Unsupervised Root Cause Analysis of LTE Networks

1. Why An Unsupervised Root Cause Analysis of LTE Networks?

Providing a good service to customer is the utmost priority of a mobile operator, on which their revenue is dependent on. Wireless networks today have grown in large size which makes management tasks difficult.  To troubleshoot and recovery actions self healing (SH) architectures have been employed. Such SH uses cell level indicators (i.e. data collected by OAM task). The main barrier of this technique is that cell-level indicators represent statistical data, which do not suggest user level behavior and thus prevent the technician from being able to recognize the certain user-level performance resulting in unintended service disconnections, which is the potent reason users usually complain and periodically, lead them to change service provider.

Such user-level analyses have been performed by means of manual move tests. However, this is a time-consuming duty. As a result, experts have to personally go alongside a predefined course of action in order to manually make the user-level measurements. Thus, the hit or miss focuses specially on those areas sampled neglecting the rest of area. Furthermore, manual move test does not support operators to analyze the rule of the connection of each specific user. Given this condition, the Third Generation Partnership Project (3GPP) has standardized the automatic aggregation of user-level information by means of mobile traces and the minimization of drive test (MDT) [22]. In contrasting, traces and MDT manage cases are especially focused on troubleshooting and on analyzing the LTE radio access network (RAN) performance of a particular cell [23]. More especially, they had the means for experts to search the radio coverage, try new features, and conform or optimize their network parameters. furthermore include the analysis of user gadget when it does not operate correctly or the identification of network-wide issues that cause a consistent subscriber suffer some service degradation [24].

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However, MDT solo automates trace collection, whereas posterior experiment of the traces for troubleshooting purposes is further a manual process. As a result, troubleshooting experts have to invest a lot of time in analyzing the mobile traces in verdict to seize the problems that each user has suffered over its connection. A thorough analysis go for user-level data would allow operators to look the cause for the release, which make out be coverage problem, unavailable cell, problems in authentication etc. We assume that the technicians collect the user level data and depending on this sampled data we analyze the problems. However we only consider certain samples and neglect the rest of the area. In certain cases, it is necessary to assume probability of a cause so all these systems come under supervisory actions,

Regarding the literature on SH, recent studies show the use of MDT to detect abnormally behaving cells in general [8] and to detect outage cells in particular [9]. Even though the analysis of outage is the most important problem, due to the great impact that it has on the network, there are more types of problems that are worth analyzing. Thus, unlike previous references, this article presents a method to not only detect abnormal behaviors, but also automatically identify a significant group of RF issues, defining the specific cause-indicator relation required to properly automate the root cause analysis. In this context, this article proposes a unified framework to diagnose the reasons for each user disconnection. This allows automatic discernment between problematic and non-problematic connections, and then focuses attention on the particular connection’s stage where the issue lies. Normally, when an undesirable release is caused by problems related to either software or protocols, the events are sufficiently explicit and clear on the cause. However, when there is a radio link failure and thus the release happens due to bad RF conditions, further diagnosis is required. Therefore, the proposed root cause analysis provides a technique to automatically identify the RF problem on the basis of the key indicators reported by the user just moments before the release happened. Then the proposed methodology aims to fully automate the diagnosis of those radio link failures, determining their reason, which is not reported by the RLF event [25].

Furthermore, this approach goes one step further than the RLF event use cases, identifying more variety of RF issues and automating the whole diagnosis process. Automatic root cause analysis is a part of self healing networks which focuses on identification of problems. In a mobile network when a particular call is disconnected abnormally, the service provider is unaware of the cause of disconnections. Providing a good service to users is the utmost priority of service provider on which their revenue is dependent on. Telecom Regulatory Authority Of India has carried out an audit between 3 to 5 May 2016, the result revealed that BSNL’s call drop rate reaches to 22.17% in Bhopal [18], such call disconnection result in poor service and thus affecting service providers revenue.

Thus we require an unsupervised method which can detect actual cause of problems without collecting such data manually with minimum or no user’s interaction. The overall objectives of our project would be:

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• To provide unsupervised technique to determine root cause of call disconnections.
• To offer result of automatic root cause analysis of call drops for entire network as well as individual user.
• To find the exact faulty region where the call has been dropped.
• To make self healing networks more accurate.
• To meet the benchmark of TRAI.

CHAPTER 5

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LTE Traces and Events

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2. LTE Traces and Events

The LTE trace function is used to store radio measurements and signaling messages (i.e., events) in order to reflect user-level performance. Therefore, traces do not aggregate any information but instead record the user-specific values of each event (signal quality, serving cell name, etc.). An event is a report consisting of
data related to current system usage and documents all pertinent data regarding the process in question, such as status information (failure or success), a rough idea of the cause of a specific failure (e.g., cell unavailability), the instant when it happened, and even specific measurements and performance information (e.g., signal level, bit rate, timing advance). Those events can be grouped into two categories according to its
origin [Fig. 13].

Figure.13 Event Flow of Connections

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Internal Events: These events represent the performance of the base station (eNB) in the LTE system, so they are specific to each eNB’s vendor. Overall, internal events contain information related to all processes that take place at the cell or UE level and user measurements.

External Events: These events are external to the eNB and consist of the signaling messages that the eNB exchanges with the rest of the network equipment. Namely, external events are radio resource control (RRC) messages received from the UE through the LTE-Uu interface and protocol messages that the eNB exchanges [fig.14], other eNBs through the X2 interface, or the mobility management entity (MME) through the S1 interface. As a consequence, external events can be divided into three categories depending on the type of message they report.

Figure. 14  LTE Interface

Among all types of events, only those chosen and activated by the operator are registered in a trace file. Operators need to find a balance between the amount of events they need to store in order to ensure accurate analysis and the cost of processing them. Note that the greater the number of activated events and traced cells, the greater the processing time. In particular, at each report period (e.g., 15 min), the eNB provides a file containing all received events [Fig.13]. Within these traces, two types can be distinguished depending on the scope of application

Cell Traffic Trace: Used to record all LTE activated events of all users (or a fraction of them) in the desired cells. Thus, they provide enough information to identify the existing problems in those cells and to analyze in detail the quality of the radio environment.

UE Trace: Only stores the events and the measurements of a specific user selected by the operator. Therefore, with this functionality, network operators can choose the user to be traced using the UE’s international mobile subscriber identity (IMSI) or international mobile equipment identity (IMEI). Given that the scope of this study is to analyze the termination of each user, both types of traces may be useful

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5.1 RootCause Analysis at the UE Level 

To identify the reason for a release and to perform a detailed diagnosis when that release is undesired, the end of the user’s connection should be analyzed. All events belonging to the same connection are aggregated and temporarily stored, building its event flow [Fig. 13]. In general, the event flow can be described in three different steps: first, the connection is established; second, the requested service is maintained and provisioned; and finally, the connection is released. In each of these stages, different protocols (e.g., RRC) and network equipment (e.g., MME) are involved; thus, identifying the phase in which the release occurs provides valuable information on what has happened.

Based on the event flow, UE releases can be grouped into three categories.

Normal Release: A normal release encompasses all releases that happen when the LTE service offered to the user has been completed. The event flow of a UE whose connection ends successfully has a Context Release event indicating that the release has been normal. There are different situations where the finalization of the LTE service is considered satisfactory. On one hand, a normal release occurs when no data is transmitted between the LTE network and the end user because either the user has been inactive during a long period (typically more than 10s) or its session has finished. On the other hand, a normal release can also be due to LTE deployment constraints. For instance, LTE networks that do not support voice call yet have to redirect users who request a voice call to one of the existing 2G/3G networks through the technology known as CS fallback.

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Access Failure: It takes place in the connection setup phase, which implies that the user cannot obtain the requested service, so its event flow ends with an Initial Context Setup event with information about the cause of the failure. An access failure can occur due to several causes, including overload, no radio resources being available, no cell being available, authentication failure, and so on.

Dropped Connection : These are the abnormal releases that have negative impact on users because they occur while the requested service is in progress, so there are still buffered data to be transmitted at the time of the release. This kind of release can occur due to hardware errors, breakdown of the interfaces, and failures in any of the processes of the mobile network (e.g., handovers). All these releases can be diagnosed
based on the values of the Context Release event [26,27]. For instance, the connection can be released because of lack of available resources in the target cell during a handover or because a particular timer has expired. However, when the connection drops due to the quality of the air interface, the release cause in the Context Release event does not provide such detailed information. In this situation, the cause indicated by the Context Release event could be something generic, for example, Eutran Generated Reason or Radio Connection with UE Lost, and it may be accompanied by a Radio Link Failure event [25]. Thus, in order to explain the RF cause of these releases (coverage hole, LTE interference, etc.), further analysis based on user measurements needs to be performed. 

5.2 Methods To Automate The Diagnosis 

The automatic diagnosis of the radio cause can be addressed by using any classification system available in the literature. The difference is that instead of applying the classifier to the statistics from the OAM, as proposed in the literature, the inputs to the classifier will be the mobile traces. In particular, the aim of this diagnosis system is to identify the radio cause for each drop based on a set of standard indicators and measurements provided by the event flow. There are several diagnosis systems available in the literature. On one hand, a rule-based system is the simplest technique to automatically identify the radio cause and it does not require big computational capacity.

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A more advanced automatic diagnosis system based on a probabilistic method such as Bayesian networks is proposed in for SH in mobile networks [13]. Both systems require the design of thresholds to analyze the input data. There are different ways of doing that, such as the percentile-based discretization (PBD) method proposed in [5]. This method consists on designing the threshold of each key performance indicator (KPI) based on their Xth percentile defined by the experts. That is, each threshold will take the Xth percentile of the KPI values in the training dataset. For instance, if the expert chooses percentage 90 for the reference signal received quality (RSRQ), its associated threshold is set to the 90th percentile of all the values of RSRQ in the dataset. On the other hand, the root cause analysis of radio problems can also be automated using either the ranking method [11] or a neural network such as the self-organizing map, which do not require any threshold to analyze the input data. Unlike the rule-based system, these methods are more complex and require more computational cost, but if they are properly designed on the basis of a good set of real samples or solved cases, the diagnosis success rate will be improved. Thus, among the available methods, the operator should select the most appropriate technique for its priorities, requirements, and available resources.

5.3 Indicators and Measurements 

The proposed indicators are those that provide enough information to analyze the RF conditions of each user when its connection is lost. This information is taken from the required events that have happened just before the end of the event flow. For the proposed framework, only two events are required: the Measurement Report, which provides the reference signal received power (RSRP) and the reference signal received quality (RSRQ) of all the cells measured by the user; and the Measurement timing advance (TA) [28], which determines the value of the timing advance (TA), which represents the transmission delay in the downlink and uplink path between the user and its serving cell. Both the user measurement and its location can also be obtained from the Radio Link Failure event (RLF) [25]. Note that this event is only available when the user has experienced an RLF, so if the values reported by normal users are required in order to compare the values or design thresholds, they should be obtained from the Measurement Report and Measurement TA events. Furthermore, it is worth mentioning that the availability and accuracy of the location information depend on the used location estimation method and LTE release. For example, a precise location can be obtained when the Global Positioning System (GPS) system is available; otherwise, the position can be estimated from the TA. The proposed indicators are:

Serving RSRP: It is the signal strength that the UE measures from the serving cell when its connection finishes. A decrease of the RSRP can be caused by either an increase of the distance or obstacles on the path. Therefore, to distinguish between those situations, this indicator should be complemented with information about the separation between the UE and the site [29].

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Serving RSRQ: The quality of the received reference signal from the serving cell is crucial to understanding the cause of the release. RSRQ is defined in [29] as the ratio of total reference power to the total received power within the measurement bandwidth [Eq.2].

RSRQ=

RSRPRSSI* N        eq-(2)

Where, N  is number of resource blocks over which Reference Signal Strength Indicator  (RSSI)  is measured and  RSSI is total received wide band power by UE. It includes power from serving cell as well as co-channel interference & noise. It helps to determine interference & noise. Here note that when the noise and interference is added, the RSSI will increase which reduces RSRQ.

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Strongest non-serving RSRP: The RSRP of the strongest cell (other than the serving one) is also important.It represents the best non-serving cell measured by the UE, thus providing additional information regarding the global LTE coverage. As the distance between UE and its client increases, the value of strongest non-serving RSRP will reduce.

Number of detected cells: The number of non-serving cells that the UE has measured at the time of the release indicates the number of cells that might cause interference to the user’s connection. Thus, this indicator allows areas with large cell overlaps to be distinguished [30]. This parameter is measured by UE which is also helpful while performing handover.

Relative TA: The radio cause also depends on the location where the release has taken place. Therefore, the TA is used to determine the distance between the UE and its serving cell. However, depending on the antenna configuration, height, and location, the service area will be different. This implies that users with the same TA but belonging to different cells could be considered to be near or far according to the service area. This means that the relative TA of the UE with respect to the service area of its cell should be considered. This requires identifying the real distance within which the majority of the users are served by a particular cell, so two regions can be differentiated: the cell center, where the UEs are considered to be near the cell, and the cell edge, where the farthest users are located. The border between those regions can be estimated as the Xth percentile (e.g., 98th) of all TA values reported in that cell. As a result, the relative TA is the ratio of the user TA to the estimated Xth percentile.

5.4 Measurements of RSRP, RSSI ,RSRQ And Timing Advance 

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Reference signal is used to estimate the channel impulse response (CIR), in other words for coherent demodulation synchronization is necessary.  This problem can be solved by sending a known signal. Thus by using reference signal channel impulse response can be obtained. Note that reference signal is transmitted every 6 subcarriers [Fig. 14].

Figure. 14  Reference Signal In Resource Blocks

Lets try to calculate RSRP, RSSI and RSRQ for one very simple case of one resource block with 12 sub carriers and 0.5 ms in time domain. For sake of simplicity, lets assume the power of reference symbols  (shown by pink square) and power of other symbols carrying other data channels (shown by white square) is same i.e. 0.021 watt

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Since RSRP is linear average of downlink reference signal for given channel bandwidth therefore RSRP is given by eq.3

RSRP = 10*log (0.021*1000) = 13.2 dBm     eq-(3)

While RSSI is total received wide-band power. Therefore we have to add power of all 12 carriers in the given resource block [eq.4]
RSSI = 10*log(0.021*1000)+10*log(12) = 24 dBm    eq-(4)

RSRQ is now simple ratio of RSRP to RSSI with N=1 [eq.5]
RSRQ = 10*log(0.021/(12*0.021)) = -10.79 dB    eq-(5)

When interference from neighboring cell increases the total received power i.e. RSSI increases. Note that as the total received power (RSSI) increases, the received signal quality degrades.  

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Timing Advance (TA):

TA is used in LTE for following reasons:

• Used to control uplink timing of individual UE.
• Ensures that transmission from all UE are synchronized when received by eNB.
• UE furthest from eNB requires a large timing advance to compensate for larger propagation delay.

The basic time unit is Ts which represents the sampling Time given by Eq.6:

Ts =

1(15Khz*2048)=  32.55 ns      eq-(6)

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Where 15 Khz is frequency spacing and 2048 is IDFT size.

The timing advance command indicates the change of uplink timing which is multiple of 16Ts

Timing advance of 1 corresponds to : 1* 16 * Ts =0.520 µs.

Thus distance can be calculated given by Eq.7:

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Distance =.

(3*108)  * 0.520 µs2= 78.12 m    eq-(7)

Timing advance of 300 corresponds to : 300* 16 * Ts =156.24 µs.

Thus distance can be calculated as:

Distance =.

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(3*108)  * 156.24 µs2= 23.43 Km

Timing advance of 900 corresponds to : 900* 16 * Ts =468.72 µs.

Thus distance can be calculated as:

Distance =.

(3*108)  * 468.72 µs2= 70.30 Km

Similarly Timing advance of 1282 corresponds to:  1282* 16 * Ts =667.66 µs

Thus distance can be calculated as:

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Distance =.

(3*108)  * 667.66 µs2= 100.1 Km

For our study, we require TA for timing delay only, i.e. how much the user is close to the current and strongest non-serving base station.

CHAPTER 6

6. Radio Causes 

When analyzing the RF conditions of the UE at the time of the release, different radio causes can be found related to both coverage and interference. The specific features of those radio causes along with the expected behavior of the indicators are detailed below and summarized in Table 2.