Network Coding Assisted Mobile-To-Mobile File Transfer - LNT

Network Coding Assisted Mobile-To-Mobile File Transfer Larissa Popova, Armin Schmidt, Wolfgang Gerstacker, and Wolfgang Koch University Erlangen-Nuremberg, Germany Email: {popova, aschmidt, gersta, koch}@LNT.de

Abstract—This paper is a sequel of previous work, in which we have studied the problem of congestions on the typically overloaded downlink channels in UMTS (Universal Mobile Telecommunication System) networks. Our concept is based on the uplink/downlink traffic imbalance in 3G wireless networks and is realized by organizing seamless cooperation between peerto-peer and cellular networks using a unified radio interface for both systems. The primary goal is to improve the spectral efficiency of cellular networks by enabling direct mobile-tomobile (m2m) communication on temporarily unused uplink channels for the distribution of a large popular content in a non-real time multicast manner. The aim of this work is to further improve the interaction between cellular and peer-to-peer networks by generalizing the traditional scheduling paradigm. A network coding technique is embedded as a solution to the scheduling problem in the distributed dynamic environment of wireless large-scale networks. We investigate the performance of the system in terms of dependability of information distribution among m2m users. Simulations demonstrate the enhanced performance of the file distribution in terms of file download time. Furthermore, the obtained results highlight that network coding based m2m data transfer allows distribution of popular files to a large number of users while placing minimal bandwidth requirements on the central server.

I. I NTRODUCTION AND M OTIVATION OF R ESEARCH Although wireless systems are continuously developing, the basic constraint of hierarchically constructed cellular systems, namely, the limited capacity of the radio interface, still remains a challenge. In [7] it was shown how capacity of the UMTS network can be improved by embedding a peer-to-peer file sharing Internet protocol. Due to the specialized fixed-network cache peer, signalling and data traffic is displaced from the air interface to the core network, which consequently minimizes redundant traffic in the scarce radio interface. An interesting approach has been proposed in [6], where the traffic load imbalance in the UMTS uplink/downlink has been exploited. The idea of this study is to put an UTRATDD (Time Division Duplex) link into the underused UTRAFDD (Frequency Division Duplex) band. On the border of an UTRA-FDD macro cell an ad-hoc pico cell with MTs, operating in TDD mode, is organized. One of these MTs is designated to act as a gateway to the BS and, thus, to the backbone network. However, a shortcoming of the proposed algorithm might be the extensive dependencies between the two system modes and corresponding requirements of efficient coordination of their functionality (the amount of FDD uplink resources to be used for the TDD mode has to be determined). In our previous work [13] a cooperative concept was proposed, where the users, who are interested in fast downloading a popular content (new movie trailers or music files) form loosely coupled groups where the members directly cooperate with each other. The primary goal of the above mentioned work was to increase the efficiency of usage of the available

frequency spectrum, by shifting the non-real time traffic away from the downlink, utilizing the released capacity for providing better Quality of Service (QoS) for real-time services. This is accomplished by dynamically allocating m2m users to temporally underused uplink channels. For this purpose the popular file is divided into small logical packets, which are distributed packet by packet in arbitrary order among the m2m users in multiple groups on their own uplink channels in a multicast mode. It has been shown that the proposed cooperative m2m strategy significantly outperforms the conventional UTRAFDD mode for download of popular content. Only a pure m2m traffic scenario was considered in [13]. The algorithm was further extended to a more realistic scenario in [12], where a dynamic population of conventional UMTS speech users, operating on the same uplink frequency as the m2m users, has been considered. In both [13] and [12] an ideal packet scheduling was employed, assuming perfect knowledge of the current state of each file download performed in the neighborhood. However, finding an optimal realizable packet scheduling algorithm is a quite complex problem in a large scale distributed network. In such an environment the mobile terminals (MTs) have only local information about the transfers performed within their local groups and very incomplete knowledge about the global network state. To circumvent the mentioned above difficulties and to speed up the distribution of the packets, this paper presents an extended algorithm, where a new scheduling criterion for packet dissemination is introduced. In particular, we extend the m2m algorithm by incorporating network coding in the packet scheduling process. Network coding in general is an efficient packet distribution strategy [2], [3]. Each user in the network encodes all the packets he has already received by performing algebraic operations on them, treating Bytes of data packets as elements in a certain base field. A user forms a linear combination of these elements and relays the resulting packet, thus, increasing the information content of each transmission. Due to the random nature of the coefficients of the encoded packets, each encoded packet will most likely contain information that is useful for each user, improving dependability of packet distribution, consequently. This means that the performance of the system depends less on the scheduling mechanism and there is no need for a global scheduler-coordinator. Most of the previous work on network coding has focused on the theoretical aspects of this technique [11], [10], [4]. There is only a small number of publications available which evaluate the benefits of network coding for special applications. One of the most representative efforts to combine the theory of network coding and practical design of large scale networks

was introduced in [9], where the case of a wireless mesh network with unicast traffic was analyzed. Practical schemes were proposed in [5], where the gain achieved by using network coding in a fixed, large, unstructured overlay network with bidirectional unicast traffic was assessed. In [14] the mutual exchange of independent information between two nodes in an ad-hoc network is investigated. The main focus lies on duplex traffic streams, i.e., the nodes send packets to each other in opposite directions by using exactly opposite routing paths. The packets in the intermediate hops are XORed together and broadcast to their next hop. Network coding was performed only for a small subset of the forwarded packets. In [8] it was shown how network performance in terms of throughput can benefit from opportunistic coding (i.e., intelligent mixture of the packets) and listening. However, error-free wireless channels were assumed. Our contribution differs from previous work in the following aspects: • We consider a hybrid cellular based peer-to-peer network employing network coded data distribution. • Network Coding is applied in order to ease the problem of packet scheduling in the distributed dynamic environment of a wireless large scale network. We call our concept NC-m2m. • Instead of looking for the best scheduling with a simple packet replication we use network coding to obviate the need for centralized knowledge of the global state of the network. • Unlike other content distribution schemes, our approach takes into account the main features of UTRAN, i.e., collisions between users of different service classes creating wireless interference. • We demonstrate that the NC-aided m2m file sharing technique significantly outperforms our previously proposed m2m algorithm with a simple replicate-and-forward routing scheme in terms of download time. • We show that the network, supported by the NC-based m2m algorithm for file distribution, is quite dependable and robust to sudden departure (due to battery life, handover, loss of interest in the content) of file sharing participants. • Furthermore, in our extended m2m technique, significantly less radio resources as in the earlier simple m2m algorithm need to be spent in both downlink and uplink in order to achieve the same aggregate system throughput. II. N ETWORK C ODING A SSISTED M 2 M A LGORITHM A. Basic m2m System Model For the convenience of the later discussion, we first briefly revisit our basic replicate-and-forward m2m algorithm. Then, we formulate the extended assumptions and model characteristics necessary for embedding network coding based scheduling of the packets in our file transfer concept in cellular radio networks like UMTS1 . • Upon arrival, each new user, who is interested in downloading a popular content, establishes contact to the BS/RNC (Radio Network Controller) in order to get an authorization to participate in cooperative file transfer and 1 Although we focus on the FDD mode of WCDMA, our solution is general enough to fit into a variety of networks.

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to get a random packet of the desired file. Thus, none of the users has the complete file, but only a small set of single original packets. A user which received a packet from the BS acts as a server for that particular packet. The packet transfer is performed on the user’s own currently not used uplink carrier frequency and receivers switch to listen on the uplink. Without loss of generality we assume that the mobile terminals are able to receive on both uplink and downlink channels. In order to reduce the number of transmissions of identical packets on the network links and in turn to increase the efficiency of the uplink bandwidth usage, the m2m data transfer is performed in multicast mode. Such a parallel packet downloading strategy additionally increases the service probability, in terms of number of simultaneously served m2m users. To avoid collisions on the uplink channels and to minimize the interference among m2m users, the MTs are organized into multiple groups, based on their locations and radio propagation conditions. The size of the groups is limited. More details can be found in [13], [12]. The BS supports MTs with the signalling information and allows only one MT within the group to transmit in a given time interval, avoiding collisions within a particular group. Additionally, due to the low antenna heights of both, transmitter and receiver, and their limited transmit power the inter-group interference is minimized as well. Identification of the sender is done via a unique scrambling code. In forming a wireless cooperative community, as done here, groups must be periodically updated and reshaped in order to check the new positions of MTs and their radio propagation characteristics. Information about the link quality can be derived from, e.g., "Hello" packets, periodically transmitted by MTs.

B. Extended Network Coding Assisted m2m Model: Characteristics and Assumptions In our basic m2m concept the data exchange policy was based on a local "most-utile packet" distribution scheme [13], [12]. For this, it was assumed that the users make a packet scheduling decision based on the local information about packets available in the neighborhood (info is periodically sent by m2m users in "Hello" packets). However, such an assumption is quite unrealistic in a large distributed wireless network. Moreover, this packet distribution scheme has much redundancy, when considering the global state of the whole network. Since the "most-utile" packet exchange policy is employed in each group independently it could happen that the most beneficial packet in a particular group is well mapped in other groups, i.e., in the rest of the network, and the local scheduling decision will be suboptimal therefore. • In order to minimize redundancy of the packet distribution and to enhance the efficiency of intra-group multicast sessions the simple replicate-and-forward packet routing policy, proposed in our previous work, is replaced by network coding (NC). • In our new version of the m2m file sharing algorithm, m2m users independently combine original packets they

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have already got via algebraic operations and send the corresponding results to their group neighbors. Due to the random nature of the encoded packets coefficients, any randomly picked packet processed by NC will most likely contain information that is useful for each of the m2m participants in the group. The data exchange algorithm determines the sender candidate at random. There is no need for a scheduler any more. The coding scheme is assumed to be predetermined. Coefficient vectors specifying the generated linear combinations are sent with each data packet, allowing decoding at the MTs.

C. NC-m2m Algorithm To illustrate the main concept of the extended NC-m2m algorithm, consider the example in Figure 1. Mobile terminals voluntarily participate in file sharing via direct mobile-to-mobile data transfer with the purpose to reconstruct the original popular content. In each step, the respective user sends the linear combination of all packets he has received so far.

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NC-m2m Concept (for simplicity,

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The following Initialization procedure is performed: Each active m2m user creates two zero matrices X = 0m,k , G = 0m,m , where m is the number of logical packets, the source file is divided into, and w is the length of a logical packet in number of Bytes. X will be called the information matrix or decoding matrix. It stores the received encoded data (vectors) and is used for decoding later on. We call G encoding matrix. In this matrix the corresponding coefficient vectors will be stored. The rank, ν, of G is initialized with zero. Initially, none of m2m users has any information (packets) of the popular content; the logical packets are available in the core network only. The original packets are periodically distributed by the BS packet by packet to active m2m users

in order to generate one complete copy of the content within a radio cell. The following algorithm is executed: Step 1: The packet dj (particular j ∈ {1, . . . , m}) received by a MT from its BS is stored in the first all-zero row ν +1 of X (Xν+1,l = dj,l , l = 1, . . . , w). The corresponding row ν + 1 in G (Gν+1,j = gj , j = 1, . . . , m) will be a unit vector with the 1 at the ith position, which means that the currently received information vector is not encoded. Step 2: An MT, which is entitled to send in the current frame2 , uses the information stored in X to create a new encoded packet x0 . For results shown in this work all operations are done in the finite field 28 . In general, for the network under consideration the field size must be sufficiently large [3]. The MT-sender has to execute the following steps: • Create a vector g of uniformly distributed random coefficients gi , where i = 1, . . . , ν and gi ∈ 28 . • Compute the new P encoded vector, which we call encoded packet, x0 = νk=1 gk · xk where xk is the kth row of matrix X. The encoding is done separately for each Byte position in a packet, using the same encoding vector. 0 • x is broadcast to the group members together with the 0 corresponding encoding vector g0ν+1 , where gν+1 = g1 · 0 0 0 g1 + g2 · g2 + . . . + gν · gν . The MT-receiver(s) has to execute the following steps: Step 3: If a packet x0 has been received by a MT, it is stored in the row ν + 1 of X, and the corresponding encoding vector g0 is stored in G. G is transformed to lower triangular form, using Gaussian elimination. If the received packet was innovative, the number of non-zero row in G and therefore ν will increase by one. If ν = m (G has full rank) the user has collected enough linearly independent combinations to completely reconstruct the original file. Thus, file reconstruction can be accomplished by simply solving a system of linear equations. Step 4: Users, which have not found any useful packets within a specified time interval, try to connect to the BS for packet delivery. Additionally, our elimination routine outputs the IDs of all packets, dj , which are guaranteed to increase ν and therefore can be ordered in Step 4. All encoding/decoding operations are performed in the same way for each m2m user. The network coding scheme implemented in our algorithm is linear, which makes encoding/decoding procedure quite simple. III. R ESULTS AND D ISCUSSION In this section we present the most relevant simulation results. A. Simulation environment To obtain performance results and to demonstrate the effectiveness of the proposed network coding based m2m technique, we have developed a simulation model based on realistic assumptions. Our reference scenario consists of a UMTS network supported by m2m data transmission mode. We consider mixed 21

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traffic scenarios, where conventional UMTS speech users coexist with m2m file sharing participants. The simulated network is divided into regular hexagonal cells. Since the main focus of our analysis lies in the optimization of data availability to users in hotspots, e.g. airport lounges, railway stations, shopping malls, the radius of the cell is assumed to be 50 m. • We simulate a population of m2m users with Poisson distributed service requests, that are interested in downloading a popular file. Each user moves independently with the same speed. In our simulation, the minimal and maximal speed were 3 m/s and 30 m/s,respectively. • The size of the mentioned above content is 40 Kbyte; content is distributed with the cooperative m2m strategy. • Users dynamically join and leave the group at any time due to battery life, handover. They can also leave the system before they have finished the download process (e.g. loss of interest in the content), representing a relatively loosely coupled formation. • The size of the m2m groups is restricted to 7 members.

B. Traffic scenario We study the following traffic scenarios: 1) Scenario 1: The number of speech users per cell is kept constant (approx. 3 speech user per cell), while the load of m2m users is varied (low (10 users/cell), medium (30 users/cell), high (50 users/cell)). All users are assumed to be pedestrian. 2) Scenario 2: The amount of speech traffic varies proportionally to that of the m2m traffic, with the average number of speech users being 20% of the average number of m2m users in a cell. All users are assumed to be pedestrian. 3) Scenario 3: The same intention as the previous setting with 20% speech users load, increasing the velocity of m2m users from 3 m/s to 30 m/s. 4) Scenario 4: The same as Scenario 2, but with dynamic arrivals of new m2m users. The speech users operate on the same uplink frequencies as m2m participants, composing a potential source of m2m signal disturbance. The transmission in conventional mode complies with 3GPP specifications for UTRA-FDD [1]. • During the simulation all m2m users in the network use the same packet distribution policy, either transmission of original packets or dissemination of encoded packets using linear network coding. • Network coding is done only at the MTs, who participate in the m2m file sharing. • In order to avoid interference from MTs transmitting in m2m mode on other signals at the BS receiver, the transmit power is set to the minimum, which is -44 dBm according to 3GPP specifications [1]. • Without loss of generality, we assume that the transmission rate is 1 packet/frame. Thus, depending on the coding scheme and spreading factor the packet length becomes appropriately large. • The characteristics of the wireless channel in each group can vary from slot to slot (fast fading) due to the crosstraffic users, which, as we have already mentioned before, transmit on the same uplink frequency. • Information about the quality of the multicast signal within the group is obtained based on the ratio of the

average received power of the useful signal to that of all relevant interfering signals (C/I) on a slot-by-slot basis. We collect data framewise. Network performance for mentioned above scenarios is evaluated in terms of usage of network resources. The benefits are an improved system throughput, released overall downlink/uplink capacity and reduced file download time. C. Performance Measures We are essentially interested in the distribution of the file for users in different states of the download progress. We evaluate the system performance according to the following measures: • Overall downlink/uplink throughput gain: data volume reduction in the downlink/uplink. • Service probability gain: increase in the number of served users (%). • Download time gain: download time reduction. We define the download time as the time window, in which the user receives the complete file. The criterion for the download time gain is the 90% quantile of finished downloads in the system. The most important simulation parameters are summarized in Table I. TABLE I M AIN SIMULATION PARAMETERS . Traffic and environmental settings Traff. load (max. num. of m2m users/cell) Maximum m2m group size Cell radius Moving process for MT User profile Radio interface and algorithmic settings File size Required data rate Max. user data rate with 1/2-rate cod. Size of logical packets for m2m data Receiver sensitivity Transmission power in m2m mode Eb /N0 target Inner loop power control for m2m sender Simulation step

30 (medium), 50 (high) 7 50 m Gaussian Random Walk Pedestrian, Vehicular 40 KByte 60 kb/s 30 kb/s 225 Bit (coded) -112 dBm -44 dBm 3 dB OFF 1 radio frame (0.01 sec)

D. Simulation results In this section we present numerical results to highlight the effectiveness of the proposed NC-m2m technique. Comparison of simple m2m algorithm with NC-m2m file dissemination: We first study the situation where MTs depart from the system immediately after finishing their downloads and we don’t consider arrivals of new users. All users are assumed to be pedestrians. We start our analysis with Figure 2, where we compare the performance of a UMTS network, supported by the NCm2m algorithm to that of a system with a simple replicateand-forward m2m algorithm, where the "most utile-packet" scheduling policy was used. The upper graphs of Figure 2 demonstrate the benefit of the NC-m2m file distribution mechanism for medium traffic load (Scenario 1) and high traffic load (Scenario 2) in terms of the download time reduction. The bottom graphs depict the performance gain of the NC-m2m algorithm in terms of released downlink resources. The results show that the NC-m2m algorithm performs significantly better than m2m cooperative schemes without employing NC. In particular, we

Comparison of download times (medium m2m load, scenario 1) NC−m2m / users leaving m2m / users leaving gain: 32%

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Fig. 2. Performance comparison of simple forward-and-replicate-m2m file transfer for medium traffic load, Scen. 1 (left) and high traffic load, Scen. 2 (right).

observe that the users experience a reduction of the download time for the complete file of up to 32% in case of NC. The reason is that each encoded packet can be useful to any user. Extended NC-m2m file sharing policy: We investigate now a more strict file sharing policy. Namely, the finished users have to stay in the system for a certain period of time allowing the m2m algorithm to continue sharing the file with not yet finished users. The observations, visualized in Figure 3, indicate that if we encourage the users, who have completed their downloads, to behave "altruistically" by staying online for a while, this can help the other file sharing participants in the last states of the download progress to speed up the reception of their still missing packets. It is quite evident, that such a modified policy gives still not finished users better opportunity to get missing packets directly from staying completed users, releasing, in turn, more BS capacity. The lack of finished users’ willingness to help can be mitigated by offering e.g. a bonus system. Impact of m2m users’ velocity: We increased the users’ speed from 3 m/s to 30 m/s (Scenario 3) in order to investigate

the influence of user mobility on the performance of the proposed NC-m2m algorithm. The results indicate a significant effect of the user mobility on the system performance. We observe a substantial gain of up to 33% in download time reduction in case of fast moving MTs compared to MTs with low velocity in a UMTS system with medium traffic load (see Figure 4). This is reasonable, because the higher the mobility of the users, the more frequently the groups are updated and reshaped. Thus, the probability for some user to find packets of interest during a short time interval is increased. System in the steady state: dynamic users’ arrivals: The results presented in the previous subsection analyze the performance of the NC-m2m algorithm for a snapshot simulation. In this section we investigate the efficiency of the NCm2m scheme in a network scenario with dynamic new users’ arrivals (Scenario 4). Users join and leave the network, but the population of the system remains approximately constant. The number of reserved downlink channels in the steady state of the system versus time is depicted in Figure 5. Some numerical values for the overall downlink throughput gain with respect to amount of data which had to be sent via

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Fig. 3. Impact of the cooperative behavior of finished users on the download times, medium traffic load, Scenario 2.

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the downlink channels in order to distribute the data file of 40 Kbyte size to the users within one cell are presented in Table II. Three packet distribution strategies have been compared: the simple replicate-and-forward m2m algorithm and the NCbased m2m scheme w/o and with finished "helpers" (the finished users stay in the system for a certain period of time and help to speed up the distribution of missing packets to still not finished users). Again, the network, where the finished users stay in the system for a certain period of time performed better. TABLE II OVERALL DOWNLINK THROUGHPUT GAIN , S CENARIO 1. Load Data vol. in DL in m2m mode (kB/cell) Data vol. in DL in NC-m2m mode (kB/cell) Data vol. in DL in NC-m2m mode (stay) (kB/cell)

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Another performance indicator of interest is the released

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Fig. 6. Impact of the NC-m2m technique on the uplink resource consumption, medium traffic load.

uplink capacity. Figure 6 shows the positive impact of the NCm2m technique on the uplink resources consumption. Clearly, when NC is applied each m2m-sender in a group saves a number of transmissions, by combining original packets into network-coded packets, which most probably can be useful to any user in the group. IV. C ONCLUSIONS In the present work we have investigated the performance of a system in terms of improvement in information distribution by generalizing traditional scheduling paradigms. This work advocates the extension of a UMTS based cooperative network supported by the m2m data transmission mode by employing network coding. Network coding is incorporated in order to solve the scheduling problem in the distributed dynamic environment of wireless large-scale networks. We have been especially interested in the distribution of the file download times for m2m users in different states of the download progress.

Our work reveals the following findings: • The NC-m2m technique ameliorates the benefits of the m2m solution, obviating the need for proper scheduling for non-real time file sharing applications. • The expected file download time is improved by more than 33% with network coding compared to replicateand-forward packet transfer. • The radio capacity of the network is released by up to 30% for both downlink and uplink. • The NC-m2m solution is much more robust to sudden departure of m2m users (due to handover, battery life, loss of interest in the content). These conclusions demonstrate that NC assisted m2m data transfer is a very efficient and quite dependable way of dissemination of popular content like new movie trailers or music files across dynamic large scale wireless networks, especially in a hotspot environment with increased users’ demand of data availability, and even when only a few users in the cell have the complete file. REFERENCES [1] UE Radio Transmission and Reception. 3GPP TS 25.101. [2] R. Ahlswede, N. Cai, S. Y. R. Li, and R. W. Yeung. Network information flow. IEEE Trans. Information Theory, 46, July 2000. [3] C. Fragouli, J. Y. Le Boudec, and J. Widmer. Network Coding: An Instant Primer. Technical report, LCA-Report-2005-010, 2005. [4] C. Fragouli and E. Soljanin. Information Flow Decomposition for Network Coding. IEEE Trans. of Information Theory, 52, March 2006. [5] C. Gkantsidis and P. R. Rodriguez. Network Coding for Large Scale Content Distribution. In Proc. of IEEE INFOCOM, Miami, FL, USA, 2005. [6] H. Haas and G. J. R. Povey. A Capacity Investigation on UTRA-TDD Utilising Underused UTRA-FDD Uplink Resources. In IEE Colloquium on UMTS Terminals and Software Radio, Glasgow, UK, April 1999. [7] T. Hossfeld, K. Tutschku, and F. Andersen. Mapping of Filesharing onto Mobile Environments: Enhancement by UMTS. In Proc. of IEEE Pervasive Computing and Communications (PerCom), Kauai Island, Hawaii, March 2005. [8] S. Katti, D. Katabi, W. H. H. Rahul, and M. Medard. The importance of being opportunistic: Practical network coding for wireless environments. In Proc. of 43rd Annual Allerton Conference on Communication, Control and Computing, Monticello, IL, USA, September 2005. [9] S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft. Xors in the air: Practical wireless network coding. In Proc. of Conference on Applications, technologies, architectures, and protocols for computer communications, (SIGCOM), New York, NY, USA, 2006. [10] R. Koetter and M. Medard. Beyond Routing: An Algebraic Approach to Network Coding. In Proc. of IEEE INFOCOM, New York, USA, 2002. [11] Y. R. Li, R. W. Yeung, and N. Cai. Linear Network Coding. IEEE Trans. of Information Theory, 46, July 2000. [12] L. Popova, T. Herpel, and W. Koch. Efficiency and Dependability of Direct Mobile-to-Mobile Data Transfer for UMTS Downlink in MultiService Networks. In Proc. of IEEE Wireless Communications & Networking Conference (WCNC), Hong Kong, March 2007. [13] L. Popova, T. Herpel, and W. Koch. Enhanced Downlink Capacity in UMTS supported by Mobile-to-Mobile Data Transfer. In Proc. of 6th International IFIP-TC6 Networking Conference, Springer, LNCS 4479, Atlanta, GA, USA, May 2007. [14] Y. Wu, P. A. Chou, and S. Y. Kung. Information Exchange in Wireless Networks with Network Coding and Physical-layer Broadcast. Technical report, MSR-TR-2004-78, 2004.