Transport Layer Protocols for the Land Mobile Satellite Broadcast Channel

Transcription

1 Transport Layer Protocols for the Land Mobile Satellite Broadcast Channel Núria Gassó Montserrat (1), Harald Ernst (1) (1) Deutsches Zentrum für Luft- und Raumfahrt (DLR) Institute for Communications and Navigations Weßling, Germany INTRODUCTION An important advantage of satellite transmission is their inherent capability to broadcast data to large audiences. The standard application is thereby to send data streams (i.e. TV) to wide audiences using fixed satellite receivers. An extension to this approach is broadcast to mobile receivers, especially cars. This has been realised in the U.S. by XM-Radio and Sirius. All these system have in common that they broadcast streams to receiver, which are immediately played. In contrast to this traditional broadcast models, other approaches are researched in Europe (e.g. in the ESA project Ku-Mobil and the EU project Maestro), which go away from the traditional thinking of services as streams. Instead, this new approach culminates in the idea of transferring distinguished files. For these systems, unidirectional file based transport protocols are needed, which fit into the very specifics of the land-mobile satellite environment. While broadcasting streams can make use of the well defined MPEG-protocols, file transfer via an unreliable medium is less well specified. In the following an Internet Protocol (IP) based transport scheme is analysed, where the main focus is on protocols from the IETF Reliable Multicast Group. There, the combination of the Layered Coding Transport (LCT) protocol with the Asynchronous Layered Coding (ALC) and the File Delivery over Unidirectional Transport (FLUTE) protocol seems a most promising candidate for this kind of application. The basic mechanism of these protocols and their behaviour in the land mobile environment is shown and analysed. BACKGROUND In 21 DLR worked as a subcontractor together with SES Astra, ND SatCom and IMST on an ESA feasibility study for the design of a land-mobile Ku-band multimedia broadcast system for vehicles [1]. In the study it was shown that an approach, using only the Line-of-Sight LOS state and based on a file based personalised radio system, could present the user with a high-quality radio system, without the need to use high link margins. The basic idea is to transfer individual audio files to the receiver. There, the radio program is reassembled, using only correctly received files (Fig.1). The system broadcasts a large number of files to the receiver, e.g. different music or news files. The receiver then mixes the received files together, ignoring corrupted files. If enough files of good quality are received, the user experiences a continuous service, without interruptions due to bad link conditions. In such a system, it is no longer necessary to receive all content error free, but the aim is to receive a maximum number of files correctly at the receiver. The challenges for such a system are the relatively long and frequent fades of the LMS-channel, which make the channel highly unreliable, even including channel coding and interleavers of up to some seconds. This will be further motivated in the next section. For long files, there is therefore a relative high probability that the channel decoding at the physical layer will fail at some point during their transmission. For a broadcast scenario, without return channel, this would result in the failure of the whole file transfer. In this case, additional coding on the file layer is a possible solution to overcome the impairment of the channel, which is the topic of the next section. After that, the used transport layer protocol is described more in detail and its features described. In the section on Low Density Parity Check (LDPC) codes, the Forward Error Correction (FEC) scheme, which was used, is shortly described and it is shown that it has some very interesting properties in regard to flexibility and computational efficiency.

2 C F C >, C F C C I J = J A > > I J = J A F > > F > C, > F C C F C > F > > F > A F I = J A E J A A A L = J E A L E H A J L A? E J O Figure 1: Personal Radio System. Figure 2: Two-State Narrowband Channel Model [3]. Finally, the behaviour of the protocols in the land-mobile satellite channel are simulated and the results analysed. The article ends with a conclusion and a short outlook on the steps for the future realisation of such a system. THE LAND MOBILE SATELLITE (LMS) CHANNEL The main effect in an LMS channel is signal shadowing, experienced when no clear Line-Of-Sight (LOS) between the satellite and the user terminal is present. The average duration and depth of this shadowing are key parameters for any such system. For this reason, a measurement campaign was performed inside the study and the recorded data was analysed for four different environment types: Highway, Rural, Sub-Urban and Urban (for further information see [2]). The main effect in an LMS-channel is signal shadowing, experienced when no clear Line-Of-Sight (LOS) between the satellite and the user terminal is present. A relative robust model for the description of the land-mobile satellite channel is the two-state Lutz model. In our case in the Ku-band, one can assume that only the Line-of- Sight (LOS) state can be used for transmission purpose, whereas the shadowed state always results in erroneous reception. The Lutz Model is presented in Figure 2, where D g and D b are the mean durations of the good (LOS) and of the bad (NLOS) state respectively, and p xx denotes the constant state transition probabilities. The average duration of this shadowing are now key parameters for any system and the numbers which are used in the simulations later on are found in the following table: Table 1: Lutz model parameters Environment D g D b LOS Highway 18 s 2 s 9 % Rural 16 s 4 s 8 % Suburban 8 s 2 s 8 % Urban 22 s 15 s 6 % where D g and D b are the mean durations of staying in the good (LOS) and or the bad (shadowed) state before it changes to the opposite state. TRANSPORT LAYER CODING As indicated in previous section, our system is hampered by the relative long faded states. While the mean duration of fades are a few seconds, there is a non-negligible probability that fades can last up to one minute or that fades can follow so fast after each other as to be considered nearly one large fade. Normal channel coding approaches with such long interleavers require a high complexity and pose additional problems, especially concerning synchronisation. Instead we propose to add Forward Error Correction (FEC) mechanism at the transport layer of our system. The transport object is separated into k individual packets and h redundancy packets are added to it. The

3 individual packets are then transferred to the physical layer, which adds independent channel coding to each packet (e.g. a based on the Digital Video Broadcasting standard for Satellites DVB-S). The principle is shown in Fig.6. This results in a double structure, where the normal channel coding per packet combats the effect of fast fading and white Gaussian noise using soft-decision decoding. On top of it h redundancy packets are added to the k normal data packets, resulting in the transmission of n = k + h packets. This allows combating the slow fading events, where typical whole packets, or series of packets, are destroyed and it is known which packets are received correctly. Using Maximum Distance Separable (MDS) codes, like the Reed-Solomon (RS), allows a receiver to reconstruct the original information if at least k out of n packets of a packet group are received. Therefore, the receiver can cope with erasures, as long as they result in a total loss not exceeding h packets, independently, where this erasures did happen. An important point is the capability to reliably detect erroneous packets. Packets are often protected using a CRC at the Network layer (e.g. in UDP). Alternatively, one could also use the channel codes of the Physical layer, which often have also error detection capability, like in the case of DVB-S (see [4], for specific aspects). In the following it is assumed, that erroneously packets are always correctly detected and discarded. Regarding the protocol stack there are implementations possible at different layers: Data Link Layer Network Layer (IP-layer) Transport Layer The first two cases are relative similar. Both can use a fixed size (k, n) packet group, where the packet stream is segmented into groups of constant length for which redundancy is added, see e.g. [4]. In this approach parameter are implicitly fixed in the system. Alternatively, the coding can be done at the transport level for the whole object. A file is encoded as one unit, with some added redundancy packets. The coding spans a logical object, which is of variable length. The approach which is used in the following is specified by the IETF Reliable Multicast Group and is based on the transport layer. It can be found in RFC 345 Asynchronous Layered Coding Protocol (ALC)). It forms the base of the File Delivery over Unidirectional Transport (FLUTE) protocol, which will be explained in more detail in the following section. A major advantage of the second approach is that the redundancy can be file specific, and therefore highly protected files can be mixed with best effort datagrams. Additionally, each file is encoded and decoded independently, allowing parallel execution and storage of files before decoding. Lastly, the possible multiplex between packets from different files increase the spreading of the file in time. However, there are also disadvantages. First, short files consisting only of a low number of packets are not so well protected. Secondly, the signalling information of the coding scheme like e.g. packet position or the parameters of the coding have to be transferred. Lastly, the variable length and large size of the file make the usage of RS-codes either complex or sub-optimal. Especially to address this last point, an alternative low complexity FEC scheme for this purpose is described in section on LDPC codes. But before going into the details of the coding, a more in depth look on the used protocols is done. FLUTE PROTOCOLL The proposed FLUTE/ALC/LCT scheme provides reliable asynchronous delivery of files to an unlimited number of concurrent receivers from a single sender. As no information is required to be sent back from the receivers to the sender, this scheme is particularly suitable for satellite broadcast systems, which have no or only limited return link capability. Although we are referring to the scheme FLUTE/ALC/LCT as a unit, each protocol has its own specific function. Layered Coding Transport (LCT), which is based upon UDP (User Datagram Protocol) in the protocol stack (see Fig.3), is responsible for adding support for multicast on transport level, e.g. differentiating between sessions or indicating the start and stop of objects. Asynchronous Layered Coding (ALC) adds the necessary

4 8 H ) *, ! + > E J I 6 5 1! 5 $ > E J I 6 1! $ > E J I E B E B 4 A A H A N J A I E I E B = F F E? = > A Figure 3: FLUTE Protocol Stack. Figure 4: LCT Header support for combining LCT with a Forward Error Correction (FEC) building block, which is responsible for increasing the robustness of the transfer. Finally, FLUTE specification defines additional mechanisms needed to signal and map the properties of files to the general concepts of ALC. The functioning of FLUTE/ALC/LCT is based in the delivery of one or more files, called objects in the IETF language, during a session. A session comprises multiple channels originating at a single sender that are used for some period of time to carry packets pertaining to the transmission of one or more objects. Each session has a Transport Session Identifier (TSI), which together with the IP address of the sender uniquely identifies the session. In order to start receiving the data packets, a receiver must join a channel. A FLUTE/ALC/LCT channel is defined by the combination of a sender IP address and its Transport Session Identifier (TSI), which is associated with the channel by the sender. If more than one object is going to be carried within a session, the Transmission Object Identifier (TOI) must be used in the LCT header (see Fig. 4) to identify which packets are to be associated with which object. As mentioned before, the file properties are carried in-band together with the delivered files. Thus, each FLUTE session has an entity called File Delivery Table (FDT) containing these properties. An FDT consists of a set of file description entries for the files to be delivered in the session. Each file description entry must include at least the TOI for the file that it describes and the Uniform Resource Identifier (URI) indicating the location of the file. The FDT is delivered through FDT Instances. An FDT Instance is an XML structure containing one or more file description entries of the FDT. Reliable transmission is achieved in this scheme through the use of a Forward Error Correction (FEC) building block at the ALC layer, which adds a certain redundancy to each object. Therefore, it is no longer necessary to receive all IP packets correctly, but it is enough, if a certain fraction of the overall number of packets of a file arrives successfully. Information related to the FEC building block can be contained in an ALC specific LCT header extension, in FDT or in both. The format of this information is determined by the FEC encoding scheme being used. The introduction of additional redundancy at the transport layer is an important advantage for this family of protocols and in the paper the effect of this scheme is analysed for the special case of the land-mobile satellite channel. LOW DENSITY PARITY CHECK (LDPC) CODES In the following only a short overview on the used channel coding, which is based on Low Density Parity Check (LDPC) aspects is given. Since Reed-Solomon (RS) codes are rather hard to use for variable file sizes, one can utilise other codes, which are no longer maximum distance separable and need therefore to receive in average more than k packets, before they can correctly decode all erasures, but may have certain other advantages. Actually, a flexible and fast decodable code, which hence can span a longer block length, may even result in a better overall coding gain due to its time diversity, than an optimal RS-code, which can only connect a limited amount of packets. Block codes of size (n, k) can in general be described by the generating matrix G, which defines the transformation of the information vector u of length k to a codeword vector x of length n. Nevertheless, in our case for the LDPC codes the description via the parity check matrix H is more appropriate and results in the definition:

5 for any valid codeword x. Hx T = T (1) One can see each row of H as a parity check equation, where all those bits are added via XOR together, which have a 1 in the row of the matrix H. If it is a valid code, the summation results in a. Unfortunately, this does not give a general way to find the correct codeword, just a check, if the correct codeword has been found. A special code class, which is used for our erasure decoding, are LDPC codes, which can be described via its sparse parity check matrix H. The principal structure of the code is defined by a profile stating how many 1 per columns and per row are allowed. If this is constant for any row and columns, it is called regular LDPC code. These codes were invented by Gallager [5] and he showed that they are optimal codes for infinite block length. If the number of ones per columns varies, one typically defines a percentage how many columns have two ones (in the later called degree two nodes), three ones and so on for higher degrees. This approach resulted in codes, which for long blocklength compete with the best Turbo codes [6]. A code with such a structure can be decoded via iterative message passing algorithm, based on belief propagation [5]. In the case of erasure decoding, the steps simplify to individual XOR calculations per row. In [7] it was proved that LDPC decoder can in principle reach the channel capacity for Binary Erasure Channels (BEC) with an arbitrary small deviation e. In the following a different family of LDPC codes based on staircase structures are used, which allow a systematic and linear encoding in time and are also very flexible in regard to the extension to different file sizes. Staircase LDPC codes For codes which at have at least (N K 1) degree 2 variable nodes a systematic encoding method which requires linear encoding time is available [6]. H = [H 1 H 2 ] H 2 = (2) The matrix H is split in two parts, where H 1 is a sparse random matrix with the appropriate profile, excluding degree 2 variable nodes. H 2 has the mentioned staircase structure. If the source word u is loaded into the first k bits of the codeword relating to H 1, then the n k parity bits p can be computed in linear time. An example of a code of this family is the LDPC code that is under evaluation for the second generation of the digital satellite TV standard (DVB-S2). More information on the used coding scheme can be found under the webpage of Vincent Roca 1, who developed the used implementation of the FLUTE protocols. RESULTS In this section results are presented regarding the behavior of the FLUTE transport IETF protocol in the LMS channel. The first point which is looked into is the effect of spreading the transmission of a file in such a channel over the time at the transport layer level. Secondly, the combination of coding and spreading is studied for each environment, so one can decide which is the best pair of Code Rate (CR) and Transmission Time (TT) under each specific situation. Effects of Spreading Files over Time An interesting feature of the IP protocols is the possibility to multiplex different transfers together, so that each transfer uses only a limited bandwidth. This can be used to spread the individual transfer over different time spans. 1

6 Highway Rural Suburban Urban.45.4 Highway Rural Suburban Urban Probability.25 Probability Received Packets in % Received Packets in % (a) Short transmission time (b) Long transmission time Figure 5: PDFs of Received Packets for Different Environments with both Starting States In order to see which are the effects of increasing the transmission time two different situations have been tested: one with a short transmission time, in which 1 Mbyte files have been sent out using all the channel capacity (it means 8,4 seconds), and another one in which files have been spread over longer time (3 seconds). If files are sent as fast as possible and the total file transfer time is smaller than the mean duration of the good and bad states, the probability for a packet to be received mainly depends on the state in which the receptions starts. If for example the initial state is the good one, almost all packets will be correctly recovered. In contrast, if the initial state is the bad one, a high number of packets will be lost, specially in the urban environment, which is the one that has the longest bad state duration. However, when a file is spread over a long period of time, the mean number of packets received (µ g ) is equivalent to the LOS share LOS and the success probability no longer depends on the initial state. Thus, as can be seen in table for some thousands transfers, the higher the spreading time, the closer the values of µ g and LOS are and the lower the standard deviation (σ) is. Furthermore, referring to this table, note that for the short TT situation, µ g is always above the LOS value if transmission start in the good state, whereas if the starting state is the bad one, µ g is below LOS. Table 2: Mean Value of Packets Received (µ g ) and LOS in percentage 8,4 sec 3 sec Environment Good Bad Good Bad LOS Highway 93 % 72 % 9 % 91 % 9 % Rural 89 % 57 % 79 % 79 % 8 % Suburban 84 % 67 % 8 % 8 % 8 % Urban 86 % 22 % 6 % 53 % 6 % Until now we have separately studied the number of received packets depending on the state in which a transmission starts. But for having an idea of what happens in a real situation one need to apply the total probability theorem. According to this and being P G and P B the probability of staying in the good or bad state respectively. Equation (3) denotes de probability of receiving a packet (x = 1): P (χ x = 1) = p(x = 1 Good) P G + p(x = 1 Bad) P B (3) PDFs obtained after applying 3 shows that for the long TT situation there are no practical differences between the general case and the ones starting in one of the two states. And, of course, µ g and σ has similar values (see Fig. 5(b)). Otherwise, for the short TT situation, although µ g gets close to LOS, the resulting PDF is characterized by the two peaks centered in % and 1% and by a high σ value (see Fig. 5(a)). Combination of coding and spreading As shown in the last section, the number of packets received depends not only on the environment (Highway, Rural, Suburban or Rural) of the end user but also on the amount of time taken by the transmission. Although

7 .6 time 3s time 18s time 12s time 6s Transport layer n = k + h packets.5 k data packets(file/object) h redundancy packets k k+1... k+h channel coding Probability k k+1... k+h.1 Physical layer transmission Received Packets in % Figure 6: Transport Layer FEC. Figure 7: Received packets for different TT in Urban environment. it is possible for a user to receive all the packets belonging to a file, even for the short transmission time it does not happen always. Whereas with longer transmission times µ g tends to LOS, which is in the best case 9%. As long as our system is file oriented, assuming that uncompleted files are discarded, the receiver would need to recover all the data packets, which in principle never happens. In order to increase the probability of correct reception P Succ of a file two approaches can now be taken: Sending a file more than once Adding redundancy to the file at the transport layer level and interleaving it over a certain time With the first option and n repetitions, P Succ increases according to equation (4). This increases the reception probability especially for short transfers, but at least doubles the needed bandwidth per file. Therefore, in the following the second option is to be evaluated. In order to that, different coding rates (1, 2/3 and 1/2) and transmission times (15, 6, 12, 18 and 3 seconds) have been tested. P Succ rep = 1 (1 P Succ ) n (4) As a general outline one can see that using CR=1, which is equal to adding no redundancy at all, leads to a very small probability of file success. Consistent with the results of the last section, note that P Succ is then higher for low transfer times. Results obtained for code rates 2/3 and 1/2 differs considerably from one environment to the others, so the four cases needs to be separately analyzed. In the Highway environment P Succ is almost always 1% as soon as some extra coding is added, independently of the parameters chosen. In the Rural and Suburban environments protocols behave in a similar way: with CR=1/2, the transfer time has rather no influence, so it is not worth to spread files over longer times, if such a code rate is used. But, as can be seen in Fig. 8(a), with CR=2/3 P Succ increases with the transmission time. Although this tendency is common to the previously commented environments, the Urban one behaves in the opposite way and P Succ decreases with transfer time for this code rate (Fig. 8(b)). This happens, since with CR=2/3 the mean number of received packets µ g is not enough for recovering the 1 Mbyte file (6% of 1123, which is the number of sent packets, is 673,8 and the file has 743 data packets). As the standard deviation σ of the results for shorter transfer times has higher values (see Fig.7 ), it happens more frequently for shorter than for longer transfer times that a higher number of packets are correctly received. So the number of correctly received files is higher for short transfer times. Due to this big variety in the obtained results, there is not a pair (CR,TT) that can be considered optimum for all the environments. Since in the broadcast system users experience different reception conditions, one has to choose the file transmission parameters according to the characteristics of the file itself in terms of urgency, length and importance. While urgent and short files can be transmitted more than once without any kind of redundancy, the best option for files of vital importance seems to be (CR=1/2, TT=3). Whereas for the other files an intermediate approach as for example (CR = 2/3, TT=12) can be adopted.

8 Code Rate 1 Code Rate 2/3 Code Rate 1/ Received Files in % Code Rate 1 Code Rate 2/3 Code Rate 1/2 Received Files in % Transmission time in minutes (a) Suburban environment Transmission time in minutes (b) Urban environment Figure 8: P Succ for different Transmission Times CONCLUSIONS AND OUTLOOK In the paper it has been shown, that transport layer coding is an interesting option to overcome the obstacles of the slow fading land-mobile satellite channel. It could be seen, that the improvement of such a coding scheme strictly depends on the target success probability of the transfer and the time delay a transmission is allowed to have. In a follow on of the feasibility study described here, the presented ideas will now be realised as part of a demonstrator system under ESA Contract 17769/3/NL/US. Transport Layer Coding is going to be implemented for this environment and its performance will be measured in live tests, also in regard to applications like multilingual traffic information, multimedia passenger entertainment and personal radio applications. ACKNOWLEDGMENT The authors would like to thank their colleagues in the before mentioned project and especially Vincent Roca from INRIA, who developed the open source implementation of the FLUTE protocol, which was used for the simulations and is really a great tool for research. REFERENCES [1] H. Ernst, S. Scalise, and R. Dietrich, Feasibility study of a mobile Ku terminal, WP 3 design & analysis, DLR, NDSatcom, ESA-Contract No 15593/1/NL/DS, 22. [2] H. Ernst, S. Scalise, J. Kunisch, J. Siemons, G. Harles, and J. Hörle, Measurement campaign for the land mobile satellite channel in Ku-band, in European Personal and Mobile Satellite Conference (EMPS), Bologna, Italy, September 22. [3] E. Lutz, M. Werner, and A. Jahn, Satellite Systems for Personal and Broadband Communications. Berlin, Heidelberg, New York: Springer-Verlag, 2. [4] A. Donner, S. Bovelli, and S. Shabdanov, Reliable multicast based on DVB-RCS, in 2th International Communication Satellite Systems Conference & Exhibit, May 22. [5] R. G. Gallager, Low-density parity-check codes, IRE Transactions on information theory, vol. 8, pp , Jan [6] D. J. C. MacKay, Information Theory, Inference and Learning Algorithms. Cambridge University Press, 23. [Online]. Available: [7] M. G. Luby, M. Mitzenmacher, M. A. Shokrollahi, and D. A. Spielman, Efficient erasure correcting codes, IEEE Trans. Information Theory, vol. 47, pp , 21.