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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

An Optimized Neural Network for monitoring Key Performance Indicators in HSDPA Laura Pierucci° IEEE Member, Alessandra Romoli°, Romano Fantacci° IEEE Senior Member and Davide Micheli* °University of Florence, V.S.Marta,3 Firenze-ITALY Email: [email protected]

*Telecom Italia,V.di Val Cannuta Roma-ITALY Email: [email protected]

 Abstract— HSDPA (High Speed Downlink Packet Access) is drawing great attention as the 3.5G technology capable of providing higher data rate packet switch services over Universal Mobile Telecommunication System (UMTS) to support broadband services like multimedia conferencing, VoIP, or highspeed internet access. The paper proposes the use of a Learning Vector Quantization (LVQ) Neural Network able to estimate the quality of service (QoS) across analysis of Key Performance Indicators (KPIs) and to provide automatically a possible classification of warnings related to the load status of HSDPA radio resources or to the bad radio channel quality condition. Index Terms— Neural Networks, Key Performance Indicators, Channel Quality, HSDPA . I.

INTRODUCTION

Cellular communication has recently evolved towards data oriented channels with the steady increase of data throughput expected by users for a variety of cellular services including IP-based services such as data, mobile-TV and video streaming. In order to improve support for high data rate packet switched services, 3GPP has developed an evolution of UMTS based on WCDMA known as High Speed Downlink Packet Access (HSDPA) which was included in the Release 5 specifications [1]. HSDPA targets increased capacity, reduced round trip delay, and higher downlink (DL) data rates. In HSDPA, high-speed data packet transmission is possible by code and time-sharing of access users amount. It also relies on new technologies like adaptive modulation and coding (AMC), Hybrid automatic repeat request (H-, ARQ), Multiple Input and Multiple Output (MIMO) techniques, fast cell selection and fast packet scheduling. Several capabilities of HSDPA mobiles were proposed in the release 5. The peak throughput varies from 1.8 Mbps for mobiles category 12 to 14.4 Mbps for mobiles category 10. New mobile categories were recently introduced in release 7 in order to achieve higher throughputs. Such throughputs associated to each HSDPA mobile category are the peak ones and can only be achieved under optimal network conditions: very close to the base station, i.e., good radio channel conditions in terms of both, down-link and up-link low interference, good received signal strength, good fading conditions during off-hours. Authors are with the Department of Electronics and Telecommunications – University of Florence, via di S. Marta 3, I-50139 Italy (Tel.: (+39) 055 4796271, Fax: (+39) 055 494569 Email {laura.pierucci}@unifi.it).

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The objective metrics used to determine the degree of user satisfaction are typically packet loss, delay, jitter and throughput. In the paper the radio throughput for each user is considered in order to monitor the performance of HSDPA network. Several performance indices are available and can be used to determine the status of cell in terms of average throughput provided to the single user and they are composed starting from counters located on network equipments. Such counters are named RNC counters or BTS counters and are related respectively to the radio network controller element or to the Node-b element. In both cases, all the measured events are referred to radio access network (RAN) and as a consequence the throughput measured by the counters is at the moment a “radio interface throughput” not a TCP-IP throughput. Given a fixed time interval for the analysis, called Repetition Of Period (ROP), counters are able to measure the number of times that a certain event occurs, such as the number of handovers properly carried out, the number of allocations success for a particular transmission channel or the number of failure events as an example dropped-calls, the rate of accessibility to a particular services, type of modulation, signal strength, signal quality and so on. Each counter, usually, determines the amount of occurrences related to a single event, therefore they must be analyzed and grouped together in order to build an useful Key Performance Indicators (KPI). As an example if one is interested to monitor dropped calls he must take into account several possible causes of failure such as radio interface, backbone, base station hardware, codes Iub interface and so on. To find out the constraints for HSDPA radio user throughput, many KPIs have been analyzed in the paper, searching correlations between them and the average throughput. At the end the number of contemporaneous active users in the cell and the mean value of the Channel Quality Indicator (CQI) reported by the User Equipment (UE) are the main factors considered. The paper proposes the use of a LVQ Neural Network able to estimate the QoS across the analysis of KPIs and to provide automatically a possible classification of warnings highlighting three main events: absence of problems, problems due to High Traffic, problems due to Bad Quality of Radio Channel. At the moment, the monitoring of TIM (Telecom Italian Mobile) access network performance is carried out mainly by technical staff highly skilled on analyzing trends in time of KPIs. The estimation of QoS is not a trivial job since a lot of

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factors can affect performance and such work sometimes requires long time resources. Therefore, a smart and background working tool able to automatically identify possible problems across the analysis and related diagnosis of the radio access network problems, is a strong improvement for HSDPA analysis which could help the TIM staff to better focalize their abilities and time resources in the improvement of the customer perceived quality. After a brief review of LVQ Neural classifier in section II, the paper describes the analysis and the choice of KPIs and finally the results of the LVQ classification in terms of false alarms and missing detections show the validity of the neural classifier as a support for monitoring the radio network access (RAN) problems, and automatically detecting critical events. II. LVQ NEURAL NET A neural network is composed of simple elements operating in parallel inspired by the behavior of biological neurons [3]. It has characteristics such as self-organization, self-orientation and the ability of permitting wrong etc., it is especially superior at dealing with nonlinear problems. LVQ is a nearest neighbor pattern classifier based on competitive learning training in a supervised manner. LVQ network generally has the following three layers: input layer, competition layer and output layer. A competitive layer automatically learns to classify input vectors, while the linear layer transforms the competitive layer’s classes into target classifications defined by the user. In the input layer the original data are applied and all the units at the input layer are connected to all the neurons at the output layer with link weights. During the training stage, the values of weights used to form the reference vectors are adjusted according to the patterns of input samples in order to match with desired classes. Using Euclidean distance, the simplest strategy to find the winning neuron, j*(t), is given by [4]: where x(t) denotes the input vector, wj(t) is the weights vector of neuron j, and t is the number of iterations. The node of a particular class which has the smallest distance is declared to be the winner. Then, the weights of the winning neuron are modified as follows:

where is the learning rate. The weights will be moved closer to the class if it is the expected winning class, otherwise they will be moved away. During the testing stage, the distance of an unknown input vector to each of all classes’ reference vectors is calculated again. Then, the unknown input vector will be assigned to the class which the reference vector belongs to. To get high rate of correct classification, several variants of LVQ are proposed, such as LVQ2 [9]. The two nearest (the winning neuron and the next neuron) neurons are determined and are updated. One of them belongs to the correct class and the other one to a wrong class. The training vector must fall

inside a small, symmetric window defined around the midpoint of the two neurons, the decision boundary between the two vectors. If these conditions are met, the incorrect reference vector is moved further away from the input, while the correct reference vector is moved closer. III. KEY PERFORMANCE INDICATORS SELECTION KPIs (Key Performance Indicators) are performance indices derivable from counters located on network equipments. Counters are simple indices that increase every time an event occurs in the network, such as the number of handovers carried out properly or the number of allocations for a particular transmission channel. Given a fixed interval of analysis, called Repetition Of Period (ROP), counters measure the number of times that such events occurred. KPIs are derived through mathematical expression which take into account several single counters. The goal of research is to give a classification of possible bad perceived user experiences related to load of resources and QoS, offered by the radio access network. Speaking about the user experience, the more sensible parameter in packet connectivity is the throughput. To create these KPIs, it is required to compute the throughput per cell and then divide it by the number of contemporary active users in the cell within the measurement period. To calculate the real throughput per cell, the volume of bits transmitted by the cell is divided by the number of TTIs (Transmission Time Intervals) in which active users are present in the cell. This throughput value “in download” it is the one that better represents the radio user throughput . To find out the causes limiting the user throughput, many KPIs have been analyzed, searching for correlations among them and the throughput and finally some KPIs are established as the main useful indicators: the number of contemporaneous active users in the cell and the mean value of the Channel Quality Indicator (CQI) reported by the UE. In the following analysis, all the reported measurements are purified by the congestion situations in the connections between the Node-b and the Core Network (IuB interfaces). A. Erlang

Figure 1 – Relation between Average User Throughput and Erlang

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As shown in Figure 1, there is a strict relationship between the number of users in the cell and the throughput each user can obtain in download: this is easily explained considering that a cell with many users will be forced to share all available resources (codes, channel elements and power) between all the scheduled users. This implies that an user cannot experience the maximum throughput available as in an unloaded cell. With respect to the RAN software release installed in RNC and BTSs, when more than five users are simultaneously active into the HSDPA cell, the “radio” user throughput never reaches the threshold of 1 Mbps (Figure 1), which is retained as an adequate target for the main offered packet data services. A recent research by IPSOS, an European provider of market research surveys, focuses on the main data throughput requirements, some details are reported in Figure 2.

(region C), at least mainly two different situations are possible:  the user requires low throughput services, such as instant messaging services, chat services, and so on;  there are some other reasons which limits the throughput mainly due to the poor radio channel quality conditions. Statistically speaking, this last reason is highlighted by the CQI reported value.

% Users High throughput needed

40

Low throughput needed 30

Figure 3 – Decision regions based on Erlang 20

B. Channel Quality Indicator 1 0 0 Browsing Send/R eceive E-mail

Use Download/S Program hare IM Multimedia Files

VoIP Traffic

Services

Research IPSOS – Ott. ‘08

Figure 2 - Average Throughput

The 83% of the total packet traffic does not need high of TCPIP throughput, thus establishing a range between 350-450 kbit/s for the most of services would guarantee a good throughput/user. Since from TIM experience has been established that taking into account the present mobile categories distribution and, taking into account several factors like users activity factor, protocol stack header and so on, the ratio between the KPI measured radio interface throughput and final user perceived application throughput is in the range 23, therefore a threshold for the Node-b measured radio throughput of 1 Mbit/s appears to be adequate to support the most part of data services required by the customers. Considering these assumptions, we are able to provide a first classification for the throughput offered to the user, shown in Figure 3. For ROPs in which the number of Erlang in the cell is bigger than 5, the cell is classified as “Critical for High Traffic” (region B); for ROPs where the number of user is less than 5 and the throughput is bigger than 1 Mbps the cell is classified as “No Critical” (region A) whereas when in the cell there are less than 5 Erlang but the throughput is less than 1 Mbps

In HSDPA, the UE monitors the quality of the downlink wireless channel and periodically reports this Channel Quality Indicator (CQI) to the base station (the Node-b) on the uplink connection [2]. The frequency of CQI reporting is configured by the network, and typically it is set to once every 4 ms. According to the conditions of the downlink wireless channel reported by CQI, the Node-b schedules data on the High Speed Physical Downlink Shared Channel (HS-PDSCH) selecting the appropriate transport block size (number of information bits per packet), number of channelization codes, modulation type. Allocation choices for other resources such as HS-PDSCH transmit power are driven by CQI too. The CQI indicates the maximum transport block size that can be scheduled as a function of block error rate (BLER). 3GPP TS 25.214, defines CQI for UE categories based on 10% BLER. This information is encoded using an index in the range 0-31. Since the CQI by the standard is directly correlated to the Transport block, it obviously affects the throughput. In Figure 4, the strict relationship between the average CQI reported by the UE and the Average Cell Throughput is shown.

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Figure 4 - Relation between Average User Throughput and CQI

3GPP standardized a table for each terminal category where CQI values are mapped with Transmission Block Size (TBS) and TTI number. Given the UE categories distribution percentage (an example in Figure 5), the theoretical radio link throughput value can be estimated by using the following:

ThrTheor CQI  x

 4  TBS i CQI  x  * %Category _ UEi    i 1 0,002 * MinTTI i   100

(1)

where the index i corresponds to the category weighted by the number of TBS over the TTI.

Figure 6 - Comparison between theoretical and measured throughput

Moreover this CQI value is chosen by 3GPP to change the modulation scheme from QPSK to 16QAM. If a CQI value equal to 16 is assumed then it is reasonable that the channel quality is granted. The theoretical user throughput of 1Mbits corresponds to the CQI threshold of 16 and it is a value which usually meets customer needs for packet connectivity. The chosen thresholds for the classification are then:  CQI=16;  Erlang =5;  Average User Throughput=1000 Kbps. Given the thresholds, the decision regions the neural network will have to recognize are:  Critical for High Traffic (ROPs where Erlang is bigger than 5);  Critical for Bad Quality Channel (ROPs where CQI