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ENHANCING SPECTRAL EFFICIENCY FOR LTE-A D VA N C E D A N D B E Y O N D C E L L U L A R N E T W O R K S

SPECTRAL- AND ENERGY-EFFICIENT TWO-STAGE COOPERATIVE MULTICAST FOR LTE-ADVANCED AND BEYOND YIQING ZHOU, HANG LIU, ZHENGANG PAN, LIN TIAN, AND JINGLIN SHI

ABSTRACT

Yiqing Zhou, Hang Liu, Lin Tian, and Jinglin Shi are with the Wireless Technology Research Center, Institute of Computing Technology, Chinese Academy of Sciences, and the Beijin Key Laboratory of Mobile Computing and Pervasive Devices. Yiqing Zhou is also with the University of Kent. Zhengang Pan is with China Mobile Research Institute.

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Driven by the strong demand for mobile video services in future mobile communications systems, advanced multicast techniques such as cooperative multicast, CM, have been developed to support multimedia services with high spectral and energy efficiency. With path loss gain, spatial diversity, and time diversity, CM outperforms traditional multicast, TM, in various scenarios at the cost of additional signaling and location information. Assuming selective combining based on average received signal strength, SCA, and high user density, the design of spectral- and energy-efficient CM (SECM-H) is presented, which employs a mobile relay arrangement scheme based on sector ring structures. It is shown by numerical results that SECM-H could improve the spectral efficiency per Watt of TM by 75.3 percent. Moreover, using cyclic prefix combining instead of SCA, BS coordination and MR coordination could further enhance the spectral efficiency per Watt of SECM-H by 10.4 and 20.0 percent, respectively. The design of SECM with low user density (SECM-L) is also described. When the number of users is small, a try-best, or TB, MR selection scheme should be employed that chooses the successful mobile station closest to the unsuccessful mobile station as its MR. The spectral efficiency per Watt of SECM-L with TB increases rapidly with the number of users. Its performance is inferior to that of TM when there are fewer than 37 users, while about 60 percent enhancement can be achieved when there are 100 users.

INTRODUCTION With the development of mobile communications and consumer electronics, the demand for broadband wireless data transmission is increasing rapidly. Based on Cisco’s forecast, there is a general consensus that 1000 times more capacity will be needed in mobile broadband before 2020, and about 70 percent of traffic will be videos. Therefore, highly spectrally efficient technolo-

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gies are crucial to future cellular systems such as Long Term Evolution-Advanced (LTE-A) and beyond, especially for providing video services. Exploiting the broadcasting nature of radio transmission, wireless multicast could provide videos to a group of mobile stations (MSs) in one transmission and is made to be spectrally efficient. Multicast has been widely supported in cellular systems, such as multimedia broadcast multicast service (MBMS) in third generation (3G) and enhanced MBMS (eMBMS) in LTEA. Based on these standards and to open new business models for operators, big companies, such as Ericsson and Huawei, have set LTE broadcast solutions to support a virtually unlimited number of users simultaneously employing LTE technology and networks. In fact, mobile TV broadcast services have already been launched worldwide using 3G cellular networks (e.g., by T-Mobile in the United Kingdom, Verizon in the United States, and Reliance in India). According to RNCOS’ research report [1], the number of mobile TV subscribers was projected to grow at a compound annual growth rate of over 45 percent between 2009 and 2013, reaching around 450 million by the end of 2013. Driven by the strong demand for mobile video services, wireless multicast has been widely investigated, and various advanced techniques have been proposed. It is well known that in traditional multicast (TM), the same data needs to be transferred to all MSs in the multicast group. Hence, the transmission rate is limited by the MSs with the worst channel condition, such as those at cell edges in cellular systems. Due to the large path loss, the data rate supported by cell edge MSs is much lower than those near the cell center. Therefore, TM results in inefficient use of the rare spectral resources, especially for those MSs with good channel conditions near the cell center. Various advanced schemes have been proposed to further improve the spectral efficiency of multicast transmission. One approach is to use source layered coding [2-3] to overcome the capacity limitation of TM. The main idea is to separate the

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source multicast data into a base layer and multiple enhancement layers, any combination of which is decodable. Then the MSs at the cell edge may only receive the base layer and be served with basic video quality, while for the MSs near the cell center, thanks to the good channel conditions, the base and enhancement layers could be received, and thus high quality videos can be obtained. Although layered multicast could improve spectral efficiency compared to that of traditional ones, it brings unfairness among the MSs who pay the same for the multicast service but are served with different qualities. Another promising approach is to introduce cooperation into multicast. The main idea is to employ MSs as mobile relays (MRs) to help the base station (BS) transfer multicast data to cell edge MSs [4]. In this cooperative multicast (CM), the transmission time slot is divided into several stages, the first for the BS to convey the data to all MSs and the rest for successful MSs (SMSs) that have received the data to further relay them to unsuccessful MSs (UMSs). This article focuses on the two-stage CM since it is more practical. Compared to unicast, cooperative communication is more attractive for multicast, where the relays are part of the intended recipients and hence free from the incentive and security concerns that have hindered the practical deployment of cooperation in unicast communications. By providing path loss gain and spatial and time diversity, CM could improve the received signal quality at UMSs significantly. It is shown that by employing all SMSs as MRs, much higher spectral efficiency can be achieved compared to that of TM [4-5]. However, the total power consumption also increases significantly with the number of MRs. It should be noted that energy efficiency has been widely considered a key feature of future wireless systems [6]. Therefore, it is necessary to consider both spectral and energy efficiencies when designing LTE-A and beyond systems. The spectral efficiency per Watt (SPW: bits per second per Hertz per Watt) is taken as a performance criterion in this article when investigating different multicast transmission schemes. It is also worth noting that device-to-device (D2D) communications, which is essential to support the data transmission from SMSs to UMSs in CM, has drawn a lot of attention recently. With D2D being discussed as a study item in 3GPP standards now [7], it is highly possible that CM could be employed in LTE-A and beyond systems as a promising spectral- and energy-efficient technique.

COOPERATIVE MULTICAST VS. TRADITIONAL MULTICAST TRADITIONAL MULTICAST The transmission scheme of TM is simple and well known. To provide multicast service with a predefined data rate, R con , the BS transmits multicast data at a sufficiently high power paging BS (PBS) to ensure that at least C cov per-

IEEE Wireless Communications • April 2014

centage of all MSs in the multicast group can receive the data. In the literature, it is always assumed that all MSs should obtain the data (i.e., Ccov = 100 percent) [4]. In this case, since the multicast transmission data rate is limited by the worst channel experienced by all MSs, when the number of MSs gets large, the total system throughput will saturate [8], which is obviously not desirable in practice. Thus, in real systems, C cov is set to a value less than 1 (e.g., 95 percent). Note that Ccov is the average success probability of all MSs. For MSs near the cell center, a success probability higher than C cov can be achieved due to better channel conditions, while for cell-edge MSs, the success probability is actually much lower than C cov . Hence, although a cell coverage of C cov can be ensured, the coverage performance of cell-edge MSs is much poorer than that of cellcenter MSs.

Driven by the strong demand for mobile video services in future mobile communications systems, advanced multicast techniques such as cooperative multicast have been developed to support multimedia services with high spectral and energy efficiency.

TWO-STAGE COOPERATIVE MULTICAST To improve the performance of cell edge MSs, CM is considered, which introduces MS-based cooperative relay in multicast transmission. As an example, Fig. 1 illustrates the transmission scheme of two-stage CM, where the time slot allocated for multicast services, T, is divided into two time intervals, T 1 and T 2. At the first stage, the BS transmits multicast data at a data rate R two,1 with a power of P BS,CM so that only part of the MSs (i.e., the MSs with good channel conditions) can successfully receive the data. It has been shown that instead of employing all SMSs as relays, which is not energy-efficient, a limited number of properly selected SMSs should be sufficient to provide good performance [5]. For example, SMSs near the cell edges could be selected as MRs where there are more UMSs. Then at the second stage with a time duration of T 2, the selected SMSs convey the multicast data at a rate of R two,2 to the UMSs. In this article, it is assumed that T1 = T2 and Rtwo,1 = Rtwo,2. Note that although T1 = T2 is not necessarily an optimized value that could maximize the SPW, it is a reasonable setting that has been shown to be able to maximize the total system throughput [9].

ADVANTAGES OF COOPERATIVE MULTICAST Compared to traditional one-stage multicast, the advantages of the two-stage CM include: • Path loss gain: As shown in Fig. 1, the distance between the MRs and UMSs is much shorter than that between the BS and UMSs. To convey the multicast data successfully, the transmission power of MRs is much lower than that of the BS. • Spatial diversity: At the second stage of CM, multiple distributed SMSs are employed as MRs to forward the same multicast data to UMSs. These signals can be combined at each UMS to provide better performance. For example, in orthogonal frequency-division multiplexing (OFDM)-based systems such as LTE and LTE-A, when the cyclic prefix (CP) is sufficiently long, signals from all MRs could fall within a CP duration, and a stronger signal may be constructed by CP combining (CPC).

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Note that the power consumption of two-stage CM depends on various system parameters such as the BS transmission power at the 1st stage, the number of MRs at the 2nd stage, the MR arrangement/selection schemes, the receive signal processing algorithms, the user density, and so on.

BS transmits data at the first stage SMS: MS that successfully receives the data at the first stage

BS

MRs transmit data at the second stage while the BS keeps silent UMS: MS that does not obtain the data at the first stage

Selected SMSs act as MRs

BS

Time T1

T2

Time slot for multicast services T = T1 + T2

Figure 1. Illustration of two-stage cooperative multicast transmission.

• Time diversity: If the same data is transmitted at the first and second stages, which is possible when T 1 = T 2 , each UMS could further combine the signals received at the first and second stages with techniques such as maximum ratio combining (MRC). Thus, a better received signal-to-noise ratio (SNR) can be obtained. With the path loss gain provided by shorter communication distances, spatial diversity provided by distributed MRs, and time diversity provided by two-stage transmission, CM should be more spectrally efficient than the traditional ones given the same power consumption, or more energy-efficient to provide the same spectral efficiency, equivalently. In addition, another benefit provided by two-stage CM is that the coverage performance is more uniform than that in TM, and thus better user fairness can be achieved. As illustrated before, to provide a cell coverage ratio of Ccov with TM, the coverage at cell edges is actually much lower than the average value of Ccov. With two-stage CM, the coverage ratio of cell edge MSs is mainly decided by the second stage transmission from MRs, and can be naturally and conveniently set to Ccov, so the coverage performance of the whole cell can be guaranteed after two-stage transmission.

tion information of MSs may be needed in CM. Thus, after the first stage transmission, the BS could know the positions of SMSs and UMSs, and efficient MR selection could be performed. With the rapid development of location-based services, the location of MSs might be obtained using terminal-based or network-based positioning techniques [10]. The second stage transmission could also happen without BS control. For instance, after the first stage transmission, UMSs could broadcast relay requests, and SMSs receiving this request could act as MRs at the second stage. Without the need for feedback from SMSs to provide location information, this scheme is easy to implement. However, without the central control of a BS, it is possible that there are more MRs than necessary working at the second stage if multiple SMSs receiving the request from the same set of UMSs; thus, the energy efficiency may be low. In addition, SMSs in CM spend extra power to relay the data, which is undesirable considering the limited battery life at mobile devices. There are two possible ways to mitigate the problem. One is that the number of SMSs selected as MRs should be as small as possible. The other is to introduce proper stimulating schemes to encourage SMSs to participate in the CM [11].

IMPLEMENTATION ISSUES OF COOPERATIVE MULTICAST

SPECTRAL AND ENERGY EFFICIENT COOPERATIVE MULTICAST

Although CM could enhance the spectral and energy efficiency of traditional multicast, it is obtained at the cost of increased complexity. First, additional signaling is needed in CM if the BS is to control the second stage transmission. For example, SMSs may send signals to the BS to indicate that they are willing to act as relays. The BS should then inform the selected SMSs to relay data to other UMSs. Moreover, the loca-

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DESIGN PRINCIPLE Focusing on the SPW, it is first assumed that CM and TM provide the same spectral efficiency. Moreover, for fair comparison, the two schemes should guarantee the same coverage performance. Then the energy consumption can be investigated. The design of spectral- and

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energy-efficient CM (SECM) is to achieve the best SPW, or equivalently the lowest power consumption. Hence, the key is to find the relationship between the total power consumption and the coverage ratio for a given spectral efficiency. Note that the power consumption of two-stage CM depends on various system parameters such as the BS transmission power at the first stage, the number of MRs at the second stage, the MR arrangement/selection schemes, the receive signal processing algorithms, the user density, and so on. First of all, to provide insight on the possible gain in SPW provided by CM, the system with simplified receive signal processing and high user density is investigated: • Selective combining based on average received signal strength (SCA): SCA is employed, by which UMSs only receive the signal from the nearest MR. As illustrated before, in practical systems based on OFDM, the signal received by each UMS at the second stage is actually CP combined (CPC). Although CPC could provide better performance than SCA due to spatial diversity, it is difficult to analyze the coverage performance of MRs with CPC. Therefore, as an initial investigation and to provide a lower bound on energy efficiency, SCA is considered. • High user density: Since the MR transmission power is given and fixed, the total power consumption depends on PBS,CM and the number of MRs employed at the second stage, which is highly related to the user density in the cell. For example, if there is only one MS, there is no need to employ the two-stage scheme. On the other hand, when the density is sufficiently high, it can be assumed that any location in the cell can be covered by at least one MR at the second stage. Then proper MR selection schemes [12] are needed to avoid unnecessary relaying with additional power consumption. With these assumptions, investigation can be carried out on the total power consumption of two-stage CM. First, the number of MRs should be decided. With high user density, it is possible to employ the MR arrangement based on a sector ring structure, which could provide relatively uniform coverage performance for MSs at different locations in the cell. As shown in Fig. 2, given a desired cell coverage ratio, Ccov, for any BS transmission power, PBS,CM, a corresponding radius can be obtained according to the definition of coverage ratio within which C cov MSs could successfully receive the data. Hence, no MRs will be deployed in this area at the second stage. All MRs should be located in the annular region with radius from RBS,CM to the cell edge. This annular region is further divided into several rings, where the coverage ratio at the first stage is below Ccov and decreases as the distance from the BS increases. Thus, each ring needs MRs to improve the overall coverage ratio to C cov after the two-stage transmission, and the coverage capability requirement for the MRs increases with their distance from the BS. Assuming SCA, the coverage area of each MR is circular, and the radius decreases as their distance from the BS increases for a fixed MR transmission power. The farther away the ring is from the BS, the more MRs should be employed.

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Success probability of BS

Average Ccov

Average Ccov with the help of MR

(0,0) Success probability of MR

Coverage radius reduce

RBS,CM

(0,0)

R

Figure 2. Design principle of MR arrangement based on sector ring structures.

Using the previous design principle, the coverage radius of MRs at each ring could be derived from the coverage requirement with reasonable approximations, which is a function of P BS,CM [12]. Then the number of MRs at each ring can be obtained by simply dividing the ring area with the coverage area of MRs at this ring, which is also dependent on PBS,CM. As a whole, the total power consumption can be expressed as a function of P BS,CM , and the minimization of total power over PBS,CM can be carried out.

PERFORMANCE EVALUATION Numerical calculation and simulations are performed to verify the effectiveness of SECM with high user density (SECM-H). The system parameters are chosen according to practical network settings, where the target coverage ratio Ccov is

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set to 95 percent, the transmission power of the BS for TM is 33.88 W (45.3 dBm), and the transmission power of an MS is 0.20 W (23 dBm). The radius of the macrocell is set to 1500 m. Moreover, for the channels between the BS and the MS, and between the MR and the MS, two different path loss models are employed [13] since the antennas on the BS and MR have different heights and reference distances. First of all, by varying the BS transmission power at the first stage, PBS,CM, from 4 to 64 W, the total power consumption can be numerically calculated, and the results are plotted in Fig. 3. When PBS,CM gets larger than 4 W, the required number of MRs reduces, and the total power consumption tends to be smaller, reaching the minimum value of 19.3 W when P BS,CM = 11.3

300 Total power of SECM-H Total power of TM Required number of MRs in SECM-H 240

180

120

25

Number of MRs

Total power (W)

35

60 (11.3, 19.3) 15

14 PBS,CM(W)

4

24

34

44

0 54 64

Figure 3. Number of MRs needed and total power consumption of SECM-H.

Spectraol efficiency per watt (SPW: b/s/Hz/W)

104

SECM-H Scheme [4] TM

EFFECT OF SIGNAL PROCESSING SCHEMES 103

102

101

100 103

104 Number of users in the cell

Figure 4. Comparison of spectral efficiency per Watt at high user density.

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W. At this point, the macrocell is exactly covered by the BS and three rings, where 31, 47, and 60 MRs (a total of 138 MRs) are allocated with coverage radii of 127.9, 104.9, and 94.0 m, respectively. As PBS,CM is increased further, the required number of MRs reduces, but the total power becomes higher. This is because with a large PBS,CM, the path loss gain provided by MRs could not be efficiently exploited. It is interesting to see that the total power of the designed SECM-H changes in a staggered way with P BS,CM . This is because with the MR arrangement based on sector ring structure, the cell must be covered with an integer number of rings even if the actual coverage area is larger than the macrocell. This results in waste of MR power. To be energy-efficient, the optimal PBS,CM must be obtained when the macrocell can be exactly covered by the BS and integer number of rings. Next, by means of simulations, the SPW of the designed SECM-H, the scheme in [4] and TM are compared in Fig. 4 as a function of the number of MSs. It can be seen that the SPW of the SECM-H scheme could be doubled compared to that of Scheme [4], and the performance gap increases with user density. Moreover, TM also outperforms Scheme [4] in terms of SPW. This is because Scheme [4] employs all SMSs as MRs, and the total power increases with user density. In contrast, according to SECM-H, with sufficiently high user density the number of MRs and their locations are determined, and will not change with user densities. When the number of MSs gets larger, the system throughput increases while the power consumption is fixed. Similarly, for TM guaranteeing 95 percent coverage, the spectral efficiency gets larger with the number of users with fixed power consumption. Therefore, the SPWs of SECM-H and TM are higher than that of Scheme [4]. As a result, by minimizing power consumption with guaranteed coverage at high user density, SECM-H provides better spectral and energy efficiency than Scheme [4] and TM. However, it should also be noted that TM and Scheme [4] are easier to implement since SECMH may need additional signaling and the location information of all SMSs.

105

Although SCA could facilitate performance analysis, it will not be employed in practice, especially for OFDM systems, since the signals from multiple MRs are naturally CP combined at the receiver. Not shown in this article due to space limitations, simulations have demonstrated that with the optimal BS power allocation and MR arrangement scheme obtained with SCA, the system with CPC could achieve a coverage ratio higher than 95 percent [12]. That is, to guarantee a coverage ratio of 95 percent, further power saving is possible with CPC. When CPC is employed at the UMSs, the coverage area of each MR is complicated because it theoretically contributes to the coverage of all UMSs. Thus, a similar analysis as that with SCA in the previous section is difficult for CPC, and it is hard to obtain the corresponding optimized BS transmis-

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Cooperative multicast (SECM-H)

Schemes Traditional multicast (TM)

CPC SCA

Performance

BS coordination

MR coordination

BS power (W)

33.9

11.3

7.6

11.3

Number of MRs

0

137

137

105

Total power (W)

33.9

19.3

17.5

16.1

Power reduction compared to that of TM

0

43.1%

48.4%

52.5%

Power reduction compared to that of SECM-H with SCA

Null

0

9.3%

16.6%

Spectral efficiency per Watt (SPW) (b/s/Hz/W) for one user

0.0263

0.0461

0.0509

0.0553

SPW improvement compared to that of TM

0

75.3%

93.5%

110.3%

SPW improvement compared to that of SECM-H with SCA

Null

0

10.4%

20.0%

One possible sub-optimum solution is that the system with CPC should use the optimized results obtained with SCA; then the BS transmission power is decreased (BS coordination) or the number of MRs is reduced (MR coordination) to provide a coverage ratio of 95 percent.

Table 1. Performance comparison of different multicast schemes.

sion power and MR arrangement. One possible sub-optimum solution is that the system with CPC should use the optimized results obtained with SCA; then the BS transmission power is decreased (BS coordination) or the number of MRs is reduced (MR coordination) to provide a coverage ratio of 95 percent. Using the system configurations, numerical results are obtained, and the total power consumption is shown in Table 1 for TM and SECM-H with SCA and CPC. Compared to TM and SECM-H with SCA, SECM-H with CPC and BS coordination could reduce the total power consumption by 48.4 and 9.3 percent, respectively, while MR coordination performs even better by saving about 52.5 and 16.6 percent total power consumption, respectively. This is because compared to BS coordination, MR coordination could provide more uniform success probability throughout the cell and achieve better energy efficiency [14]. Moreover, in terms of SPW, CPC with BS coordination could bring an improvement of 93.5 and 10.4 percent over TM and SECM-H with SCA, respectively, while MR coordination could enhance the SPW performance of TM and SECM-H with SCA by 110.3 and 20.0 percent, respectively.

EFFECT OF USER DENSITY It has been shown earlier that with high user density, CM could significantly enhance the SPW of TM. However, with low user density, CM may be inferior to TM. For example, from simulation results in [4], one can expect that CM may perform worse than TM when the number of MSs is less than 10. In practice, there are scenarios when only a small number of MSs are served, such as at the beginning stage of promot-

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ing multicast services. In these cases, the schemes designed with high user density may not be applicable. For instance, with high user density, an SMS could always be found at or near the desired location. Hence, the SECM-H presented earlier selects a fixed number of MRs in a circular pattern to provide uniform coverage throughout the cell. However, when the user density is low, it is possible that there is no SMS at or near a desired location in the cell, and the SECM-H scheme cannot be applied. Therefore, it is necessary to investigate the effect of user density on the performance of CM and design MR selection schemes with low user density. First of all, the MR selection scheme is considered with low user density, when both SMSs and UMSs should be sparsely distributed in the cell, and the probability that several UMSs are covered by one MR is low. Moreover, to provide fairness to all users, each UMS should get relay help from an MR to be successful, whether it is located near the BS or at the cell edge. Hence, the closest SMS to a UMS should be selected as its MR. This is a try-best (TB) scheme since with low user density, even the closest SMS could be far away from the UMS and fail to help the UMS to be successful after the second stage transmission. It is shown later that the CM with TB could considerably enhance the SPW of TM, and it is denoted as spectral- and energy-efficient cooperative multicast with low user density (SECM-L). Note that the TB scheme is different from that in [15] where an SMS becomes an MR only if it receives requests from UMSs. Using the scheme in [15], it is possible that multiple SMSs receive a request from one UMS and all of them become MRs, which is not necessary and makes the scheme energy-inefficient. The TB scheme

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Spectral efficiency per watt (SPW b/s/Hz/W)

4

It can also be seen from Fig. 5 that with various signal processing schemes, SECM-L could always achieve much higher SPW than that of Scheme [4], and the performance gap increases with the number of users. This is because the total power consumption of Scheme [4] increases with the number of MSs by using all SMSs as MRs. Thus, SECM-L is more energy-efficient than Scheme [4]. It can also be seen that when the number of users is less than 37, even with CPC and MRC, the SPW of SECM-L is less than that of TM, although the difference is small. Then it increases rapidly with the number of users and surpasses that of TM. When there are 100 users, SECM-L could provide about 60 percent improvement in SPW compared to TM. This demonstrates that CM could be spectraland energy-efficient when there is a high enough number of users in the cell.

SECM-L (SCA+MRC) SECM-L (CPC+MRC) SECM-L (CPC) Scheme [4] TM

3.5 3 2.5 2 1.5 1 0.5 0

10

20

30

40 50 60 70 Number of users in the cell

80

90

100

Figure 5. Comparison of spectral efficiency per Watt at low user density. would also be less energy-efficient when the user density increases and two closely located UMSs could be covered by one SMS. In this case, TB brings power waste since it selects the two closest SMSs for the two UMSs separately. Moreover, to realize the TB scheme in practice, location information of each MS is needed at the BS so that the BS could notify the closest SMS to relay data to a UMS at the second stage. Given the TB MR selection scheme and assuming SCA with MRC, coverage performance analysis could be carried out for CM, which is a function of the BS transmission power at the first stage PBS,CM and the total number of MSs in the cell. Thus, for a given coverage ratio, the lower bound of user density can be found by setting P BS,CM to a maximum possible value. Using the system configurations mentioned earlier, a lower bound of user density is found to be 37, which means that if there are no more than 37 users in the cell, CM is not energy-efficient and TM should be employed. Figure 5 compares the SPW of SECM-L to that obtained from Scheme [4] and TM. Scheme [4] employs CPC, but MRC cannot be used since the transmitted data at the first and second stages may be differently encoded and modulated to provide different data rates. It can be seen that with MRC, SECM-L with SCA performs similar to CPC. This is because MRs are sparsely located in the cell when the number of MSs is small, and could contribute little to other UMSs except the closest one. On the other hand, without MRC, the performance of SECM-L with CPC is degraded considerably compared to that with MRC. Hence, the time diversity provided by MRC is critical to CM when the user density is low. Note that for high user density, MRC could also improve the SPW since the time diversity gain will always be obtained from two transmissions regardless of user density. However, the performance of CPC and SCA will be quite different with high user density, as shown in Table 1, since the contributions from MRs other than the closest one are not negligible.

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CONCLUSIONS Cooperative multicast is promising to enhance the spectral and energy efficiency of LTEAdvanced and beyond systems, especially for video services. Its main idea has been introduced and compared to TM. It has been demonstrated that with path loss gain, spatial diversity, and time diversity, CM outperforms traditional multicast in various scenarios. Assuming SCA and high user density, the design of spectral- and energy-efficient CM has been described, and SECM-H could improve the spectral efficiency per Watt of TM by 75.3 percent. Using CPC instead of SCA, SECM-H further enhances the SPW of TM by up to 110.3 percent. Moreover, for low user density, a try-best MR selection scheme is employed by CM, and the SPW of SECM-L increases rapidly with the number of users. Although SECM-L is inferior to TM when there are fewer than 37 users, a 60 percent enhancement can be provided when there are 100 users. It should also be noted that various challenges should be solved before CM can be applied in practice, such as limited battery life of mobile devices, stimulating schemes to encourage cooperation, signaling design, requirements for location information, and so on.

ACKNOWLEDGMENT This work was supported by the KC Wong Fellowship (the Royal Society), Beijing Natural Science Foundation Major Project 2010 (4110001), National Natural Science Foundation (61331009), National Science and Technology Major Project (Grant No. 2013ZX03003003), New Technology Star Plan of Beijing (Grant No. xx2013052), and Main Direction Program of Knowledge Innovation of Chinese Academy of Sciences.

REFERENCES [1] “Mobile/Tablet TV & Video Content, Broadcast & OTT Strategies 2013–2017,” Juniper Research, May 2013. [2] H. Schwarz, D. Marpe, and T. Wiegand, “Overview of the Scalable Video Coding Extension of the H.264/AVC Standard,” IEEE Trans. Circuits and Systems for Video Tech., Special Issue on Scalable Video Coding, vol. 17, no. 9, Sept. 2007, pp. 1103–20. [3] H. Zhu, “Radio Resource Allocation for OFDMA Systems in High Speed Environments,” IEEE JSAC, vol. 30, no. 4, May 2012, pp. 748–59.

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[4] F. Hou et al., “A Cooperative Multicast Scheduling Scheme for Multimedia Services in IEEE 802.16 networks,” IEEE Trans. Wireless Commun., vol. 8, Mar. 2009, pp. 1508–19. [5] N. Guan et al., “An Energy Efficient Cooperative Multicast Transmission Scheme with Power Control,” IEEE GLOBECOM ’11, Dec. 2011, pp. 1–5. [6] C. Han et al., “Green Radio: Radio Techniques to Enable Energy Efficient Wireless Networks,” IEEE Commun. Mag., vol. 49, no. 6, June 2011, pp. 46–54. [7] K. Doppler et al., “Device-to-Device Communication as an Underlay to LTE-Advanced Networks,” IEEE Commun. Mag., Dec. 2009, pp. 42–49. [8] C. Suh and J. Mo, “Resource Allocation for Multicast Services in Multicarrier Wireless Communications,” IEEE Trans. Wireless Commun., vol. 7, no. 1, Jan. 2008, pp. 27–31. [9] B. Niu, J. Hai, and H. Zhao, “A Cooperative Multicast Strategy in Wireless Networks,” IEEE Trans. Vehic. Tech., vol. 59, Jul. 2010, pp. 3136–43. [10] I. A. Junglas and R. T. Watson, “Location Based Services,” Commun. ACM, vol. 51, issue 3, Mar. 2008, pp. 65–69. [11] G.. Zhang et al., “Bargaining Game Theoretic Framework for Stimulating Cooperation in Wireless Cooperative Multicast Networks, “ IEEE Commun. Letters, vol. 16, no. 2, Feb. 2012. [12] Y. Zhou et al., “Two-Stage Cooperative Multicast Transmission with Optimized Power Consumption and Guaranteed Coverage,” IEEE JSAC on SEED, early access articles, Issue 99, May 2013, pp. 1–11. [13] IEEE 802.16m-08/004r5, “IEEE 802.16m Evaluation Methodology Document,” Jan. 2009. [14] H. Liu et al., “ Investigation on Energy Efficiency of OFDM-Based Two-Stage Cooperative Multicast with CP Combining,” IEEE WCNC ’13, Apr. 2013. [15] J. Lee et al., “Energy Efficient Cooperative Multicast Scheme Based on Selective Relay,” IEEE Commun. Letters, vol. 16, no. 3, Mar. 2012, pp. 386–88.

BIOGRAPHIES YIQING ZHOU [SM] ([email protected]) received her B.S. degree in communications and information engineering and her M.S. degree in signal and information processing from Southeast University, China, in 1997 and 2000, respectively. In February 2004 she received her Ph.D. degree in electrical and electronic engineering from the University of Hong Kong. Now she is a professor at the Wireless Communication Research Center, Institute of Computing Technology, Chinese Academy of Sciences. She has published over 70 papers and book chapters in the areas of wireless mobile communications. She is an Associate and Guest Editor for IEEE Transactions on Vehicular Technology, IEEE JSAC (Special Issues on Broadband Wireless Communication for High Speed Vehicles and Virtual MIMO), WCMC, ETT, and JCST. She is Symposium Co-Chair of IEEE ICC ’14 and Tutorial Co-Chair of ICCC ’14, and was Tutorial Co-Chair of WCNC’13, TPC Co-Chair of ChinaCom ’12, and Workshop Co-Chair of IEEE SmartGridComm ’12 and IEEE GLOBECOM ’11. She has also served many international conferences as a TPC member, including IEEE ICC, GLOBECOM, WCNC, and VTC. H ANG L IU ([email protected]) received his B.S. degree in mathematics and applied mathematics from Northwestern Polytechnical University in 2010, and his M.Phil degree in wireless communications from the Chinese Academy of Sciences (CAS) in 2013. He is currently a Ph.D. candidate at

IEEE Wireless Communications • April 2014

CAS. His research focuses on cooperative multicast, interference management, and network information theory. ZHENGANG PAN [SM] ([email protected]) is a principal member of staff at the Green Communication Research Center (GCRC) of China Mobile Research Institute (CMRI), currently leading a team working on the key technologies of next generation (5G) wireless communication systems. Before joining CMRI in spring 2013, he worked at HongKong ASTRI for more than six years where he was involved in multiple technical fields, from wireless communication (WiFi, WiMax, LTE) to mobile digital TV (T-DMB, DVB-T/H, CMMB) to wireline broadband access (HomePlug, MoCA), in both system/algorithm design and terminal SoC chip implementation. He has also been working with NTT DoCoMo Beijing Communication Labs Co. Ltd. on the frontier research for 4G wireless communication standards, including 802.11n, 802.16d/e, HSPA, and LTE. He received his Ph.D degree in 2004 from the Department of Electrical and Electronic Engineering, University of Hong Kong. He has expertise in many technical fields including time/frequency/sampling synchronization technology for single-carrier/multicarrier(OFDM/A)-based systems, channel estimation, forward error correction coding, multiple antennas systems (MIMO) and space-time processing/coding, cross layer optimization, and so on. He has published more than 40 papers in top journals and international conferences, and filed 38 patents with 15 granted so far.

It should be noted that various challenges should be solved before CM can be applied in practice, such as limited battery life of mobile devices, stimulating schemes to encourage cooperation, signaling design, requirement on the location information, and so on.

LIN TIAN [M’07] ([email protected]) is an associate professor at the Wireless Communication Technology Research Center, Institute of Computing Technology (ICT), CAS. She received her B.S and M.S. degrees from Beihang University, Beijing, China, in 2002 and 2005, respectively. In April 2012, she received her Ph.D. degree from ICT/CAS. Her research interests include wireless resource management and multimedia multicast schemes in next-generation mobile communication systems. She has published more than 20 research papers in IEEE journals and international conferences. She is also the inventor of more than 10 Chinese patents and pending applications. She was the Symposium Co-Chair of ChinaCom ’13 and publication chair of ChinaCom ’12. She has also served as a reviewer for a number of referred journals and international conferences. JINGLIN SHI ([email protected]) currently serves as the director of the Wireless Communication Technology Research Center of ICT/CAS. He is also a visiting professor at Beijing University of Posts and Telecommunications, the University of Sydney, the University of Wollongong, and Macquarie University. His research interests include wireless communications system architecture and management, wireless signal processing theory, and wireless communications baseband processor design. As a team leader, he successfully led the development of TD-SCDMA, WiMAX, and LTE protocol stack systems . He is currently responsible for several national projects in broadband wireless communications, including TDD-LTE baseband chip design and research on radio resource management techniques toward IMT-A. He has published two books and over 100 papers in telecommunications journals and conference proceedings, and has more than 30 patents granted. He has also served on the organizing and technical committees of numerous national and international conferences. He was General Co-Chair of ChinaCom ’12, and a member of the Technical Program Committees of IEEE WCNC, IEEE ICC, IEEE AusWireless 2006, 7th IEEE ISCIT ’07, and ChinaCom ’07 and ’09.

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