in every second. The estimated number of available PRBs is compensated by the number of PRBs allocated to the test UE and the measured SINR by RF scanner are utilized to predict the downlink throughput at each measurement location. The estimated downlink throughput and the UE’s FTP throughput are displayed on the map together as shown in Fig.

13. The plot of UE’s FTP throughput is drawn in shift on the right of the estimated downlink throughput for convenient comparison purpose.

It is also plotted that the estimated downlink throughput and UE’s FTP throughput in time domain graph as shown in Fig. 14. The red dots and the blue dots stand for the instantaneous throughput values of the estimated downlink throughput and UE’s FTP throughput, respectively. The pink solid line and the green solid line are the fitted lines averaged over the three instantaneous throughput samples of the estimated downlink throughput and UE’s FTP throughput to mitigate the sudden fluctuations.

UE’s FTP Throughput

Estimated Throughput

Fig. 13. Estimated downlink throughput compared to UE’s throughput via FTP downloading.

It is noted that the estimated downlink throughput is well predicted in most of the samples and proves the proposed algorithm presents the LTE network’s throughput reliably without the usage of UE.

Fig. 15 shows that deviation of accuracy between the estimated throughput and the UE’s actual throughput is less than 10%.

Fig. 14. Comparison of estimated throughput to UE’s throughput in time domain.

In fact, the estimated throughput is supposed to be higher than UE’s actual throughput as the estimated throughput is calculated assuming that the remained PRB resources left over from the use PRBs by common channels and PDSCH for other UE’s traffic data are fully utilized. However, the test UE may not be fully allocated for the remaining PRBs by the eNB. From this perspective, the estimated throughput is regarded as the potential throughput that an LTE network can perform at best.

Fig. 15. Comparison of PDF and CDF curves between the estimated throughput and UE’s throughput.

IX. CONCLUSION

This paper showed that LTE network load and downlink throughput can be estimated using the RF scanner measurement data without performing UE’s FTP downloading tests. It is observed that the estimated network load and the downlink throughput based on RF scanner data are reliably accurate when compared to eNB’s load KPI and UE’s FTP throughput, respectively.

With the proposed algorithm, it is envisaged that the throughput of a LTE network can be measured with much lower complexity of test configuration by removing the usage of UEs. As a future study, the algorithm presented in this paper can be used to identify the root causes of unsatisfactory throughput performance whether they are from poor RSRP, high interference from the neighboring cells or high traffic load.

ACKNOWLEDGMENT

The authors would like to show gratitude to the mobile network planning division of Softbank co. for their support for providing the actual network load information for the verification tests. Thanks to their help, this paper could become a meaningful work.

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