Computer Networks 51 (2007) 4412–4420 www.elsevier.com/locate/comnet
Network monitoring and performance evaluation in a 3.5G network Roberto Cusani a, Tiziano Inzerilli a
a,*
, Luca Valentini
b
Sapienza Universita` di Roma, Via Eudossiana 18, 00184 Rome, Italy b H3G S.p.A., Via Alessandro Severo n. 246, 00145 Rome, Italy
Received 10 September 2006; received in revised form 23 May 2007; accepted 13 June 2007 Available online 22 June 2007 Responsible Editor: C. Westphal
Abstract Monitoring network performance and status is a fundamental task for network operators as it directly impacts the quality of the offered services and hence user satisfaction. For this purpose a consolidated approach, which is largely adopted by network operators, is based on the so-called KPIs (key performance indicators). In this paper, we propose and discuss a set of KPIs to monitor network performance of the new HSDPA enhanced UMTS infrastructure. KPI statistics are collected and analysed from the novel HSDPA network of H3G, one of the major Italian mobile network operators. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Network performance; KPI; Channel quality; UMTS; HSDPA
1. Introduction In the year 2006 the high-speed downlink packet access (HSDPA) enhanced UMTS infrastructure was deployed in Europe as well as in Japan. HSDPA raises the maximum bit rate in a UMTS cell up to 14.4 Mbps as it improves the radio link resource management by including adaptive modulation mechanisms [1], adaptive error correction schemes based on Hybrid ARQ [2] as well as other techniques for enhancing the throughput and the
* Corresponding author. Tel.: +39 3333070111; fax: +39 0644585471. E-mail address:
[email protected] (T. Inzerilli).
quality of the transmission in the downlink [3]. The forthcoming high-speed uplink packet access (HSUPA) will further improve uplink transmissions [4] in the UMTS infrastructure. High-speed (HS) technology will be supported by a sharing of network resources between HS and R99 (release 1999) in the radio link as well as in the fixed network, thus increasing the overall complexity of the UMTS infrastructure. As a consequence, dedicated management tools are to be adopted by operators to properly introduce it into the UMTS network [5]. Critical aspects in monitoring the network are the available capacity and the data throughput for end users, as well as the radio link quality. Achievement of good performance greatly depends on the ability to quickly detect occurrence
1389-1286/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2007.06.003
R. Cusani et al. / Computer Networks 51 (2007) 4412–4420
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of congestion as well as channel state degradation in the wireless link. A consolidated approach for network performance evaluation and monitoring relies on a set of key performance indicators (KPIs) [6]. In this work, we describe a KPI-based framework for the HS network. In particular, in Section 2 a set of KPIs is proposed which can be exploited by mobile operators adopting such novel technology. Our strategy is validated by experimental results reported and discussed in Section 3 collected from the Italian H3G operator network, deployed in Spring 2006. Some conclusions are reported in Section 4.
Table 1 List of proposed KPIs
2. The proposed network monitoring approach
from the final users (i.e. RAB_FR, hCQIi, BLER, CLR), which largely depends on the call attempt success ratio, communication throughput, channel impairments and network congestion.
KPIs are implemented into operator monitoring tools, exploiting counters, which are some elementary variables whose values are regularly collected by the network in a certain number of sites, network nodes and interfaces.1 The practical definition of a set of KPIs is then largely dependent on the counters available for the observed network and monitoring tool has to be developed specifically on the basis of the available counters. The set of KPIs proposed in the following exploits counters which are commonly available and present in the H3G network. Deployment of HS in the current R99 access network infrastructure impacts both the fixed part as well as the radio link, where adaptive modulation and coding allow for higher throughputs. In turn, the increased throughput in the radio link requires extra capacity in the fixed network to be provided. KPIs for both the fixed network and the radio link are then to be defined and monitored. The KPIbased monitoring approach we are presenting include both the fix access network and the radio link. Table 1 summarizes the KPIs discussed in the following sections. All the proposed KPIs are used to monitor most important events in the overall status of the operation of the mixed R99/HS network. Namely, the proposed KPIs provide measurement on two general aspects which recur in the evaluation of any telecommunication network, i.e. efficient resource utilization (i.e. HSPR, CU, TM and THD) as well as assurance of QoS level as perceived 1 Examples of counters can be the number of positive acknowledgement for frame reception and the number of successful radio access bearer (RAB) set-up.
KPI HSPR CU TM THD RAB_FR hCQIi BLER CLR
Utilization Allocation of power to HS Allocation of codes to HS Utilization of HS bearers HS traffic out of the total generated traffic Radio access bearer failure ratio Measuring effectiveness of AMC (mean value of reported CQI) Assessing quality of the channel and of the AMC channel estimation Cell Loss Rate in the IUB inteface
2.1. Resource management and monitoring of the radio link In the radio link the most important resources to allocate are transmission power and codes, which are shared between R99 and HS radio bearers. Monitoring their utilization is then particularly important to observe the overall operation of the network. In a mixed HS/R99 infrastructure the total transmission power PTOT for a single base station is the sum of the power terms PCC, PHS, PR99 allocated to Common Channels for signaling, to HS traffic and to R99 traffic, respectively: P TOT ¼ P CC þ P R99 þ P HS ¼ P CC þ P HS-R99 :
ð0Þ
The total transmission power PHS-R99 allocated to HS and R99 traffic can be optimized to a target value with a modulation of the distribution of power between HS and R99 radio bearers, which takes into account the maximum transmission power for a cell PMAX and the power fluctuations due to the power control algorithm present only in R99. Namely, PHS-R99 has to be set generally lower than PMAX. When no HS traffic is sent PHS-R99 is given by the following: P HS-R99 ¼ P R99 ¼ P MAX K;
ð1Þ
where the difference between PMAX and PHS-R99 is the allowed fluctuation for the power control algorithm. Instead, when HS traffic is present the target power PHS-R99 is calculated using the following more general equation:
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P HS-R99 ¼ P TOT P CC ¼ ðP MAX P HS Þ K þ P HS :
ð2Þ
The parameter K determines the percentage of power allocated to HS out of the maximum power PMAX. This is hereafter referred to as HS power ratio (HSPR) and defined as HSPR ¼ P HS =P HS-R99 ¼ 1 K ðP MAX P HS Þ=P HS-R99 ;
ð3Þ
where K can be fixed experimentally by the network operator to optimize system performance under some specific goals, e.g. to maximize the HS bearers throughput. Bandwidth allocated to a terminal in the radio channel increases with the number of codes which are assigned to it. The actual exploitation of the assigned codes is measured by the Code Usage (CU) over an observation interval set by the operator, a parameter hereafter defined as PN TTI i¼1 N i-PDSCH CU ¼ ; ð4Þ N TTI N max-PDSCH where NTTI is the number of transmission time intervals (TTIs) over the observation time range (e.g suppose an observation time range of 15 min, then NTTI would be 450,000 TTIs, each of them lasting 2 ms [7]), Nmax-PDSCH is the maximum number of configured codes for HSDPA traffic in a cell and Ni-PDSCH is the number of codes actually used by HS traffic in the ith TTI out of the maximum usable codes in the cell Nmax-PDSCH. Eq. (4) can be used to assess the degree of utilization of radio resources: low values of CU denote limited utilization of codes for HS traffic, thus allowing for a substantial increase of HS traffic without the need of increasing Nmax-PDSCH. In general, the greater number of codes used by HS traffic, the greater throughput experienced. However, a limited use of allocated codes reduces throughput. In the initial phase of deployment, radio bearers allocated to HS traffic in the mixed R99/HS infrastructure will be limited. HS bearer need to be used only when high bit rates are actually necessary, while low-bit-rate voice communications can be better transported on R99 bearers. Hence, HS-enabled terminals are capable of switching traffic from HS to R99 channels whenever the level of throughput falls under a certain threshold. As a consequence, actual use of HS bearers can be assessed with another KPI, which measures transitions from HS to R99 chan-
nels. This is hereafter referred to as Traffic Model (TM) and defined as TM ¼
N RAB TOT 1; FACH N RAB TOT N TR
ð5Þ
where N FACH is the number of transitions from the TR DSCH state (traffic on HS bearer) to the FACH state (traffic on R99 bearer). Whenever the throughput pertaining to a HS bearer drops below a certain threshold a transition to a R99 bearer is forced so as not to waste the precious HS bearers. The TM indicator of Eq. (5) then reflects the frequency of such transitions and allows assessing the actual exploitation of HS technology: a reduced frequency of transitions denotes a good use of HS bearers. Another aspect of interest for the operator is the repartition of traffic between the high-speed (HS) network and the current UMTS network, based on R99. The throughput distribution (THD) of the offered HS traffic over the total offered traffic managed by the network can be measured by the following indicator: PNCHS
THD ¼ PNCHS i¼1
HS i¼1 THi P NCR99 THHS þ i¼1 THR99 i i
;
ð6Þ
where NCHS, NCR99, THHS and THR9 i i denote the number of HS bearers, the number of R99 bearers, the throughput of the ith HS bearer and the throughput of the ith R99 bearer respectively. In the early stage of deployment of HS since year 2006 this KPI will return modest values, as the bulk of the traffic will be conveyed through the R99 infrastructure conveying mostly voice communications. Moreover, first terminal handsets [8] will not fully exploit HS technology to achieve 14.4 Mbps and will allow for a limited peak data rate of 1.8 Mbps only. Conversely, in the medium or short-term, this KPI can be used to evaluate the actual usage of the HS technology vs. R99. Once the analysis of resource utilization is completed, it is possible to observe network performance with a better understanding of the status of the network. Key aspects to assess are the degree of accessibility to the network, the effective throughput offered by the network and error conditions of the radio link. These mainly determine the QoS perceived by the end user unlike the previous KPIs whose purpose was to assess efficient exploitation of network resources. With the following KPIs an operator has some convenient instruments to
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monitor network congestion as well as channel impairments. As far as the accessibility degree is concerned, we define the RAB attempt failure ratio (RAB_FR) as RAB FR ¼
RAB N RAB TOT N FLR ; N RAB TOT
BLER ¼ N NACK =ðN NACK þ N ACK Þ:
USER PLANE (AAL2)
vc
ð7Þ E1
where N RAB TOT is the total number of RAB set-up attempts while N RAB FLR is the number of successful RAB set-up attempts. High values of this KPIs are a symptom of either congestion or frequent errors in the radio channel or wrong network configuration, which all requires operator intervention on the network. In a HS network congestion and link errors are greatly dependent on the adaptive modulation scheme specific of this technology. Namely, the throughput is dynamically adjusted and governed by the AMC algorithm [1], which is based on regular measurements of radio channel quality. For this purpose the so-called channel quality indicator (CQI) [7] is introduced as an integer variable ranging from 0 to 30 estimating the channel quality. For each CQI value a specific coding and modulation scheme is adopted in the radio link and the resulting throughput is a monotone function of the measured CQI. The mean value of the measured CQI, i.e. hCQIi, along with the achieved throughput levels can be used to assess the operation of the AMC algorithm. Namely, large values of CQI denote a satisfactory evaluation of the channel quality by the algorithm, which result into a significant performance. The block error rate (BLER) experienced in the radio link can be observed directly in order to verify channel estimation of the AMC algorithm be correct. This can be obtained through the network counters collecting samples of positive and negative frame reception acknowledgements, as follows: ð8Þ
2.2. Resource management and monitoring in the fixed network As far as the fixed network is concerned, the higher throughput introduced by HS with respect to R99 requires larger bandwidth. The IUB interface between RNCs and Nodes B (see [9] and Fig. 1) is the most critical point for bandwidth management in the fixed network, which requires suitable design choices. In particular, bandwidth in each link
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VP
vc vc vc vc
ALCAP (AAL5)
NBAP-C (AAL5) NBAP-D (AAL5)
NODE B
SYNC (AAL5)
RNC
Fig. 1. Interconnection between a Node B and an RNC in the UTRAN through the ATM IUB interface.
between a Node B and an RNC is reserved and partitioned into traffic classes to convey multiple virtual circuits (VCs) and connections. VCs and connection can be established for the transport of either R99 or HS traffic. In the following, we propose a possible configuration strategy for the IUB interface adopted in the Italian H3G network. In its first phase of deployment HS will support mainly data traffic in best effort mode, voice traffic being supported by R99 channels. Constant bit rate (CBR) connections can be then used to convey R99 traffic, whereas unspecified bit rate (UBR) connections can be destined to HS traffic. In this scenario two VCs, i.e. VC1 and VC2, are used. Common channel (CC) signalling is transported only in VC1 while signalling associated to each connection is transported in both VC1 and VC2. HS traffic is then conveyed using the bandwidth which is left by the R99 CBR connections. However, a minimum bandwidth, hereafter referred to as BHS min , can be reserved to HS traffic to prevent its starvation when the HS traffic load becomes nonnegligible with respect to R99 traffic. In such scenario, in order to dimension the IUB interface and to support admission control function for each RNC-Node B interconnection, we introduce the following two constraints: N cell X
ðN R99 þ N HS þ N Si Þ 6 2 N CID 2 N cell ; i i
ð9Þ
i¼1 N cell X
HS ðBR99 þ BSi Þ 6 C 1 þ C 2 BCC i 1 Bmin ;
ð10Þ
i¼1
where the index i refers to the ith cell headed by the Node B under question, for i ranging in the interval
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[1, Ncell], being Ncell the number of the cells which are governed by the considered Node B. S N R99 ; N HS i i ; N i and NCID in Eq. (9) are respectively the number of connections allocated for R99 traffic, for HS traffic, for individual-connection signaling in each cell i and the maximum number of connections which can be established for a single VC. The righthand side of Eq. (9) accounts for the maximum number of connections NCID for the two considered VCs minus the total number of CC signaling connections (an uplink and a downlink CC signaling connection per cell). BR99 ; BSi ; C 1 ; C 2 ; BCC in Eq. (10) are respectively i 1 the shares of bandwidth allocated for R99 traffic, and for individual-connection signaling, the total capacity allocated for VC1, for VC2 and for common channel signaling in VC1. HS traffic can use the reserved portion of capacity BHS min and it can be also assigned the spare bandwidth left by R99 traffic and signaling, which is given by the difference of the right member and the left member in Eq. (10). Once Eqs. (9) and (10) are enforced for admission control, performance monitoring for the HS traffic can be achieved by observing the frame loss rate in an RNC-Node B interconnection, which is the most significant performance indicator for the IUB interface. Cell loss rate (CLR) for HS traffic is defined as trans CLR ¼ N Loss i;j =N i;j
ð11Þ
and it is used to assess if the IUB interface for a VP between the jth RNC and the ith Node B is suitably configured. Values of CLR exceeding a certain threshold denote the need of reconfiguring the IUB interface, e.g. by increasing the overall bandwidth of the VP, or by increasing reserved bandwidth for HS traffic BHS min . It is worth noticing that Eqs. (9) and (10) are to be used for long-term planning of network configuration. Renting or deploying further links in the fix network to increase capacity impacts significantly mobile operator profits and costs.
Table 2 CQI values and throughputs for terminal category 12 CQI
Throughput [kbps]
0–7 8–9 10 11 12 13 14 15–30
160 320 480 640 800 960 1120 1600
exhibited a peak rate of 1.8 Mbps at RLC level (Table 2). This terminal category was the only commercially available in the time when the trials were run. Our KPIs samples were calculated from detections of counters of the H3G network. Namely, for each counter a detection was carried out every TTI, i.e. 2 ms [7]. Detections were then averaged over suitable observation time intervals, e.g. one quarter of hour, or one hour depending on the counter type and location, and stored. Sometimes also maximum and minimum values over observation time intervals were stored in the place of mean values, in order to provide alternative statistics. Storing and managing detection of all types of counters for many sites in a big network, such as that of H3G, with a greater granularity instead of averaging or keeping only maximum or minimum values is not a viable solution. It would consume the storage capabilities of the monitoring system and require frequent back-ups. To give an example in order to store all detections (using 4 bytes) of a single counter relevant to one network node only for one week a storage capacity superior to 1 GB would be needed. The stored average (or maximum) detections are then the only accessible counter statistics in the H3G operator network. However, they can be conveniently used to monitor the overall status and operation of the network. In particular, they are advantageously exploited to obtain our set of KPIs. An analysis of the network every 15 min allows an operator to monitor simultaneous KPIs conveniently and detect malfunctioning.2 In the
3. Experimental analysis In this section, we present some statistics collected in the second decade of May 2006 from the HS network by H3G. Trials were carried out in a district of Rome headed by a single RNC. User equipments belonging to category 12 were used [8]. They adopted QPSK modulation only and
2 Shorter term observations might be realized with triggers able to automatically launch alerts whenever detections from a counter averaged over short periods, such as 1 min or 5 min, exceeds or falls below a certain threshold. However, in practice, it is easier for an operator to watch the status of the network and react adequately to some critical events if an action is requested no more frequently than 10–15 min.
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T-put [Kbps]
1600 1400 1200 1000 800 600 400 200 0 0
2 4 6 Allocated Power toHSDPA [W]
8
Fig. 2. Measured throughput (T-put) of HS terminals vs. transmission power.
800
5 4 BLER [%]
following, we are commenting statistics collected for the KPIs defined in Section 2. In Fig. 2, average throughput vs. transmission power allocated to HS is shown (Eqs. (1)–(3)). The average of measured throughput of HS terminals increases with the allocated power for most of the power range in [0,7] W. However, it is worth noticing that the experimental curve beyond 6 W exhibits a reduction of throughput, which corresponds to a K, roughly equal to 0.7. Such reduction can be explained with an increase of interference due to non-linear effects in the specific equipment used in the trial when exceeding certain thresholds of transmission power in a base station. Power allocations to HS beyond 6 W should be then avoided in order to reduce resource consumption and optimize network performance. Fig. 3 shows how the average throughput (T-put, continuous line) and Code Usage (see Eq. (4)) are roughly proportional in the observed interval of time, which shows the correct operation of the AMC (adaptive modulation and coding) scheme: alignment of throughput and CU denotes limited BLER values. This is also confirmed by Fig. 4, which shows BLER dynamics vs. CU. Basically,
3 2 1 0 0
5
10 15 Code Usage [%]
20
25
Fig. 4. Average block error rate (BLER) as a function of code usage.
when the BLER decreases, the CU increases. Null values of BLER determines CU beyond 20%. Such results are obtained in presence of a CU never exceeding 45% and shows that the radio link can still sustain substantial increase of HS traffic. Fig. 5 shows some statistics on traffic model (see Eq. (5)). The diagram shows the frequency of transitions from HS-DSCH (HS channel) to FACH (R99 channel) state, which takes place when HS throughput is lower than a certain threshold. The limited number of transitions shows a satisfactory use of HS technology. This is due to the fact that HS terminal, predominantly laptops provided with a HS network interface card, are most of the time committed in data downloading and uploading session rather than in voice communications with good quality of the radio channels and low congestion degree. Data-transfer applications are in fact conveyed in TCP sessions whose throughput increases up to the maximum available bandwidth, given sufficiently good conditions of the radio channel. In Fig. 6, the percentage of average RAB set-up failures is shown (see Eq. (7)). The observed RAB_FR appear satisfactory as only 2.5% of the samples in the observed interval experience RAB failures greater than 1% (values lower than 1% are approximated to 0 by counters), though some of
50
700
50
40
600
30
400 20
300 200 100 0
TM [%]
40
500
CU [%]
T-put [%]
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30 20
10
10
0
0
Date 8-12 May
Fig. 3. Throughput (T-put) and code usage (CU) measured during 5 days.
Date ( 8-12 May)
Fig. 5. Behavior of the traffic model (TM, see Eq. (5)) in the period 8–12 May.
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R. Cusani et al. / Computer Networks 51 (2007) 4412–4420 120 Frame Loss [%]
RAB_FR [%]
100 80 60 40 20 0
4 3.5 3 2.5 2 1.5 1 0.5 0
Date ( 8-12 May)
Fig. 6. Radio access bearer (RAB) set-up failures in the period 8– 12 May.
the samples exhibits 100% failure as were detected during network configuration and maintenance periods. The overall accessibility degree of the network in this first operation phase is then satisfactory. This is mainly due to the fact that in this first trial the network is not particularly loaded. Fig. 7 shows the CQI calculated on the basis of the channel status. Detected values range in the interval [2,15] as expected on account of the fact that we are using terminal 12 handset category. Large values of hCQIi (CQIs greater than 10) show an increase of throughput brought about by the AMC algorithm of 4 times (Table 2) as well as its wide variability range denote a satisfactory behavior 16 14
Date 8-12 May
Fig. 9. Cell loss rate (CLR) in the IUB interface measured during 5 days.
of the AMC algorithm, which allowed for measured peak rates greater than 1500 Kbps. This is also confirmed by the low values of BLER reported in Fig. 8: BLER is often lower than 0.1% and the largest value is 4%. Fig. 9 shows the CLR in the IUB interfaces of the fixed network headed by the RNC of the considered district. Within the observed interval of time the CLR is limited to a maximum of 3.5%, which shows that the bandwidth offered in the IUB, with UBR connections is sufficient for HS traffic. It is worth noticing that in this scenario no capacity BHS min is reserved to prevent HS traffic starvation. These values of CLR denote that spare capacity which is not used by R99 traffic is sufficient for transport of high-speed (HS) traffic in the observed RNC. This is due to the fact that HS generated is still modest in May 2006.
CQI
12 10
4. Conclusion
8 6 4 2 0 Date (8-12May)
BLER [%]
Fig. 7. Measured channel quality indicator (CQI) in the period 8–12 May.
4 3.5 3 2.5 2 1.5 1 0.5 0 Date (8-12 May)
Fig. 8. Behavior of the block error rate (BLER) in the period 8– 12 May.
In this work, we have discussed an overall approach for network performance dimensioning and evaluation in a mixed HS/R99 infrastructure. The proposed approach has the following main features: (i) it relies on KPIs, largely adopted by operators for network performance monitoring, (ii) include network resource utilization as well as QoS performance KPIs, (iii) it is based on commonly available counters, which makes it adapt for easy application to a generic HS operator network and (iv) it allows making an assessment of the whole access network including the radio link as well as the fixed access network. The approach was validated using the H3G network infrastructure as our benchmark. Namely, KPIs statistics were collected in Rome in the second decade of May 2006. Experimental results showed how the KPIs model can be used to assess the overall operation of a HS infrastructure in terms of use
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of HS resources, quality of radio channel, network congestion and effectiveness of adaptive modulation algorithm. Namely, the collected statistics with KPIs have shown correct dimensioning of the network in this early stage of deployment of HS confirmed by low BLER in the radio link and low CLR in the fixed network. In addition, a good degree of accessibility to the HS infrastructure was revealed by the limited percentage of RAB set-up failures. Low frequency of transitions from the DSCH state to the FACH state revealed a good exploitation of the HS bearers to achieve higher throughput, which conveyed data traffic. The effectiveness of the AMC scheme was proved by the wide dynamics of the measured CQI as well as by its mean value greater than 10. Furthermore, observed throughput vs. transmission power revealed a non-monotone dynamics on account of interference caused by non-linear effects beyond 6 W. This observation can be used to optimize transmission power allocation to HS and R99 traffic. Acknowledgements The authors thank Stefano Crescentini, Maurizio Fianchini, Luciano Gaggero and Francesco Lapreziosa, who partially supported this work. References [1] Yuping Zhao, Theoretical study of link adaptation algorithms for adaptive modulation in wireless mobile communications, ICUPC’98 1 (1998) 587–591. [2] A. Das, F. Can, S. Nanda, An asynchronous and adaptive HARQ scheme for 3G evolution, in: Proceedings Conference on IEEE VTC2001 Spring, vol. 1, 2001, pp. 628–632. [3] X. Liu, E.K.P. Chong, N.B. Shroff, Transmission scheduling for efficient wireless resource utilisation with minimumperformance guarantees, in: Proceedings Conference on IEEE VTC 2001, Fall, vol. 2, 2001, pp. 824–828. [4] J.M. Holtzman, CDMA forward link water filling power control, in: Proceedings Conference on IEEE VTC2000, Spring, vol. 3, 2000, pp. 1663–1667. [5] Pedersen, Lootsma, Stottrup, Frederiksen, Kolding, Mogensen, Network performance of mixed traffic on high speed downlink packet access and dedicated channels in WCDMA, in: Proceedings Conference on IEEE VTC2004, Fall, vol. 6, 2004, pp. 4496–4500. [6] R. Kreher, T. Rudebusch, Short introduction to network monitoring, troubleshooting, and network optimization, in: UMTS Signaling, John Wiley & Sons, 2005, pp. 133–148. [7] 3GPP TS 25.214 V5.5.0 (2003–2006) 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Physical layer procedures (FDD), Release 5.
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[8] 3GPP TS 25.306 V5.5.0 (2003–2006) UE Radio Access Capabilities, Release 5. [9] 3GPP TS 25.430 V5.5.0 (2005–2006) 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; UTRAN Iub Interface: general aspects and principles, Release 5.
Roberto Cusani received the ‘‘laurea’’ degree in Electronic Engineering (cum laude) and the Ph.D. in Communication Systems and Computer Science from the University of Rome ‘‘La Sapienza’’. From 1986 to 1990 he was research engineer at the University of Rome ‘‘Tor Vergata’’, teaching Digital Signal Processing. In 1991, he joined the University of Rome ‘‘La Sapienza’’ as Associate Professor of Signal Theory. In 2000, he becomes Full Professor and teaches Information Theory and Coding, and Mobile Communications. Since 2004 he is the head of the Telecommunication Department (INFOCOM) of the University of Rome ‘‘La Sapienza’’. His former research activities concern transmission and coding of signals and images, with emphasis on random processes, spectral estimation and image coding. Since 1992 he focused his activities in the field of the digital communication systems, with emphasis on channel equalisation and coding for HF and radio-mobile (GSM) links, on the design of CDMA receivers for UMTS and, in general, on the use of digital techniques within telecommunication equipments. More recently his interests also includes the study of multiple access control (MAC) protocols with application to wireless area networks (WLANs), reconfigurable ad-hoc networks and satellite links. He is author of more than 100 publications in international journals and conferences, of the textbook ‘‘Teoria dei Segnali’’ and of five patents regarding telecommunication applications. He was involved in many research programs, both national and international, and in projects with the industries.
Tiziano Inzerilli graduated in Electronic Engineering cum laude in 2000 at the University of Rome Sapienza where he also received a Ph.D. in Computer Engineering in the year 2004. He is currently holding a class of Fundamentals of Telecomunications for the degree course of computer engineering at the University of Rome Sapienza and carrying out postdoctoral studies on service management and QoS in wireless networks. He has coordinated research activities for the University of Rome within the EU sponsored projects in fifth and sixth research framework since year 2000, in the WINE, BRAHMS, SATIP6, DAIDALOS and DAIDALOS II projects focusing on mobility, QoS and pervasive computing in wireless networks, satellite broadband access in DVB-S/RCS networks. He is currently representative of the University of Rome Sapienza in the Scientific and Technical Committee of CRAT, a research institute owned by Qualcomm Italia, Elsag Security and the University of Rome Sapienza.
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Luca Valentini graduated in Electronic Engineering with First Class Pass in 1995 at the University of Perugia. He started his career at TIM (Telecom Italia Mobile) and was initially responsible for a research project on UMTS commissioned to Coritel (Alessandro Pace, Luca Valentini: ‘‘System Level Performance Evaluation of UTRA-FDD’’, 11th PIRMC, 19th September 2000, London). He managed W-CDMA trial in collaboration with Ericsson S.p.A. and CSELT S.p.A. (‘‘UMTS experimental system in Italy – First evaluation results’’, IEEE Wireless Communications and Networking Conference 1999,
New Orleans, 21–24 Sept 99). He participated to standardization groups: ETSI/STC/SMG2+UMTS Ad Hoc, ETSI/STC/SMG2/ L1 Expert Group, 3GPP/TSG-RAN plenary, 3GPP/TSG-RAN/ WG4. He is currently employed at H3G S.p.A. where he is in charge of UTRAN equipment department. Among his current responsibilities it is worth mentioning UTRAN product evaluation, specification of roadmap requirements and product change requests, evaluation of transmission equipments for backhauling, identification of H3G requirements for new functionalities and architectures to be deployed in field (i.e. HSDPA/HSUPA, new transmission architecture, ATM/AAL2 concentration, HSDPA offload over ADSL2+, Wireless Local Loop).
