Multi-Resolution Broadcast/Multicast Systems for MBMS - CiteSeerX

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IEEE TRANSACTIONS ON BROADCASTING, VOL. 53, NO. 1, MARCH 2007

Multi-Resolution Broadcast/Multicast Systems for MBMS Américo M. C. Correia, João C. M. Silva, Nuno M. B Souto, Luísa A. C. Silva, Alexandra B. Boal, and Armando B. Soares

Abstract—Multimedia Broadcast/Multicast Service (MBMS) supports downlink streaming and download-and-play type services to large groups of users. From the radio perspective, MBMS includes point-to-point (PtP) and point-to-multipoint (PtM) modes. This paper investigates and presents different multi-resolution broadcast systems for Wideband Code Division Multiple Access (WCDMA) cellular mobile networks, namely, multi-code, hierarchical QAM constellations and multi-antenna (MIMO) systems. Each one present performance gains over conventional single-resolution broadcast systems. A comparison is made between the three proposed multi-resolution systems. The use of High Speed Downlink Packet Access (HSDPA) to multicast video streaming as a multi-resolution system, associated or not to MIMO, can be employed by the MBMS PtP mode, but dependently on the deployment scenarios it can yield substantial reduction in resource demand and optimization of the allocated radio resources. Index Terms—Hierarchical constellations, MBMS, MIMO, multi-code, multi-resolution.

I. INTRODUCTION

T

HERE is still a lot of investigation in ways to improve the delivery of multimedia information. The multimedia paradigm has put pressure in resources optimization, and sharing channels is one of the most important aspects in network optimization. Efficient network resources usage should be the leverage for near-coming multimedia applications. Besides that, in order to guarantee scalability, multi-resolution schemes have to be considered in UMTS (Universal Mobile Telecommunications Systems) environments. Multimedia Broadcast and Multicast Service (MBMS), introduced by 3GPP in Release 6 is intended to efficiently use network/radio resources (by transmitting data over a common radio channel), both in the core network and, most importantly, in the air interface of UTRAN (UMTS Terrestrial Radio Access Network), where the bottleneck is placed to a large group of users. Manuscript received June 22, 2006; revised December 5, 2006. This work was supported in part by the European Commission project: IST-2005-27423, “Advanced MBMS for the Future Mobile World,” C-MOBILE, http://c-mobile. ptinovacao.pt. A. M. C. Correia is with the Instituto de Telecomunicações, Lisboa 1049-001, Portugal, and the Associação para o Desenvolvimento das Telecomunicações e Técnicas de Informática, Edifício ISCTE, Lisboa 1600-082, Portugal (e-mail: [email protected]; [email protected]). J. C. M. Silva, and N. M. B. Souto are with the Instituto de Telecomunicações, Lisboa 1049-001, Portugal. L. A. C. Silva, A. B. Boal, and A. B. Soares are with the Associação para o Desenvolvimento das Telecomunicações e Técnicas de Informática, Edifício ISCTE, Lisboa 1600-082, Portugal. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBC.2007.891705

MBMS includes Point-to-Point (PtP) and Point-to-Multipoint (PtM) modes. The former allow individual retransmissions but the latter does not. MBMS is targeting high (variable) bit rate services over a common channel. One of the most important properties of MBMS is resource sharing among many User Equipments (UEs), meaning that many users should be able to listen to the same MBMS channel at the same time. So, power should be allocated to this MBMS channels for arbitrary UEs in the cell to receive the MBMS service. For broadcast and multicast transmissions in a mobile cellular network, depending on the communication link conditions some receivers will have better signal to noise ratios (SNR) than others and thus the capacity of the communication link for these users is higher. Cover [1] showed that in broadcast transmissions it is possible to exchange some of the capacity of the good communication links to the poor ones and the trade-off can be worthwhile. Possible methods to improve the efficiency of the WCDMA network are the use of multiple codes or multiple antennas both at transmitter and receiver (MIMO) or non-uniform signal constellations (also called hierarchical constellations). Each one is able to provide unequal bit error protection. In any case there are two or more classes of bits with different error protection, to which different streams of information can be mapped. Depending on the channel conditions, a given user can attempt to demodulate only the more protected bits or also the other bits that carry the additional information. Several papers have studied the use of non-uniform constellations for this purpose [1], [3]. Non-uniform 16-QAM and 64-QAM constellations are already incorporated in the DVB-T (Digital Video Broadcasting—Terrestrial) standard [4]. To accomplish high data rates over wireless links the use of multiple transmit and receive antennas (MIMO) is an alternative that does not require any extra bandwidth at all. Also in this paper we will analyze MIMO associated or not to HSDPA, as another radio resource management technique to provide multiresolution. The HSDPA mode [5] has been standardized for UMTS providing bit rates up to 10 Mbps on a 5 MHz carrier for the best effort packet data services in the downlink. HSDPA supports new features that rely on, and are tightly coupled to, the rapid adaptation of transmission parameters to instantaneous radio conditions. The specific feature for multi-resolution is: — Fast Link Adaptation: Instead of compensating the variations of downlink radio conditions by means of power control, the transmitted power is kept constant and the modulation and coding of the transport block is chosen every for each user, Transmission Time Interval

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Fig. 1. Scalable video transmission.

this is called Adaptive Modulation and Coding (AMC). To users in good conditions, 16-QAM can be allocated to maximize throughput, while users in bad conditions are penalized on throughput, reaching a point to which the service can be denied. Up to thirty different channel quality indicators (CQIs) are employed associated to corresponding different bit rates. The HSDPA as a means to deliver MBMS multi-resolution video streaming will be studied with suitable packet scheduler algorithms that try to guarantee the same bit rate to the users in order to offer a good fairness and capacity. In Section II multi-resolution broadcast is presented including the multi-code packet scheduling, Section III describes hierarchical (or non-uniform) QAM constellations. MIMO systems are introduced in Section IV as another multi-resolution technique. In Section V the HSDPA mode is presented and related to a multicast multi-resolution system, Section VI presents simulation results. Finally conclusions are drawn in Section VII. II. MULTI-RESOLUTION BROADCAST The introduction of multi-resolution in a broadcast cellular system deals with source coding and the transmission of the output data streams. In a broadcast cellular system there is a heterogeneous network with different terminals capabilities and connection speed. For the particular case of video, there are several strategies presented in the literature to adapt its content within a heterogeneous communications environment [6]–[9]. In this paper we choose scalable media. Scalable media (see Fig. 1) [8], provides a base layer for minimum requirements, and one or more enhancement layers to offer improved qualities at increasing bit/frame rates and resolutions. This method therefore significantly decreases the storage costs of the content provider. Common scalability options are: temporal scalability, spatial scalability and SNR scalability. Spatial scalability and SNR scalability are closely related, the only difference being the increased spatial resolution provided by spatial scalability. SNR scalability implies the creation of multi-rate bit streams. It allows for the recovery of the difference, between an original picture and its reconstruction. This is achieved by using a finer quantizer to encode the difference

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picture in an enhancement layer. This additional information increases the SNR of the overall reproduced picture; hence, the name SNR scalability. Spatial scalability allows for the creation of multi-resolution bit streams to meet varying display requirements/constraints for a wide range of clients. It is essentially the same as in SNR scalability, except that a spatial enhancement layer here attempts to recover the coding loss between an up-sampled version of the reconstructed reference layer picture and a higher resolution version of the original picture. Besides being a potential solution for content adaptation, scalable video schemes may also allow an efficient usage of power resources in MBMS, as suggested in [1]. This is depicted in Fig. 1, where two separate physical channels are provided for one MBMS service (e.g., @ 256 kbps)—one for the base layer, at half bit rate of the total bit rate (128 kbps), and with a power allocation which can cover whole cell range; one for the enhanced layer, also at half bit rate of the total bit rate (128 kbps), but with less power allocation than that of the base layer. A. Multi-Code A flexible common channel, suitable for point-to-multipoint transmissions is already available in UMTS networks, namely the Forward Access Channel (FACH), which is mapped onto the Secondary Common Control Physical Channel (S-CCPCH). In [11], it was shown that, without macro diversity combining, about 40% of the sector total power has to be allocated to a single 64 kbps MBMS if full cell coverage is required. This makes MBMS too expensive since the overall system capacity is limited by the power resource. To make MBMS affordable for the UMTS system, its power consumption has to be reduced. If MBMS is carried on S-CCPCH, there is no inner-loop power control. Assuming that macro diversity combining is not used, extra power budget has to be allocated to compensate for the receiving power fluctuations. Our approach is to consider MBMS video streaming as scalable, with one basic layer to encode the basic quality and consecutive enhancement layers for higher quality. Only the most important stream (basic layer) is sent to all the users in the cell to provide the basic service. The less important streams (enhancement layers) are sent with less amount of power or coding protection and only the users who have better channel conditions are able to receive that additional information to enhance the video quality. This way, transmission power for the most important MBMS stream can be reduced because the data rate is reduced, and the transmission power for the less important streams can also be reduced because the coverage requirement is relaxed. The first studied transmission scheme uses a single rate ), stream (single spreading code with spreading factor which is carried on a single 256 kbps FACH channel and sent to the whole area in the cell. The second scheme uses a double streaming transmission, i.e., two data streams (two orthogonal spreading codes with ), each FACH with 128 kbps where basic information for basic QoS is transmitted with the power level needed to cover the whole cell, and the second stream conveys additional information to users near the Node B (base station).

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TABLE 1 QoS REGIONS PARAMETERS

TABLE 2 URBAN MACRO CELLULAR PARAMETERS

1) System Model: The system model (illustrated in Fig. 1), consists of two QoS regions, where the first region receives all information while the second region receives the most important data. The QoS regions are associated to the geometry factor that reflects the distance of the UE from the base station antenna. The interference geometry factor is defined as the ratio of interference generated in generated in the own cell to the the other cells plus thermal noise, i.e, (1) In Table 1, are shown the values chosen. For the first region the geometry factor is and for the second region . Based on the chosen urban macro cellular topology (see Table 2) used in our radio subsystem level simulations these two geometries values correspond to cell coverage of at least 60% and 80%, respectively. UE2 will receive the most important data (transmitted at 128 kbps) to get a basic video quality service, whereas UE1 will receive all the data to provide a higher quality reproduction of the input video. Actually, UE2 will also receive both data stream layers but it is not sure that the block error rate of the received

enhancement layer will be bellow 0.01 and as a result there will be a reduction in the quality of the video received by this user. III. HIERARCHICAL (NON-UNIFORM) QAM CONSTELLATIONS Another transmission method which provides a multi-resolution system is the use of hierarchical (non uniform) constellations. In this study we consider the use of 16-QAM non-uniform modulations for the transmission of broadcast and multicast services in WCDMA systems. For 16-QAM two classes of bits are used. Some modifications to the physical layer of the UMTS system to incorporate these modulations were already proposed in [12], [13]. 16-QAM hierarchical constellations are constructed using a main QPSK constellation where each symbol is in fact another QPSK constellation, as shown in Fig. 2. The bits used for selecting the symbols inside the small inner constellations are called weak bits and the bits corresponding to the selection of the small QPSK constellation are called stronger bits. The idea is that the constellation can be viewed as a 16-QAM constellation if the channel conditions are good or as a QPSK constellation otherwise. In the latter situation the received bit rate is reduced to half. The main parameter for

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Fig. 2. Signal constellation for 16-QAM non-uniform modulation.

Fig. 3. Proposed transmitter chain.

defining one of these constellations is the ratio between as shown in Fig. 2:

and (2)

Each symbol of the constellation can be written as: (3) If the resulting constellation is a uniform 16-QAM. When is lower than 0.5, the bit error rate (BER) of the stronger bits decreases but since the BER of the weaker symbols increases the overall BER also increases. Fig. 3 shows a simplified transmission chain incorporating 16-QAM hierarchical constellations. In this scheme there are 2 parallel processing chains, one for the basic information stream and the other for the enhancement information. The channel estimation is performed using the pilot channel (named CPICH in UMTS) which is transmitted in parallel to the data channels, using a reserved orthogonal variable spreading factor (OVSF) code. At the receiver, the modulation is removed from the CPICH by multiplying it by its conjugate, which results in a sequence of noisy channel estimates. These noisy channel estimates are then passed through a moving average filter and the filtered sequence can be interpolated (or decimated) to match the rate of the data channels. IV. MIMO AS A MULTI-RESOLUTION SYSTEM Multiple Input Multiple Output (MIMO) schemes are used in order to push the capacity and throughput limits as high as possible without an increase in spectrum bandwidth, although

there is an obvious increase in complexity. The capacity limit of any CDMA system is taken to be the resulting throughput obtained via the usage of the maximum number of codes. As the codes used are orthogonal to each other there is a strict limit in its maximum number. However, if multiple transmit and receive antennas are employed, the capacity may be raised due to code re-usage across transmit antennas. If there are a sufficient number of receive antennas, it is possible to resolve all data streams, as long as the channel correlation between antennas transmit and receive antennas, we have isn’t too high. For the capacity equation [14], [15], (4) where is the identity matrix of dimension , is is the transpose-conjugate of and the channel matrix, is the SNR at any receive antenna. Foschini [14] and Telatar [16] both demonstrated that the capacity grows linearly with , for uncorrelated channels. Therefore, it is possible to employ MIMO as a multi-resolution distribution system where the concurrent data streams are and received by different antransmitted by tennas. The downside to this is the receiver complexity, sensitivity to interference and correlation between antennas, which is more significant as the antennas are closer together. For a UMTS system, it is inadequate to consider more than 2 or 4 antennas at the UE/mobile receiver. Note that, unlike in CDMA where user’s codes are orthogonal by design, the resolvability of the MIMO channel relies on the presence of rich multi-path which is needed to make the channel

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Fig. 4. MIMO receiver scheme.

spatially selective. Therefore, MIMO can be said to effectively exploit multi-path. The receiver for such a scheme is obviously complex due to the number of antennas, users and multi-path components. Different receivers were analyzed in [17], [18], in order to establish the trade-off between performance and complexity for such systems. Two strategies were discussed; the Equalization and MRC (Maximum Ratio Combining) Based receivers. The main difference between both strategies is the fact that the equalization receivers operate on the whole block at once whereas MRC receivers work on tap/finger level, combining the taps later to form an estimate for the symbols. Fig. 4 illustrates the main blocks from which the receiver is compiled [17], [18]. The MIMO receiver block is a matched filter receiver which, in conjunction with the main system matrices (amplitudes per user and link, channel parameters per user and link, and spreading codes), provides the necessary data for the minimum mean square error (MMSE) operation (receiver algorithm). V. HSDPA TRANSMISSION AS A MULTI-RESOLUTION MULTICAST TECHNIQUE A flexible shared channel, suitable for MBMS PtP transmissions is currently available, namely, the High Speed Downlink Shared Channel (HS-DSCH), which is mapped onto the High Speed Physical Downlink Shared Channel (HS-PDSCH) (see [23]). HSDPA supports new features that rely on, and are tightly coupled to, the rapid adaptation of transmission parameters to instantaneous radio conditions. These features are: 1) Fast Link Adaptation: Instead of compensating the variations of downlink radio conditions by means of power control, the transmitted power is kept constant and the modulation and coding of the transport block is chosen for each user, this is called Adaptive every Modulation and Coding (AMC). To users in good conditions, 16-QAM can be allocated to maximize throughput, while users in bad conditions are penalized on throughput, reaching a point to which the service can be denied. There is obviously a return channel that indicates the quality of each link. 2) Fast Channel-Dependent Scheduling: The scheduler determines to which terminal the shared channel transmission should be directed at any given moment. The term channeldependent scheduling signifies that the scheduler considers

instantaneous radio-channel conditions. This greatly increases capacity and makes better use of resources. The basic idea is to exploit short-term variations in radio conditions by transmitting to terminals with favourable instantaneous channel conditions. 3) Fast Hybrid-ARQ with soft-combining: The terminal (user equipment, UE), can rapidly request retransmission of erroneously received data, substantially reducing delay and increasing capacity (compared to 3GPP Release 99). Prior to decoding, the terminal combines information from the original transmission with that of later retransmissions. This practice called soft-combining, increases capacity and robustness. All three features are useful for providing multi-resolution, in this paper we will concentrate only on the first one. Because we have already considered the use of hierarchical 16-QAM modulation and multi-code as multi-resolution techniques in this section we will only consider adaptive coding as another multi-resolution technique. Here, the first six channel quality indicators (CQIs) values of the CQI mapping table [23] will be considered due to use of a single HS-PDSCH (one code and QPSK modulation) with similar bit rates of the MBMS PtM transmission channel (from 64 kbps up to 256 kbps). Streaming video is the most expected MBMS service. The QoS constraint, for such service, is often defined by the maximum tolerable delay, which directly translates into the play-out buffer size at the mobile receiver. In our study, a packet that includes basic layer enhancement layers is fragmented into frames (data streams) of varying sizes due to adaptive coding. For instance, CQI 1 (transport block size of 137 bits) only carries the enhancement layer and plenty of redundant bits (introduced later by the channel encoder). CQI 6 (transport block size of 461 bits) carries the base layer plus five enhancement layers with much less redundant bits. As a result there is a different energy distribution used for each of the six layers [21]. In [19], [24] is presented how the scheduling is performed with performance results of throughput, jitter and probability of receiver buffer underflow of different schedulers. In this paper we only consider that based on the users reported CQI in the uplink they will receive packets with the corresponding transport block size. VI. SIMULATION RESULTS Typically, radio network simulations can be classified as either link level (radio link between the base station and the user terminal) or radio network subsystem system level. A single approach would be preferable, but the complexity of such simulator—including everything from transmitted waveforms to multi-cell network—is far too high for the required simulation resolutions and simulation time. Therefore, separate link and system level approaches are needed. The link level simulator is needed for the system simulator to build a receiver model that can predict the receiver BLER/BER performance, taking into account channel estimation, interleaving, modulation, receiver structure and decoding. The system level simulator is needed to model a system with a large number of mobiles and base stations, and algorithms operating in such a system.

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TABLE 3 LINK LEVEL SIMULATION PARAMETERS

The considered interference is the sum of intra-cell and inter-cell interference. Both have a noise-like character. This is mainly due to the large number of sources adding to the signal, which are similar in signal strength. Link performance results are used as input by the system level simulator where several estimates for coverage and throughput purposes can be made by populating the scenario topology uniformly and giving users a random mobility. The estimates are made for every TTI being the packets that are received with a BLER over 1% considered to be well received. The estimate for coverage purposes are made for an average of five consecutive received packets, if the average received BLER of these packets is above the 1% BLER the mobile user is counted as being in coverage. For the throughput calculation the estimation is made based on each individual packet received with a BLER higher than 1%. Fig. 5. BLER vs Tx power for S-CCPCH different geometries and propagation channels.

A. Broadcast Multi-Resolution Results

The channel model used in the system level simulator considers three types of losses: distance loss, shadowing loss and multi-path fading loss. The model parameters depend on the environment. For the distance loss the COST-Walfisch-IkegamiModel, LOS and NLOS, from the COST 231 project was used. Shadowing is due to the existence of large obstacles like buildings and the movement of UEs in and out of the shadows. This is modelled through a process with a lognormal distribution and a correlation distance. The multi-path fading in the system level simulator corresponds to the 3GPP channel model, where the Pedestrian B and Vehicular A (3 km/h) environments were chosen as reference. The latter models are also used in the link level simulator. In the radio network subsystem (RNS) system level simulator only the resulting fading loss of the channel model, expressed in dB, is taken into account. The fading model is provided by the link level simulator through a trace of fading values (in dB), one per TTI. For each environment where the mobile speed is the same several series of fading values are provided for each pair of antenna.

As told previously for MBMS PtM transmissions the available channel is the Forward Access Channel (FACH), which is mapped onto the Secondary Common Control Physical Channel (S-CCPCH). Results for different multi-resolution systems will be next analyzed associated to this specific transport channel of UMTS. Table 3 presents parameters which are commonly used in the subsequent sections. 1) Multi-Code Results: Fig. 5 presents the link level S-CCPCH performance in terms of (dB) representing the fraction of cell transmit power necessary to achieve the corresponding BLER graduated on the vertical axis. For the the use of a single spreading code reference (bit rate of 256 kbps) imposes to with spreading factor less have a geometry factor of 0 dB in order to achieve than 80% ( 1 dB) considering the VehA propagation channel. This means that we can only offer such a high bit rate for users located in the middle of the cell, not near the border. The use of (bit rate of 128 kbps) two spreading codes each with transmitted with different power allows an increase of coverage and throughput as we will next confirm with

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Fig. 6. S-CCPCH average coverage vs Tx. power for 1 Radio Link (1RL).

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Fig. 8. BLER vs Tx power for 16-QAM strong and weak blocks of bits (SF = 16), k = 0:5.

Fig. 7. S-CCPCH average throughput vs Tx. power (1RL).

system level results. Remind that for multi-resolution using multi-code the base layer is transmitted with higher power and the enhancement layer with lower power . Here, we will assume that is available for the transmission of MBMS PtM mode, without any macro diversity combining. is presented. The In Fig. 6 the average coverage versus introduction of multi-code allows multi-resolution and an increase of average coverage for the same total transmitted power. With multi-code the aggregate bit rate of 256 kbps is achievable with two data streams of 128 kbps for Pedestrian B channel assuring model. One of them is transmitted with 62% coverage and the other is transmitted with offering 85% coverage. For the same total transmitted power a single stream of 256 kbps allows a coverage of 66%. Similar coverage values (58% and 81% for multi-resolution and 64% for single resolution) are obtained for the Vehicvalues. ular A propagation channel considering the same is presented. In Fig. 7 the average throughput versus Remind that blocks with errors are not retransmitted, in MBMS

PtM mode. Taking as reference the same values used and for comparison of coverage, namely, the Pedestrian B channel we will check that multi-code allows an increase of throughput. For the enhancement data stream assures average throughput 80 kbps and with the throughput of 110 kbps is achieved for the base data stream. The total throughput is 190 kbps for multiresolution. For the single resolution stream with the achieved throughput is 172 kbps. The throughput gain is around 10%. 2) Hierarchical Modulation Results: Fig. 8 presents an alternative way of offering multi-resolution where the bit rate is 256 kbps using non-uniform 16-QAM modulation and a single for and . spreading code with This case is more spectral efficient than the previous one without multi-resolution, presented in Figs. 6 and 7, because it uses a higher SF. An iterative receiver based on the one described in [12] is employed for decoding both blocks of bits. For the referthe difference of total transmitted ence value of power between the strong and the weak blocks is about 5.5 dB for either Vehicular A or Pedestrian B. This means that there is a substantial difference in coverage between the two data streams to assure the reference BLER. Fig. 9 presents average throughput and corresponds to Fig. 6. It is obvious that multi-resolution using hierarchical modulation provides higher throughput than with single resolution. Taking the achieved throughput the same reference for the Pedestrian B channel is 240 kbps. There is an obvious increase in throughput obtained with hierarchical modulation compared to multi-resolution by multi-code. The throughput gain is 28.3%, but there is the disadvantage of requiring a more complex receiver. 3) MIMO Results: Fig. 10 presents an alternative way of offering multi-resolution where the bit rate is 256 kbps using multiple transmitting and receiving antennas (MIMO) and a single for . The complex spreading code with correlation coefficients between antennas were taken from [20]. This case has the same spectral efficient than the previous one

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Fig. 9. 16-QAM average throughput vs Tx. power (1RL).

Fig. 10. BLER vs Tx power for SISO (1 is G = 3 dB.

0

2 1) and MIMO (2 2 2), the geometry

with multi-resolution, presented in Figs. 8 and 9, because it uses the same SF. However it provides higher coverage because the geometry is lower. The base and the enhancement layers are transmitted by different antennas. However, the results here presented only consider the case where the transmitted power per antenna is the same and equal to half of the single transmitting antenna case. If we had considered different transmitted powers per antenna then we would have a better BLER performance for the base layer compared to the enhancement layer stream. The receiver for both single resolution SISO (Single Input Single Output) and multi-resolution MIMO is the same described in [9], [10]. For the reference value of the difference of total transmitted power between the single resolution SISO (1 1) and the multi-resolution MIMO (2 2) schemes is less than 1 dB (Vehicular A) or equal to 1 dB (Pedestrian B). Considering that we are transmitting at 128 Kbps with single resolution and 256 kbps with multi-resolution we would expect the double of the transmitted power for the latter. However, we can conclude that the multi-resolution scheme is

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Fig. 11. Average throughput vs Tx. power (1RL).

Fig. 12. HSDPA BLER vs E =I

for vehicular A channel.

much more power efficient than the single resolution one for . Fig. 11 presents average throughput and corresponds to Figs. 7 and 9. It is obvious that multi-resolution using MIMO provides higher throughput than the previous multi-resolution techniques. However, the observation of the curves indicates that there is a decrease in the throughput of the enhanced layer compared to the base layer. For instance take Pedestrian . The base layer throughput is B channel and 128 kbps but the total throughput (base + enhancement) is around 240 kbps. Considering again the previous reference the achieved throughput for the Pedestrian B channel is 256 kbps. The throughput gain is 32.82% relative to . 256 kbps single resolution B. Multicast Multi-Resolution Results 1) HSDPA Results: Figs. 12 and 13 consider the HS-DSCH are prechannel, where BLER performance curves vs sented for the Vehicular A (3 km/h) and Pedestrian B (3 km/h), respectively. Only the first 6 CQIs with bit rates from 68.5 kb/s up to 230.5 kb/s (QPSK modulated), without and with MIMO

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TABLE 4

HSDPA W/WO MIMO,

Fig. 13. HSDPA BLER vs

E =I

BLER Target = 1%, E =I

for pedestrian B channel.

are considered. Multicast multi-resolution is achieved by adaptive coding (without MIMO) and by the association of adaptive coding with multiple transmitting and receiving antennas (with which correMIMO). The geometry chosen was sponds to average coverage greater or equal to 80%. It is obvious that depending on the position of the terminal more or less throughput is achievable for the same transmitted power. values can be derived in order to find the Signal to Interference Ratio (SIR) targets for each transmitted bit rate, in our value is obtained for a BLER of 1%, for MBMS case the services. Table 4 summarizes the Fig. 12. We conclude from the link level results of Table 4 that the required fraction of total transmitted power from the base station for the geometry to assure the reference increases a lot for increasing bit rates. However, there is when using MIMO an expected substantial reduction of (CQI 6) of for the same bit rates, e.g. compare (CQI 3) of MIMO. In our single antenna with assumptions we keep constant which means that only the first CQIs (low resolution) are supposed to offer high coverage according to Table 4. According to Table 4 it is the multi-resolution by the association of adaptive 2) that imposes more restrictions of coding and MIMO (2 coverage. The coverage and throughput performance are simulated by system level simulations for video streaming multicast

, FOR DIFFERENT CQI,

Fig. 14. HSDPA coverage vs

G = 03 dB

E =I

Fig. 15. HSDPA throughput vs

E =I

for vehicular A channel.

for vehicular A channel.

using HSDPA, the Vehicular A environment (3 km/h) and are presented in Figs. 14 and 15, respectively. As expected, for there are different average coverage for different bit rates. For the same bit rate (same multi-resolution) additional coverage is offered with MIMO (2 2). Not all the users will get the highest resolution but the lowest resolution will be with MIMO (2 2) or offered everywhere. For

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without it there is an increased average throughput only for the first 5 bit rates. It means that due to the coverage restrictions of its expected throughput the highest bit rate averaged by all users in the cell is not above 210 kb/s, the av. erage throughput of the previous resolution However for users with good channel conditions the highest (with MIMO) is an resolution offered by interesting option. VII. CONCLUSIONS In this paper we have analyzed several multi-resolution broadcast systems for Wideband Code Division Multiple Access (WCDMA) cellular mobile networks, namely, multi-code, hierarchical 16-QAM constellations and multi-antenna (MIMO) systems. In addition to the obvious advantage of multi-resolution compared to single resolution in terms of graceful degradation of quality each technique presented here offered coverage and throughput performance gains over conventional single-resolution broadcast systems. We have applied each multi-resolution broadcast system to the point to multipoint mode of MBMS. A comparison between the three specific broadcast multi-resolution systems indicates that multi-code is the one with less performance gain and has no spectral efficiency gain compared to single resolution (single code). Hierarchical 16-QAM modulation has the double spectral efficiency and higher performance gains than multi-code. MIMO with two transmitting and two receiving antennas has also the double spectral efficiency and offers the highest gains. However the expected capacity gains that MIMO and hierarchical 16-QAM schemes provide require more complex receivers than multi-code or single resolution. For MBMS transmission using HSDPA channels the advantages of HSDPA transmission were considered for one specific MBMS mode. Namely, HSDPA was analyzed as a multicast multi-resolution system (point-to-point MBMS transmission), without or with MIMO. HSDPA by itself (without MIMO) offers much more enhancement layers for multi-resolution than the presented broadcast multi-resolution systems. Additional multi-resolution capability is offered by the association with spatial multiplexing schemes (MIMO). REFERENCES [1] T. Cover, “Broadcast channels,” IEEE Trans. on Inform. Theory, vol. IT-18, pp. 2–14, January 1972. [2] K. Ramchandran, A. Ortega, K. M. Uz, and M. Vetterli, “Multiresolution broadcast for digital HDTV using joint source/channel coding,” IEEE J. Select. Areas Commun., vol. 11, January 1993. [3] M. B. Pursley and J. M. Shea, “Non-uniform phase-shift-key modulation for multimedia multicast transmission in mobile wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 17, no. 5, May 1999. [4] Digital Video Broadcasting (DVB) Framing Structure, Channel Coding and Modulation for Digital Terrestrial Television (DVB-T), European Telecommunication Standard ETS 300 744, Mar. 1997. [5] High Speed Downlink Packet Access (HSDPA) Stage 2—Release 6, 2004-03, 3GPP TS 25.308 V5.4.0. [6] S. Dogan et al., “Video content adaptation using transcoding for enabling UMA over UMTS,” in Proc. of Wiamis 2004, Lisbon, Portugal, April 2004. [7] J. Liu, B. Li, and Y.-Q. Zhang, “Adaptive video multicast over the internet,” IEEE Multimedia, vol. 10, no. 1, pp. 22–33, Jan.–Mar. 2003.

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[8] W. Li, “Overview of fine granularity scalability in MPEG-4 video standard,” IEEE Trans. CSVT, vol. 11, no. 3, pp. 301–317, Mar. 2001. [9] A. Vetro, C. Christopoulos, and H. Sun, “Video transcoding architectures and techniques: An overview,” IEEE Sig. Proc. Mag., vol. 20, no. 2, pp. 18–29, Mar. 2003. [10] Samsung Electronics, “Scalable multimedia broadcast and multicast service (MBMS),” in MBMS Workshop, London, England, May 6–7, 2002. [11] S-CCPCH Performance for MBMS, 3GPP, 25.803-600 v6.0.0, September 2005. [12] N. Souto et al., “Iterative turbo multipath interference cancellation for WCDMA systems with non-uniform modulations,” in Proc. IEEE Vehicular Technology Conf.—VTC2005-Spring, Stockholm, Sweden, May–June 2005. [13] ——, “Non-uniform constellations for broadcasting and multicasting services in WCDMA systems,” in Proc. IEEE IST Mobile & Wireless Communications Summit, Dresden, Germany, June 19–23, 2005. [14] G. J. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Pers. Commun., vol. 6, pp. 311–335, Mar. 1998. [15] I. E. Telatar, Capacity of Multiantenna Gaussian Channels. : AT&T Bell Laboratories, June 1995, Tech. Memo.. [16] ——, “Capacity of multiantenna Gaussian channels,” Eur. Trans. Commun., vol. 10, no. 6, pp. 585–595, 1999. [17] J. C. Silva et al., “Enhanced MMSE Detection for MIMO Systems,” in Proceedings of ConfTele’2005, Tomar, Portugal, April 2005. [18] ——, “Equalization based receivers for wideband MIMO/BAST systems,” in Proceedings of WPMC’2005, Aalgorg, Denmark, September 2005. [19] F. Leitão and A. Correia, “HSDPA delivering MBMS video streaming using deficit round robin scheduler,” in Proceedings of ICT’2006, Funchal, Portugal, May 2006. [20] Spatial Channel Model for Multiple Input Multiple Output (MIMO) Simulations, 3GPP, 25.996-v6.1.0. [21] M. Sehlstedt and J. P. LeBlanc, “A computability strategy for optimization of multiresolution broadcast systems: a layered energy distribution approach,” IEEE Trans. on Broadcasting, vol. 52, no. 1, pp. 11–20, March 2006. [22] C-MOBILE, “Advanced MBMS for the Future Mobile World,” IST2005-27423 [Online]. Available: http://c-mobile.ptinovacao.pt [23] Physical Layer Procedures (FDD) (Release 5), 3GPP, 25.214-5b0. [24] A. Soares, J. C. Silva, N. Souto, F. Leitão, and A. Correia, “MIMO based radio resource for UMTS multicast broadcast multimedia services,” Springer Wireless Personal Communications Journal DOI 10.1007/s 11277-006-9175-x [Online]. Available: http://dx.doi.org/10.1007/s11277-006-9175-x[1]

Américo M. C. Correia received the B.Sc degree in electrical engineering from the University of Angola in 1983, the M.Sc. and Ph.D. degrees from Istituto Superior Técnico (IST), Lisbon, Portugal, in 1990 and 1994, respectively. From 1991 to 1999 he was with IST as an Assistant Professor. He is currently with Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE), Lisbon, Portugal. He visited Nokia Research Center from September to December 1998 as a visiting scientist. From September 2000 to August 2001 he joined Ericsson Eurolab Netherlands. His current research topics include, wideband CDMA, MIMO, radio resource management and multimedia broadcast/multicast services.

João C. M. Silva received the B.S. degree for Aerospace Engineering from the Instituto Superior Técnico (IST)—Lisbon Technical University, in 2000. From 2000–2002 he worked as a business consultant on McKinsey&Company. From 2002 onwards, he has been working on his PhD thesis at IST, focusing on spread spectrum techniques, Multi-User Detection schemes and MIMO systems.

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Nuno M. B. Souto graduated in aerospace engineering—avionics branch, in 2000 in Instituto Superior Técnico, Lisbon, Portugal. From November 2000 to January 2002 he worked as a researcher in the field of automatic speech recognition for Instituto de Engenharia e Sistemas de Computadores, Lisbon Portugal. He is currently working for his Ph.D. in electrical engineering in Instituto Superior Técnico. His research interests include wideband CDMA systems, OFDM, channel coding, channel estimation and MIMO systems.

Luísa A. C. Silva graduated in telecommunication and computer science engineering at Instituto Superior de Ciências do Trabalho e da Empresa, Lisbon, Portugal. She is currently working for her M.Sc at the same university. She has been working as a researcher in the fields of radio resources optimization and efficient allocation for 3G UMTS networks and beyond. These research activities are being developed in ADETTI/ISCTE. Since 2004 she has been working in some UE funded telecommunications research projects, namely B-BONE and currently C-MOBILE.

Alexandra B. Boal graduated in Telecommunication and Computer Science Engineering at Instituto Superior de Ciências do Trabalho e da Empresa, Lisbon, Portugal. She is currently working for her M.Sc at the same university. She has been working as a researcher in the fields of radio resources optimization and efficient allocation for 3G UMTS networks and beyond. These research activities are being developed in ADETTI/ISCTE. Since 2004 she has been working in some EU funded telecommunications research projects, namely B-BONE and C-MOBILE. She is currently with Siemens (R&D Department), Amadora, Portugal.

Armando B. Soares graduated in telecommunication and computer science engineering at Instituto Superior de Ciências do Trabalho e da Empresa, Lisbon, Portugal. He received his M.Sc in 2006 at the same university. He has been working as a researcher in the fields of radio resources optimization and efficient allocation for 3G UMTS networks and beyond. These research activities are being developed in ADETTI/ISCTE. Since 2003 he has been working in Information Society Technologies EU funded telecommunications research projects, namely SEACORN, B-BONE and currently C-MOBILE.