STATISTICAL CHARACTERIZATION OF THE RAILROAD SATELLITE CHANNEL AT KU-BAND Giorgio Sciascia *, Sandro Scalise *, Harald Ernst * and Rodolfo Mura + * DLR (German Aerosace Centre) Institute for Communications and Navigation P.O. BOX 1116, 83 Wessling, Germany Email: Sandro.Scalise@dlr.de ABSTRACT + Alenia Sazio Business Directorate Multimedia via Bona 85, 156 Roma, Italy In this aer the behavior of the Railroad Satellite Channel (RSC) is analyzed. The main objective is to study some eculiar effects that are characteristic of the high seed railway environment and to develo a dedicated model in order to characterize the behavior of the satellite channel. Particularly, a model for the RSC at Ku-band is roosed and used to analyze the imact on the overall link erformance of different techniques to counteract the channel imairments, such as time and sace diversity, and hence to address the design of a robust radio link for the railway environment. This work is currently suorted as art of the FIFTH roject (Fast Internet for Fast Trains Hosts) by the Euroean Commission under IST Contract Nr. 1-3997. The goal of the FIFTH roject is to develo an innovative system in order to rovide high quality internet and digital TV services to users traveling on high seed trains [1]. I. INTRODUCTION Satellite broadcast in the Ku-band to fixed (TV) receivers has already a successful history. The interesting otion to extend the use of Ku-band to mobile recetion, together with the need of high gain directional antennas in this band, oses the additional roblem of ointing and steering the terminal antenna toward the satellite. Moreover, for small rivate vehicles, sace and design limitations oblige to limit the size of the receiver antenna. For ublic transortations such as buses or trains, these constraints are less stringent. In this last case, the availability of a return link could be also foreseen, thus extending the range of ossible services also to interactive and location based services. The imortance of studying the RSC is due to the fact that the railroad environment resents some eculiar fading events, like short reetitive fading events due to the ower line structures or long duration fading events due to long tunnels or railway stations. As deicted in Fig. 1, two main scenarios have been identified within the railway environment, namely Scenario A and Scenario B. Scenario A consists of relatively oen areas crossed by the railway where the satellite link should be sufficient to ensure continuous service. On the other hand, the Scenario B includes long tunnels, urban areas and large train stations where no direct satellite visibility can be achieved for long time intervals. In those areas, roer ga fillers have to be used to guarantee the connection to the travelers. This aer is organized as follows: Section II is dedicated to introduce the model of the RSC obtained by integrating an already existing model for the Land Mobile Satellite Channel (LMSC) derived from direct measurements with the additional effect of the ower suly arches and of the tunnels. In Section III, a characterization of the RSC based on 1 st and nd order statistics extracted from the model by means of software simulations is resented and the imact of time and sace diversity fading mitigation techniques is evaluated. Finally our conclusion and future lines of work are resented in Section IV. II ANALYSIS OF THE RAILROAD SATELLITE CHANNEL The LMSC has been widely studied in the literature ([], [3]) by means of several measurement camaigns including Ku-band [4], and many narrow and wideband models have been consequently roosed. Nevertheless, for the secific case of the RSC, only few result are found in the literature, e.g. in [5] and [6] as outcomes of limited trials camaigns erformed in Sain and Jaan. These results reresent a very interesting reference, although no secific channel model is roosed.
A first model of the RSC, resented in [7], was develoed by integrating a traditional Markov-chain model for the highway environment at Ku-band with the deterministic fading events caused by the ower suly arches and by the tunnels. This resulted in a hybrid model, where the statistical art only accounted for small obstacles such as bridges, trees and small buildings that may aear along the railroad, and the deterministic art accounted for ower suly arches and tunnels according to a given railway ath. This model is a reliable tool to estimate the behavior of the RSC over a given ath, though the statistic obtained through it may lack in generality. As reorted in [7], in fact, considering different railroad aths could result in different channel characteristics. Fig. 1. Envisaged Scenarios for the Railway Environment As a second ste, the information concerning the tunnels and the motion of the train have been rocessed in order to develo an RSC model in which the deterministic art only accounts for the resence of ower suly arches, which are in any case a deterministic function of the motion of the train and of the railway layout. Four random variables can be generated using the Probability Density Functions (PDFs) of seed and acceleration of the train, length of tunnel and inter-tunnels distance on different Italian railway aths. The deterministic fading events due to ower line arches are then suerimosed to the stochastic fading due to the Markov-chain model and to the resence of tunnels. Next, the different comonents of the model will be resented in details. II. A. Stochastic Model of the LMSC at Ku-band A narrowband statistical model of the LMSC at Ku-band, based on the Markov-chain aroach first roosed in [8], has been used. According to this model, the fading is divided into fast and slow fading. Slow fading events, normally due to large obstacles, are modeled as a finite state machine which tyically exhibits an average state length in the order of some meters. Fast fading events, due to the irregularity of the obstacles (e.g. vegetative shadowing) and to the multiath roagation henomenon can be additionally modeled as suerimosed random variations that follow a given PDF for each state. The overall PDF describing the received signal ower S is hence given by: S n () s = () s k = 1 ν (1) where n is the number of states, ν k is the absolute robability of being in state k that can be easily comuted given the State Transition Matrix Μ = [ ij ] and S,k (s) is the PDF associated to the fast fading and multiath henomena of state k. The arameters for the three states model at Ku-band taken from [9] are given in Table 1. k S, k Table 1. Parameters of the LMSC Model at Ku-band Environment LOS Shadowed Blocked µ σ c Highway 9% 7% 3% -8 db 1.5 db 17 db Rural 78% 16% 6% -7 db db 17 db Suburban 8% 17% 3% -7 db db 18 db Urban 6% 1% 3% -7 db db 17 db
The first state corresonds to Line-Of-Sight (LOS) and is modeled by means of a Rice distribution of the form: Rice ( c( s + 1) ) I ( c ), ( s) = c ex s () being I the zero-order modified Bessel function of the first kind. The Rice factor c (direct signal to multiath ower ratio) is between 17 db and 18 db, deending on the environment, as a consequence of the usage of a directive antenna. The second state, whose arameters can be found in the Table, corresonds mostly to the shadowing caused by single trees, which is modeled via the Suzuki/Lognormal-Rayleigh distribution of the received ower S: Lognormal Suzuki ( s) ( s ) = = Rayleigh Rayleigh ( s / s ) ( s ) 1 ( s / s ) = ex s 1 1 ex πσ ln1 s Lognormal s s, ds, ( 1logs µ ) σ (3) where s reresents the short-term mean ower due to the slow fading. The third state corresonds to blockages mainly due to buildings and bridges. For a directional antenna, this results in very dee fades, which could not be correctly measured, due to the noise floor of roughly - db below LOS of the measurement equiment [9]. Therefore no arameters for the blocked state are given. II.B. Model of the Fading Due to the Power Suly Arches As the train moves along the railroad, a nearly sace-eriodic fading event is exerienced whenever the LOS between the train antenna and the satellite is shadowed by the ower suly arches. A ossible layout of the arch is shown in Fig.. Although different kind of structures to suly ower to the trains are used (see Fig. 3), our analysis is based on the most critical scenario, corresonding to the uer right icture in Fig. 3. Fig. Power Suly Layout Fig 3. Different Power Suly Layout When the train aroaches the ower suly arch, and the obstacle enters the first Fresnel zone, which at m from the receiver antenna is at Ku-band only around 4 cm wide, no free sace roagation conditions can be considered. In order to characterize the attenuation suffered by the signal, a Knife-edge diffraction model has been adoted. The main constraint that limits the alicability of this model is that the dimension of the obstacle has to be much smaller than the distance between the transmitter and the receiver. In the scenario under study, this condition is fulfilled. The Knife-edge attenuation can be comuted as the ratio between the received field in resence of the obstacle (having one infinite dimension) and the received field without the obstacle by means of the following formula:
π E + j j v a + b As = = e dv v = h E 1 1, λ (4) a b v where λ is the wavelength, a is the distance between the train antenna and the obstacle, b is the distance between the obstacle and the satellite, h is the height of the obstacle above the LOS (i.e. measured from the oint where the LOS ath intersects the obstacle). In the case addressed here and deicted in Fig. 4, the obstacle has two finite dimensions and the received field is the sum of the contributions coming from both sides of the obstacle. Therefore an additional term has to be added, and (4) can be rewritten as: a+ b v d 1 1 π λ a b π E + E + j j v j v A ( ) = = + s h, e dv e dv (5) E v being d is the width of the obstacle. Finally, the usage of Ku-band leads to the need of high directive antennas. This imlies an additional attenuation due to the fact that whenever the two diffracted rays reach the receiver antenna with angles α 1 and α as in Fig.4, the antenna shows a gain G(α) < G max. Hence the total attenuation is given by: A s ( h) ( α ( h) ) ( α ( h) ) G 1 E1 G E = ( h) + ( h). (6) G E G E max max 4 d =.3 m, a = 1 m Attenuation [db] - -4-6 -8-1 Fig. 4. Knife-Edge Diffraction Concet -1 -.5 - -1.5-1 -.5.5 1 1.5.5 h [m] Fig. 6. Knife-edge Lam Post Attenuation 5 d =.4 m, a =.5 m Avarage Received Power (R = 5 Ohm) ID 56-13 -134 Attenuation [db] -5-1 -136-138 -14-15 -14-144 - -.5 - -1.5-1 -.5.5 1 1.5.5 h [m] Fig. 5. Power Line Arches Attenuation at Ku-Band -146 3.75 3.8 3.85 3.9 3.95 4 4.5 Time [s] Fig. 7. Measured Lam Post Attenuation The corresonding sace-varying attenuation has been comuted with values for a, b and d that have been extracted from the tyical layout of the Italian railroad, according to the data rovided by Trenitalia, the main Italian railway comany. Notice that the value of a deends on both the elevation angle and on the orientation of the railroad. The curve reorted in Fig. 5 refers to the worst case scenario.
As it can be seen from the lot, the attenuation due to the ower suly arch is relevant for values of h in a range of about.5 m. Therefore, considering a distance between two subsequent arches of around 53 m and a train seed of 3 km/h, the fading event occurs in the worst case every 6 ms and lasts for 6 ms. The fluctuations around the free sace loss condition ( db attenuation) are due to the fact that, as the train asses under the arch, this intersects Fresnel zones of different order. These may be either in hase, causing constructive interference or out of hase, causing destructive interference. To validate the result obtained by means of this model, a similar calculation for a frequency of 1.5 GHz has been erformed, resulting in attenuations between 3 and 5 db, which match very well with the result reorted in [5] and obtained from direct measurements. Furthermore, Fig. 6 and Fig. 7 reort a comarison between the attenuation comuted with the roosed model for a.3 m wide obstacle located at 1 m from the antenna at Ku-band, and the attenuation recorded during the measurement described in [3] when a lam ost of similar dimensions is encountered. As it can be seen, the matching is quite good. Nevertheless, the imlicit assumtion that has been made of considering each arch as a full obstacle is not always fulfilled, since is some cases the arches are made of wires. Considering that, as mentioned before, the first Fresnel zone is around 4 cm at m from the receiver antenna at Ku-band, a significant fraction of the signal ower could go through the wires that comose the arch, although several reflections and diffractions may degrade the quality of the received signal. Should the result of Fig. 5 be confirmed by dedicated measurements scheduled by the end of year 3, it is aarent that no reliable recetion can be accounted for when crossing an arch, unless some secific countermeasures are adoted. This could result in a loss of ackets each time that an arch is intersected, as reorted in [6], where IP ackets have been sent to a train via a DVB-S based satellite link. II.C. Statistics of Railway Tunnels As ointed out in [7], different railroad aths can lead to different channel erformance. The reason that mostly influences the erformance is the distribution of the tunnels along a given ath. Using the data rovided by Trenitalia regarding different Italian high seed railway aths, a statistical analysis was erformed. Fig. 8 shows the PDF relative to the length of the tunnels. As it can be seen, aart from few very long tunnels, most of them are less than 4 km long. The PDF relative to the inter-tunnels distance is shown in Fig. 9. In this case the distance is most of the times shorter than 1 km. These two statistics have been used to generate the relative random variables necessary to develo the model of the RSC..35 Samling Distance: m. Samling Distance: m.3.18.16.5.14 Probability Density..15.1 Probability Density.1.1.8.6.4.5...4.6.8 1 Length [m] 1. 1.4 1.6 1.8 x 1 4 Fig. 8. PDF of Tunnels Length 1 3 4 5 6 7 Length [m] x 1 4 Fig. 9. PDF of Inter-Tunnels Distance III. STASTISTICAL ANALYSIS OF THE RSC By means of the model resented in the revious section, time series of an arbitrary length could be generated, with the goal of refining the statistical characterization of the channel already resented in [7] and to assess the link erformance in resence of different fading countermeasures such as time and sace diversity. A new realization of the random variables describing the train seed and acceleration has been comuted every second, while the variables regarding tunnels have been udated each time the train has exit a tunnel. Finally, the received ower level inside tunnels has been set to -8 db with resect to LOS. For the sace diversity, two different scenarios have been considered:
Two antennas searated by 15 m Two antennas searated by 3 m. The first one corresonds to lacing the two antennas at the beginning and the end of one train coach, while the second one corresonds to lacing them at the beginning and at the end of the whole train. It has to be ointed out that this second case can create some severe constraints on the ossibility to arbitrarily arrange the order and the tyes of the coach when assembling a train. Finally, for the time diversity case, we have considered three different situations: One retransmission after 5 minutes One retransmission after 1 minutes Two retransmissions after 5 and 1 minutes resectively. This solution could be alied to non delay sensitive alications, e.g. TV broadcasting. III.A. Sace Diversity The overall Cumulative Distribution Functions (CDFs) for the three different cases is lotted in Fig. 1. As it can be seen, the robability to receive a signal level of -5 db wrt LOS is increased between % and 6% by the use of sace diversity technique. However the CDFs remain always above a value of 8%, due to the fact that fading events due to tunnels longer than 3 m cannot be comensated. The effect of the ower suly arches on the PDF, when no antenna diversity is alied, is visible in Fig.11, where a eak at about -1 db can be noticed. When 15 m diversity is alied, this effect is comletely comensated. On the other hand, when 3 m diversity is alied, the aforementioned effect is again visible (even if less significant comared to the case of no diversity). This is due to the fact that, whenever the train enters/exits a tunnel, the back/front antenna suffers the ower suly arches fading effects..8 1 1 Probability that the Abscissa is Exceeded.7.6.5.4.3..1 15 m diversity 3 m diversity - -15-1 -5 5 Normalized Power [db] Fig. 1. CDFs of Power for Sace Diversity Probability Density 1 1-1 1-1 -3 1-4 15 m diversity 3 m diversity 1-5 -5 - -15-1 -5 5 Normalized Power [db] Fig. 11. PDFs of Power for Sace Diversity Considering the second order statistics, the Time Share of Connection (TSC) and the Time Share of Failure (TSF) have been comuted. Time intervals where the received signal ower is greater or equal than a certain threshold are defined as connections. The TSC is defined as the robability that a given time instant belongs to a connection having duration T c. Similarly to the TSC, the TSF is defined as the robability that a given time instant belongs to a failure having duration T f. Fig.1 shows the TSCs assuming a link margin of 5 db. When no diversity is alied, the TSC curve decreases abrutly starting from aroximately 1 s. This value corresonds to the occurrence eriod of the ower suly arches at an average seed of 17 km/h. The resence of these events in the 3 m diversity case, already visible in the PDF, is reflected in a larger number of short connections (and corresondently in a lower number of long connections) comared to the 15 m case. For this reason, the TSC curve in the 15 m case overcomes the 3 m one for T c between 1 and 3 s.
1 Link Margin: 5 db Link Margin: 75 db 3 m diversity.5 1-1. TSC TSF.15 1 -.1 15 m diversity 3 m diversity 1-3 1-1 1 1 1 1 T c [s] Fig. 1. TSCs of Power for Sace Diversity.5 5 1 15 5 3 T f [s] Fig. 13. TSFs of Power for Sace Diversity Fig. 13 shows the TSF comuted considering a link margin of 75 db. In this way the effect of the sace diversity technique against the interrutions due to tunnels is addressed. The 15 m diversity shows almost the same behavior of the case with no diversity, while a modest imrovement of about 1% is exerienced when 3 m diversity is alied. III.B. Time Diversity Fig. 14 shows the CDFs in the three cases of time diversity. As it can be seen, this technique exhibits better erformance comared to the sace diversity cases, showing an increase between 5% and 3% when the normalized signal ower is set to -5 db. This leads to a value of the CDFs for the studied cases above 9% for normalized ower level below -5 db. On the other hand, from the PDFs reorted in Fig. 15 it is aarent as the effect of the ower suly arches is still resent in all the three time diversity cases. From the analysis of the first order statistics it is also evident that the 1 min case dose not lead to a relevant imrovement in channel erformance comared to the 5 min one. This is esecially due to the articular distribution of different fading events, esecially long tunnels, along the railroad. Probability that the Abscissa is Exceeded 1.9.8.7.6.5.4.3. 5 min diversity.1 1 min diversity 5/1 min diversity - -15-1 -5 5 Normalized Power [db] Fig. 14. CDFs of Power for Time Diversity Probability Density 1 1 1 1-1 1-1 -3 1-4 5 min diversity 1 min diversity 5/1 min diversity 1-5 -5 - -15-1 -5 5 Normalized Power [db] Fig. 15. PDFs of Power for Time Diversity The TSC for the time diversity cases is resented in Fig. 16. As for the sace diversity case, the effect of the ower suly arches leads to an increased number of short connections with resect to long connections. Finally, the TSF is reorted in Fig. 17: a significant imrovement is envisaged when the time diversity technique is alied. If the 5/1 min dual diversity is alied, the long fading due to tunnels is almost comletely comensated.
1 Link Margin: 5 db.5 Link Margin: 75 db 5 min diversity 1 min diversity 5/1 min diversity 1-1. TSC TSF.15 1 -.1 5 min diversity 1 min diversity 5/1 min diversity 1-3 1-1 1 1 1 1 T c [s] Fig. 16. TSC of Power for Time Diversity.5 5 1 15 5 3 T f [s] Fig. 17. TSF of Power for Time Diversity IV. CONCLUSION A stochastic model for the RSC has been resented and used to erform an extensive set of simulations. Based on the collected data, a statistical analysis has been erformed to evaluate the imact of sace and time diversity techniques on the link erformance. The following comments are in order: The effect of the ower suly arches could be effectively counteracted by 15 m diversity. Nevertheless, due to the high realization costs, other solutions such as a long channel interleaver should be investigated. Significant benefits can be achieved by setting a retransmission eriod of 5 min. No additional imrovements have been obtained by increasing the retransmission eriod to 1 min. Dual time diversity results effective also against long tunnels. The obvious drawback of the all time diversity based solutions is the waste of bandwidth. Moreover, they are only alicable to non delay sensitive alications. Future work will be dedicated to the study the issue of the robust synchronization and fast resynchronization, key oints to increase the net achievable throughut. Furthermore, a dedicated measurement camaign on the Italian railroad is scheduled by the end of year 3. REFERENCES [1] P. Conforto and G. Losquadro, Fast Internet for Fast Train Hosts: the FIFTH Project, in Proc. of 8 th Ka-band Utilization Conference,. [] E.Lutz, M.Werner and A.Jahn, Satellite Systems for Personal and Broadband Communications, Berlin: Sringer, Chater 3,. [3] F. Perez Fontan, M. Vazquez Castro, C. Enjamio Cabado, J. Pita Garcia, and E. Kubista, Statistical Modelling of the LMS Channel, IEEE Transactions on Vehicular Technology, vol. 5,. 1549 1567, Nov 1. [4] S. Scalise, J. Kunisch, H. Ernst et al., Measurement Camaign for the Land Mobile Satellite Channel in Ku- Band, in Proc. of 5 th Euroean Worksho on Mobile Personal Satellite Communications,. [5] A. Benarroch and L. Mercader; Signal Statistics Obtained from a LMSS Exeriment with the MARECS Satellite, IEEE Transactions on Communications, vol. 4,. 164-169, Ar. 1994. [6] F. Nagasage, J. Mitsugi, M. Nakayama and M. Ueba, Ku-band Mobile Multimedia Satellite Communications System for Trains, in Proc. of the 1st AIAA International Communications Satellite System Conference, 3. [7] G. Sciascia, S. Scalise, H. Ernst et al., Link Performance for Mobile Satellite Based Services in Ku-band, in Proc. of the 1st AIAA International Communications Satellite System Conference, 3. [8] E. Lutz, D. Cygan et al., The Land Mobile Satellite Communication Channel Recording, Statistics and Channel Model, IEEE Transactions on Vehicular Technology, vol. 4,. 375-386, May 1991. [9] H. Ernst, S. Scalise and E. Lutz, Land Mobile DVB-S Based Broadcast System in Ku-Band: Analysis of a Measurement Camaign, submitted to Globecom, 3.