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Energy Efficient Two-stage Cooperative Multicast: Effect of User Density Yiqing Zhou, Senior Member, IEEE, Hang Liu, Zhengang Pan, Senior Member, IEEE, Lin Tian, Member, IEEE, and Jinglin Shi.
Abstract—Exploiting the spatial diversity and path loss gain provided by the cooperative transmission with mobile relay (MR), two-stage cooperative multicast (CM) can provide the same performance as conventional ones do with reduced power consumption. However, this target cannot always be achieved, such as when the user density is low. To investigate the impact of user density on the energy efficiency of CM, selective combining based on average received signal strength (SCA) is assumed for the 2nd stage signal processing and maximum ratio combining (MRC) is employed to collect useful signals from the 1st and 2nd stages. Then a try-best MR selection scheme is proposed, choosing the successful Mobile Station (SMS) closest to the unsuccessful MS (UMS) as its MR. Based on this MR selection scheme, the coverage analysis is carried out. A lower bound of user density can be numerically obtained, below which the two-stage CM is energy inefficient. The analysis is verified by simulations. It is shown that to guarantee a given coverage performance such as 95%, for a cell with a radius of 1500m and a multicast data transmission efficiency of 0.45bps/Hz, the lower bound of user density for CM to be energy efficient is found to be 13. The total power consumption of two-stage CM reduces significantly when the number of MSs (N ) increases. For N =100, a reduction of 50% could be achieved compared to that of conventional ones. Moreover, for the signal processing scheme, the SCA assumption is verified to be reasonable for low user density, and MRC could effectively reduce the lower bound of user density from 24 to 13. Finally, the energy efficiency of the proposed CM scheme with try-best MR selection is shown to be much higher than that of existing schemes. Index Terms—device to device transmission, two-stage cooperative transmission, low user density, energy efficient, conventional one-stage multicast, coverage ratio.
I. I NTRODUCTION ITH the rapid development of broadband mobile data services, advanced technologies are needed in cellular networks to enable high capacity wireless access. Since multimedia services are and will still be the leading drive of the mobile data increasing [1], providing subscribes with high quality multimedia services with limited bandwidth becomes
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Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to
[email protected]. Y. Zhou, H. Liu, L. Tian and J.L. Shi are with Beijing Key laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (E-mail: zhouyiqing, liuhang, tianlindd,
[email protected]). H. Liu is also with University of Chinese Academy of Science. Z.G. Pan is with Spreadtrum Communications, Inc., Beijing, China (e-mail:
[email protected]). This research was supported by the National Natural Science Foundation of China (No. 61571425), 863 Program (No. 2014AA01A705) and New Technology Star Plan of Beijing (No. xx2013052).
a hot topic and has been paid much more attention recently in both industry and academy. As a spectrum efficient scheme exploiting the nature of radio transmission, multicast plays an important role in cellular networks to provide multimedia services to a group of mobile stations (MS) [2]. One recent advance in multicast is the cooperative multicast (CM) introduced in [3], which divides the transmission time into several stages. At the 1st stage, the base station (BS) broadcasts with high data rate so that a relatively low proportion of MSs (e.g., 50%, those with good channel conditions) can successfully receive the information (successful MSs: SMSs). Then, part of the SMSs are selected as mobile relays (MRs) to transfer information to the rest MSs at the 2nd stage, 3rd stage, and so on. With the help of cooperative transmission, multi-stage CM can improve the system throughput [3], reduce the total power consumption [4], or enhance the network coverage [3]. Considering the fact that MR selection should be carried out at each stage except the first stage, the signaling and computation related to MR selection increases linearly with the number of stages. From this point of view, the two-stage CM is relatively simple and would be easier to implement than other multistage CM with at least three stages. Thus, this paper focuses on the two-stage CM. Two-stage CM has attracted lots of attention recently. Setting the coverage ratio for the 1st stage transmission to be 50% and employing all SMSs as MRs at the 2nd stage, it is shown in [5] that CM is effective to improve the system throughput. However, the total power consumption increases rapidly with the number of MSs in the cell. On the other hand, when the total power consumption is fixed, it is shown in [6] that half of the total power should be assigned to the BS in order to minimize the outage probability of the two-stage CM. Recently, energy efficiency becomes another focus of future system design [7-9] and lots of efforts have been taken to design energy efficient two-stage CM schemes. For instance, transmit beamforming is studied in [10], targeting to minimize the BS transmission power in two-stage CM. Furthermore, since the total power consumption of CM largely depends on the number of MRs, MR selection schemes are vital to the energy efficiency of the system. Although relay selection has been widely investigated, they are usually designed for unicast transmissions [11-13]. It should be noted that the MR selection for CM is different to that in unicast. In unicast, relays are selected to optimize the performance of one destination MS [11-13], while in multicast, the performance of all MSs in the multicast group should be considered. Thus, existing relay selection schemes for unicast cannot be extended to multicast
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easily and new MR selection schemes should be designed for CM. For instance, a location based service technology is employed in [14-15] to select proper MRs at the 2nd stage of CM which can reduce the energy consumption by 1018%. [15] modifies the scheme in [5] by letting cooperative transmission happen only if a SMS receives requests from UMSs. Thus, power consumption can be reduced by eliminating unnecessary cooperative transmissions. However, the coverage performance cannot be guaranteed by this scheme. Focusing on the total power consumption with guaranteed coverage, [4] studied the MR arrangement with high user density and the power consumption can be reduced by 40% compared to that of conventional one-stage multicast. Although CM has been shown to be superior to conventional multicast in various scenarios, it is noted that CM may be inferior to the conventional one in some circumstances, such as when the number of MSs in the cell is small (or low user density, equivalently, considering a fixed cell radius). Although it is not explicitly stated in [16], from simulation results, one can expect that CM may perform worse than the conventional scheme when the number of MSs is less than ten. In practice, it is possible that only a small number of MSs are served in some cases, such as at the beginning stage of promoting multicast services, or only a few MSs are willing to relay the data. It remains unknown if CM should always be employed and what kind of MR selection schemes should be adopted in these scenarios. For example, in previous research [4], given high user density, it is proposed to select 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. Thus, the proposed scheme in [4] is not applicable. Therefore, it is necessary to investigate the effect of user density on the performance of two-stage CM and advice proper multicast schemes when the user density is low. This paper focuses on energy efficient two-stage CM in low density aiming to provide the same performance as conventional multicast does with reduced power consumption. A practical coverage ratio such as 95% should be guaranteed. Firstly, it is supposed that MS only receives the signals from the nearest MR at the 2nd stage and the signals from other MRs are ignored, which is named as selective combining based on average received signal strength (SCA). Moreover, maximum ratio combining (MRC) is used to collect useful signals from the 1st and 2nd stage to improve the final signal to noise ratio (SNR). Aiming to derive a lower bound for user density NLB below which CM could not be energy efficient, we propose a try-best MR selection scheme, which chooses the SMS closest to the unsuccessful MS (UMS) as its MR. Then, coverage performance analysis is carried out for CM with SCA and MRC assumption, which is a function of the BS transmission power at the 1st stage PBS,C and the total number of MSs in the cell (N ). Thus, the lower bound NLB for a given coverage ratio can be found by setting PBS,C to a maximum possible value. The analysis is verified by simulations. It is shown that for a cell with a radius of 1500m and multicast spectrum efficiency of 0.45bps/Hz, a lower bound of user density NLB is found to be 13, which
means that if there are no more than 13 users in the cell, CM is not energy efficient and conventional multicast should be employed. The total power consumption of CM reduces significantly when N increases. For N =100, a reduction of 50% in power consumption can be achieved by CM compared to that of conventional one. Moreover, to verify the assumption of SCA, it is shown that when N is smaller than 70, the performance gap between SCA and combining signals from all MRs in the cell is negligible. Thus for low user density, the analysis results obtained with SCA is reasonable and applicable. In addition, MRC could effectively reduce the lower bound of user density from 24 to 13. Finally, by means of simulations, the energy efficiency of two-stage CM with try-best MR selection is shown to be much higher than that of the existing schemes. The rest of the paper is organized as follows. Section II describes the system model and the basic principles of conventional one-stage and two-stage CM schemes with SCA plus MRC. Next, in Section III, a try-best MR selection scheme is firstly proposed, based on which the coverage performance analysis is carried out for CM. A lower bound for user density can be numerically calculated, below which CM cannot be energy efficient. Section IV presents numerical and simulation results with two-stage CM, verifying the analytical results. Finally, conclusions are drawn in Section V. II. SYSTEM DESCRIPTION Consider the orthogonal frequency division multiplexing (OFDM) based broadband downlink transmission in cellular systems, where a single antenna transceiver and a slow fading channel are assumed [6]. Moreover, a block of continuous sub-carriers are allocated for multicast transmission and they experience similar channel fading [17]. Therefore, in the following context, the received signal on one sub-carrier is concerned. Suppose that the length of cyclic prefix (CP) is longer than the maximum delay of the equivalent multipath channel. After ideal synchronization and FFT, the received signal on one sub-carrier at MS k is given by √ −γ Sk = PBS A1 · DBS,k Hk d + ηk (1) −γ where PBS is the BS transmission power, A1 · DBS,k represents the path loss from the BS to MS k, A1 is a constant, DBS,k is the distance between BS and MS k and γ is the path loss parameter. Hk is a complex Gaussian random variable and stands for the channel fading experienced on the sub-carrier with an average power of 1. d represents the transmitted symbol with unit power and ηk is the zero mean Gaussian noise with a variance of σ 2 = N0 B, where N0 is the power spectrum density of the noise and B is the system bandwidth. Thus, the SNR of Sk is given by −γ SN Rk = PBS A1 · DBS,k |Hk |2 /σ 2 . In order to investigate the energy efficiency of two-stage CM with guaranteed coverage Cth , the coverage performance should be studied which, as shown later, is a function of the power consumption and the number of MSs in the cell. Therefore, based on the coverage performance, it is possible to investigate the effect of user density on the energy efficiency of CM.
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A. One-stage Multicast Transmission The relationship between the coverage Cth and the BS power PBS is revisited [4]. Given a multicast data rate Rone , the required SN Rone can be obtained as SN Rone = 2Rone /B − 1 according to Shannon theory. Therefore, the SNR of received signal SN Rk must be no less than SN Rone in order to successfully receive the multicast data. Assuming that all MSs are uniformly distributed in the circular cell with a radius of R, the coverage ratio in the cell with a BS power of PBS is given by [4] ∫ R 2x Cth = P (SN Rk ≥ SN Rone ) · 2 dx R 0 2 (A1·PBS /σ 2 )2/γ ( 2 γ A1 ·PBS /σ 2 −1) = 2 ·Γ , R ( ) (2) γR SN Rone γ SN Rone ∫q where Γ(p, q) = 0 tp−1 exp(−t)dt is the incomplete gamma function.
•
B. Energy Efficient Two-stage Cooperative Multicast Transmission
&LUFOH$ % y x0
D % D x1
R-x0+y %6
Fig. 1.
R
Illustration of two-stage CM transmission. •
1) Basic Assumptions: • Transmission interval division In two-stage CM transmissions, the conventional onestage multicast transmission time T is divided into two intervals, T1 and T2 , respectively [4]. At the 1st stage T1 , the BS transmits multicast data at a rate of Rtwo,1 with a power of PBS,C such that only part of MSs can successfully receive the data. Then, at the 2nd stage T2 , some SMSs are selected to act as MRs and via cooperative transmission, MRs further deliver the multicast data at a rate of Rtwo,2 to the MSs which fail at the 1st stage (UMS: Unsuccessful MSs). After two-stage CM, the total coverage Cth will be ensured. For instance, as shown in Fig. 1, the MR at the point
B1 successfully transmits data to the UMS at B0 at the 2nd stage. Obviously, the transmission power needed by MR is much less compared to that by the BS due to the shorter distance. It should be noted that throughout this paper, the BS and MS transmission powers are treated as equally important. It is possible to introduce different importance between the BS and MS transmission power by weighting factors. For example, WBS and WM S can be defined as the importance factors of BS and MS power consumptions, respectively, where WBS , WM S ∈ [0, 1] and WM S + WBS = 1. If the MS power consumption is considered as more important, WM S can be set to a value larger than WBS . Since this paper focuses on the energy efficiency of the whole system and the value of WBS and WM S will not affect the main idea of the two-stage CM, WM S is set to be the same as WBS and both will not occur in the following contents. Signal processing at the two stages Note that there could be more than one SMS to act as MR in the cell and they all transmit the same data simultaneously at the 2nd stage, so one UMS could receive multiple copies of the signal from all MRs in the cell and all the received signals arriving within the CP duration is naturally added up to construct a stronger signal. However, due to the serious path loss, signals from remote MRs that are far away from the UMS are weak and contributes little to the success probability of the UMS. Thus, the remote MRs could be ignored and it is more realistic to assume that the UMS receives signals from nearby MRs but not all MRs. Furthermore, since this paper mainly focuses on low density scenarios, the MRs in the cell is sparsely distributed. The probability that there is more than one MR near the UMS is low. Hence, it is reasonable to assume that one UMS only receives the signal from the nearest MR at the 2nd stage and the signals from other MRs are ignored, which is named as selective combining based on average received signal strength (SCA). This assumption will be verified in Section IV by simulations, showing that the performance with SCA is close to that of combing signals from all MRs. Moreover, MRC is employed to collect the useful signals transmitted at the 1st and 2nd stages so that the signal quality can be further improved. Performance concerned Recently, high energy consumption has been a major concern for wireless networks and various green techniques have been proposed. Hence, different to [5][10][15], which aims to achieve higher throughput with CM, this paper focuses on the energy efficient two-stage CM aiming to achieve the same throughput and coverage performance as the conventional one but with lower power consumptions. So the two-stage CM provides the same services as the conventional one-stage multicast, and it can be obtained that Rtwo,1 T1 = Rtwo,2 T2 = Rone T . Then, according to Shannon theory again, the corresponding SNR thresholds at the 1st and 2nd stage of two-stage CM are given by SN Rtwo,1 = (1 + SN Rone )T /T1 − 1 and SN Rtwo,2 = (1 + SN Rone )T /T2 − 1, respectively
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[4]. In this paper, it is assumed that T1 = T2 = T /2 2 and SN Rtwo,1 = SN Rtwo,2 = SN Rtwo = SN Rone + 2SN Rone . Note that although T1 = T2 is not necessary an optimized value to minimize the total energy consumption, it is a reasonable setting which has been shown to be able to maximize the total system throughput [3]. 2) Received Signal Analysis with SCA and MRC: At the 1st stage of CM, the BS transmits at a power of PBS,C . Consider a point B0 with a distance of x0 from the BS. Similar to (1), √ the received signal at the 1st stage is given PBS,C H1 (x0 )d + η1 , where √ by S1 (x0 ) =
H1 (x0 ) = A1 · x−γ 0 HBS (x0 ) is the channel impact including the path loss and fading from the BS to B0 and η1 is the zero mean Gaussian noise with a variance of σ 2 . The SNR of received signal at B0 is thus given by SN R1 (x0 ) = PBS,C |H1 (x0 )|2 /σ 2 . So the probability that the data is successfully received (success probability) at the 1st stage is given by P (SN R1 (x0 ) ≥ SN Rtwo ) ( SN Rtwo · σ 2 ) γ = exp − −γ = exp(−λ1 x0 )(3) PBS,C · A1 x0
SCM,1 (x0 ) =
Rtwo ·σ st where λ1 = SN stage is PBS,C ·A1 . If the transmission at the 1 nd not successful at B0 , the 2 stage cooperative transmission is needed, whose performance is mainly decided by the MR transmission power PM S and MR selection schemes. For the UMS located at B0 , the√received signal at the 2nd stage could be given by S√ PM S H2 (y)d + η2 , similar 2 (y) = to (1), where H2 (y) = A2 · y −γ HM R (y) is the channel impact including the path loss and fading from the nearest MR to B0 and y stands for the distance between one UMS and its nearest MR. η2 is also a zero mean Gaussian noise with a variance of σ 2 . Moreover, it should be noted that A2 is another constant different with A1 , since different path loss models are employed for the 1st and 2nd stage transmissions [18]. It is further supposed that the UMS could employ MRC to collect useful signals transmitted at the 1st and 2nd stages, after which the signal is given by √ √ S(y, x0 ) = PBS,C H1∗ (x0 )Y1 (x0 ) + PM S H2∗ (y)Y2 (y) ( ) = PBS,C |H1 (x0 )|2 + PM S |H2 (y)|2 d √ (√ ) + PBS,C H1∗ (x0 )η1 + PM S H2∗ (y)η2 (4) 2
Therefore, the SNR of S(y, x0 ) is given by ( ) SN R2 (y, x0 ) = PBS,C ·|H1 (x0 )|2+ PM S ·|H2 (y)|2 /σ 2 (5) Thus, given the MR at a distance of y from the UMS, the success probability at the 2nd stage becomes ( ) SCM,2(y,x0) = P SN R2 (y, x0 ) ≥ SN Rtwo { γ γ γ λ2 y exp(−λ1 xγ 0 )−λ1 x0 exp(−λ2 y ) , λ2 y γ ̸= λ1 xγ0 ; γ −λ xγ λ y 2 1 = (6) 0 γ γ 1 − (1 + λ1 x0 )exp(−λ1 x0 ), λ2 y γ = λ1 xγ0 . Rtwo ·σ where λ2 = SN PM S ·A2 . It can be proved that (6) is always no less than zero (see Appendix A). As a whole, the success 2
probability after two-stage CM is given by SCM(y, x0)=1−P (SNR2 (y, x0) x0 could be ignored. This is because the transmission power of MR is much smaller than that of the BS. When the distance between the MR and UMS is larger than that between the BS and the UMS, the success probability provided by the MR could be ignored. It will be shown in Sec. III that (8) is a function of PBS,C and the number of users in the cell. So for a given coverage CCM , (8) can be used to show the effect of user density on the energy consumption of CM. Moreover, the detailed expression of p(y, x0 ) is closely related with the MR selection scheme and will be derived in Sec. III. III. EFFECT OF USER DENSITY ON TWO-STAGE CM Although CM has been shown to outperform the conventional one in various circumstances, it remains a question that if the cooperative one should always be preferred to the conventional one. In fact, from the simulation results of previous researches, one can expect that the cooperative multicast may perform worse than the conventional scheme when the number of MSs is small enough. This section dedicates to investigate the effect of user density on two-stage CM and find a lower bound below which the CM should not be employed since it consumes more power than the conventional one. The basic idea is as follows. Assuming M MRs at the 2nd stage, the total energy consumed by the two-stage CM should satisfy PBS,C T1 +M PM S T2 ≤ PBS T to be more energy efficient than the conventional one. So the BS transmission power at the 1st stage should be limited by PBS,C ≤ PBS T /T1 − M PM S T2 /T1 . Moreover, since M > 0 in the two-stage CM, PBS,C is upper bounded by PBS,C < Pmax = PBS T /T1 . Setting PBS,C = Pmax and the two-stage coverage ratio CCM = Cth , a lower bound for the number of MSs NLB could be found, below which the two-stage CM cannot provide desired coverage performance with energy consumption no higher than the conventional one and thus is not preferred. A. MR Selection Scheme First of all, the MR selection scheme should be designed when the number of MSs in the cell is small. Since both SMSs and UMSs should be sparsely distributed in the cell in this case, the probability that several UMSs are covered
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by one MR is low. Moreover, to provide fairness to all users, Obviously, the try-best scheme would be less energy effieach UMS should get the relay help from a MR, whether it cient when the user density is high because two closely located is located near the BS or at the cell edge. So it is proposed UMSs could be covered by one SMS instead of selecting two that the closest SMS to a UMS should be selected as its MR. closest SMSs separately. In this case, the scheme proposed in This is a try-best scheme since with low user density, even the [4] should be considered. closest SMS could be far away from the UMS and fail to help the UMS to be successful after the cooperative transmission at the 2nd stage. In this paper, it is assumed that the closest B. Effect of User Density SMSs ideally know that they are selected as MRs without The coverage performance of two-stage CM is given by (8), delay and will immediately transmit at the 2nd stage, while where SCM (y, x0 ) is independent of the number of MSs in in practice, a proper protocol should be designed to make the the cell. So the effect of user density is introduced by the joint try-best scheme work. For example, a practical protocol could probability distribution function (pdf) p(y, x0 ), which can be be that each SMS feeds back an ACK to the BS after the 1st written as p(y, x0 ) = p(y|x0 )p(x0 ). Since p(x0 ) = 2x0 /R2 , transmission. With ACK and location information of all MSs, the main task is to obtain p(y|x0 ), which is given by p(y|x0 ) = ′ ′ which might be obtained using terminal-based or network P (Y ≤ y|x0 ). Here, f (y) stands for the partial derivation based positioning techniques [19], the BS could decide which of f (y) over y and P (Y ≤ y|x0 ) is the probability that the SMSs are selected as MRs and notify them to transmit at the closest MR exists in the overlapped area of the cell and the 2nd stage. So in practice, first of all, a time slot is needed to circle centered at the UMS with a radius of y (see Circle A transmit the signaling. Secondly, due to the possible missing of in Fig. 1). Thus P (Y > y|x0 ) is the probability that there is ACKs, location errors and notification errors, a SMS other than no SMS/MR in the area, given by the desirable one may act as a MR. These would result in a performance loss. Since this paper focuses on the effect of user P (Y > y|x0 ) density, the imperfections caused by the practical signaling N−1 ∑ m transmission are ignored. = CNm−1 parea (y,x0)(1−parea(y,x0 ))N−1−m(1−pave(y,x0 ))m It should be noted that the try-best scheme is different m=0 ( )N −1 to the MR selection scheme in [15] (MRS[15]) where a = 1 − parea (y, x0 )pave (y, x0 ) (10) SMS becomes MR only if it receives requests from UMSs. Although MRS[15] needs no location information, UMSs where N is total number of users in the cell, p area (y, x0 ) = should send out requests, which complicates the cooperative A(y, x )/πR2 is the probability that one MS is located in the 0 communication. Moreover, MRS[15] may introduce redundant area, A(y, x ) is the overlapped area of the cell and the Circle 0 MRs when there are multiple SMSs in the communication A, given by range of a UMS. As described before, the try-best scheme can ( ) avoid this redundancy at the cost that the BS should completely A(y, x0 )= π−arcsin(Rsinα1 /y) y 2+α1 R2−R·x0 ·sinα1 (11) know MSs. In addition, considering the employment of MRC to combine the signals received at the 1st and 2nd stage, the √ where the angle α1 is illustrated in Fig. 1 and y = try-best scheme has the potential to provide better coverage R2 + x20 − 2Rx0 cos α1 . Moreover, pave (y, x0 ) is the avat low user density. When the closest SMS is out of the erage success probability provided by the BS within the communication range of a UMS, if MRS[15] is employed, overlapped area at the 1st stage, which is approximated by no MR could be selected for the UMS and there would be the average success probability within the annular area from no 2nd stage transmission. Thus, MRC cannot be employed the radius R − x0 + y to R and the UMS cannot obtain the multicast data. However, if ∫ R (−SN Rtwo σ 2) 2x the try-best scheme is used, the closest SMS always relays pave (y, x0 )= exp dx (12) −γ R2−(R−x +y)2 data to a UMS. This signal is MRC combined by the UMS P A x BS,C 1 0 R−x0+y st with signals received at the 1 stage. Hence, the UMS could possibly obtain the data from the combined signal even if it is where R − x0 + y ≤ R. Note that the success probabilities out of the communication range of its own MR. In summary, obtained when y > x0 are ignored since the transmit power the try-best scheme is employed due to its potential to save of BS is much higher than that of the MR in practice. From (10), it can be seen that P (Y > y|x0 ) decreases as N more energy and provide better coverage. The performance of the try-best scheme and MRS[15] will be compared in next increases. Thus, P (Y ≤ y|x0 ), the probability that the closest MR exists within a distance of y from the UMS increases as N section. nd The average number of MRs needed in the try-best scheme increases, and better cooperation can be expected at the 2 st stage via cooperative transmissions. As a result, the system can be approximated by the number of UMSs after the 1 performance should be improved as N increases. stage transmission, i.e., According to the definition of pdf functions, p(y|x0 ) can M M R = N (1 − CCM,1 ) (9) be obtained from (6) which is a function of PBS,C and the Note that when the number of MS is sufficiently large and number of users N . several UMS could select the same SMS as their MR, the Given (8) and (13), the coverage of CM can be numerically number of MRs may be overestimated by M M R . calculated using the definition of integration as follows
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p(y|x0 ) = =
∫
R
( )′ 1 − P (Y > y|x0 ) ( )N −1 ( )N −1 1 − parea (y + ∆, x0 )pave (y + ∆, x0 ) − 1 − parea (y, x0 )pave (y, x0 ) lim ∆→0 ∆
TABLE I S YSTEM PARAMETERS
∫
x0
CCM ≈
SCM (y, x0 )p(x0 )p(y|x0 )dydx0 0
Carrier Frequency Frequency Band B System to Device Path Loss Constant A1 Cooperative Transmission Path Loss Constant A2 Path Loss parameter γ Transmission power of BS for conventional one-stage multicast PBS Transmission Power of MS PM S Noise Power Spectrum N0 Coverage Ratio Cth Cell Radius R Rate of multicast Rone
(14)
0
= lim
M →∞
(13)
M ∑ M ∑
SCM,2 (ηi,j , ξi )p(ξi )p(ηi,j |ξi )
i=1 j=1
R ξi MM
M ∑ M ∑
2ξi = lim SCM,2 (ηi,j , ξi ) M →∞ RM i=1 j=1 (( )N −1 ξi ξi · 1 − parea (ηi,j + , ξi )pave (ηi,j + , ξi ) M M ( )N −1 ) − 1 − parea (ηi,j , ξi )pave (ηi,j , ξi ) where the integration region is divided into M × M small ξi R square regions, ξi = M i and ηi,j = M j. Using (3), (6), (11) and (12), CCM can be calculated for any given PBS,C and N . Let CCM = Cth , then the smallest N that could ensure the coverage performance with PBS,C = Pmax is NLB , which is a lower bound for the user density, below which the two-stage CM cannot provide desired system performance with energy consumption no higher than the conventional one.
2.5G 10M 2.36e-2 1.67e-4 4 43dBm 21dBm -174dBm/Hz 95% 1500m 0.45bps/Hz
MS) transmission because of the differences in the antenna height and reference distance [18]. If not noted, SCA plus MRC are employed by the UMS. B. Numerical and Simulation Results
1 0.95
C. Future Work
0.9 N=60
Coverage
Note that the proposed try-best MR selection scheme is a centralized scheme which needs the location information of all MSs in the cell to obtain the optimized MR selection. This may result in complicated signaling when the scheme is applied in real systems. To simplify the signaling and facilitate the application of CM, one possible future work is to design distributed MR selection schemes where one SMS decides to be MR or not by itself. Moreover, the mobile communication network is evolving into a heterogonous network [20-22] consisting of macro cells and various small cells. Macro and small cells could also collaborate to further improve the performance of CM. This would be another interesting future work of CM.
0.85 0.8 0.75 N=2
A. System Configurations The system parameters are shown in Table I [23] where the target coverage ratio Cth is chosen to be 95%, the BS transmission power with the conventional one-stage multicast PBS is 20Watt (43dBm) and the MS transmission power PM S is 0.13W (21dBm). Thus, Pmax = 2PBS = 40W since T1 = T2 = T /2. The radius of the macro cell is set to 1500m. Moreover, two different path loss models are employed for the system to device (BS to MS) and the cooperative (MR to
Numercial Result Simulation Result Conventional Multicast
0.7 0.65
0
Fig. 2.
IV. PERFORMANCE EVALUATIONS
N=30
20
40 PBS,C(W)
60
80
Coverage performance as a function of PBS,C with differen N .
Using SCA plus MRC, the coverage ratio of two-stage CM CCM can be calculated according to (14) as a function of PBS,C for any given number of users N . The numerical results are shown in Fig. 2 when there are N =2, 30 and 60 users in the cell and verified by simulations. It can be seen that the numerical and simulated coverage performance is close to each other, demonstrating that the approximation used in (8) and (12) is reasonable. For the performance as a function of PBS,C , at first CCM improves rapidly as PBS,C increases from a small value. When CCM passes 95%, the coverage
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18 Numerical Relsult Simulation Result
16
14
N LB
enhancement becomes marginal as PBS,C increases further. So it is power inefficient to enhance the coverage performance beyond 95%. To provide a coverage ratio of 95%, the required ∗ BS transmission power at the 1st stage PBS,C are about 46, 33 and 23W for N =2, 30 and 60, respectively. Obviously, when there are only two users, CM is not a good idea which ∗ consumes more power (PBS,C = 46W > Pmax = 40W ) ∗ than that of the conventional one. Fortunately, PBS,C reduces significantly as N increases. This is because the number of SMSs gets larger with N and UMSs can get relay help from more SMSs via cooperative transmission at the 2nd stage. Thus the BS transmission power at the 1st stage could be reduced. As a whole, it can be seen that as the user density increases, CM has higher potential to be energy efficient. Given PBS,C =
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Pmax = 40W , the coverage performance CCM is shown in Fig. 3 as a function of N and verified by simulations. It can be seen that CCM improves as N increases. To guarantee a coverage ratio of 95% with the two-stage CM, there should be at least N =13 users in the cell. Thus, a lower bound for the user density is NLB =13. Moreover, the lower bound is shown in Fig. 4 as a function of the MS transmission power PM S . It can be seen that when PM S increases, the corresponding lower bound gets smaller. This is because a MR with a higher transmission power PM S has a stronger capability to relay data to the UMS through cooperative transmission. Thus, to ensure the desired coverage ratio, less MRs are needed, resulting in a smaller lower bound of user density. Next, using the try-best MR selection scheme, the average number of MRs needed in the two-stage CM is plot in Fig. 5. It is shown that the estimated value M M R (see (9)) is close to the simulation results when N is small. As N increases, M M R gradually deviates from the real value and the gap increases with N . This is because when N gets larger, there is a higher probability that several closely located UMSs could select the same MR. Since M M R is obtained by assuming one MR for one UMS, overestimation happens when N is large. Moreover, the total power consumption is shown in Fig. 6 for two-stage CM with try-best MR selection scheme. The
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power consumption of conventional scheme is 20W and also plot in the figure. For any given N , to satisfy CCM = Cth , the ∗ corresponding BS transmission power at the 1st stage PBS,C can be numerically found. Then, using the approximated number of MRs M M R , the total power consumption can be ∗ estimated by Ptot = PBS,C · TT1 + M M R · PM S · TT2 . The estimated total power is also compared to the simulation result. It can be seen that the total power consumption decreases considerably as the number of users N increases, demonstrating the effectiveness of energy efficient CM when user density becomes high. Although the power consumption of MRs (or the number of MRs, equivalently) increases with N (see Fig. 5), it is much smaller than the BS transmission power at the 1st stage PBS,C , which is reduced a lot as N increases (see Fig. 2). As a whole, the total power consumption of CM decreases as N becomes larger. For N =100, a reduction of 50% can be achieved by CM compared to the power consumption of the conventional one. It can also be seen that the estimated and simulated values matches well. Although the estimated average number of MRs M M R becomes an upper bound when N increases, the estimation error is not big. Moreover, the transmission power of MS PM S is much smaller than PBS,C , so the effect of estimation error on the total power consumption is marginal. Hence, it can be concluded that the estimation M M R (9) works well in the investigation of energy efficiency of CM with low user density. 24 SCA(δ=0) SCA(δ=0.25) MRC+SCA(δ=0) MRC+SCA(δ=0.25) MRC+CPC(δ=0) MRC+CPC(δ=0.25) Conventional Multicast
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Total power consumption with different signal processing schemes.
Next, the assumption of SCA is verified by comparing the power consumption performances of SCA and other signal combining schemes. Considering try-best MR selection, the total power consumption is plot in Fig. 7 as a function of N for three different combinations, i.e., SCA without MRC, SCA plus MRC (scheme used in analysis) and CP combining (CPC) plus MRC, where CPC is the practical signal constructing process where the signals from all the MRs in the cell are located in the CP duration and form a stronger signal. It can be seen that the total power consumption reduces significantly as N is increased from 10 to 100. When SCA is employed without MRC, N =24 users are needed to make the two-stage CM more energy efficient than the conventional one. When
MRC is introduced, only N =13 users are needed to enable the two-stage CM. In general, SCA plus MRC could provide about 2W reduction in the total power consumption compared to SCA without MRC. Moreover, the performances of SCA plus MRC and CPC plus MRC are almost the same when N is less than 70. As N is increased to beyond 70, CPC starts to outperform SCA. This is because when the number of MSs in the cell is small, there could be only a few SMSs which are sparsely located in the cell. So the distances between all the selected SMSs and a UMS should be relatively large. Even if the signal transmitted from all MRs could be collected by CPC at the UMS, the contributions from remote MRs are negligible and the success probability after cooperative transmission at the 2nd stage should still be decided by the MR that is the closest to the UMS. When the number of MSs in the cell increases, the possibility becomes higher that there is more than one MR located near a UMS, in which case CPC provides much better performance than SCA. In summary, the performance difference between CPC and SCA is negligible for small N . Therefore, the lower bound of user density obtained with SCA plus MRC is a good estimation for a practical system where CPC plus MRC is considered. Moreover, note that in practice, the MRs participating in cooperative multicast at the 2nd stage need to encode the received message for relaying and this transmit signal processing also consumes energy. According to [24-25], it is reasonable to assume that this part of power consumption at a MR node should be no larger than 25% of its transmission power. Thus, to introduce the signal processing power at the MRs, the total power consumption can be modified as Ptot = PBS,C · TT1 + PM S · TT2 · NM R · (1 + δ), where δ = 0.25 and δ = 0 represent the situations with and without signal processing power consumption, respectively. It can be seen from Fig. 7 that for different receive signal processing schemes, by taking the transmit signal processing energy at the MR nodes into consideration, the total power consumption is slightly increased, and the increment gets larger with the number of users. For the considered low density scenario with less than 100 users, the results are still close to those without the signal processing energy. This is because although for each MR node, the signal processing power consumption is not negligible compared to the transmission power, the total power consumption at all the MR nodes are still small compared to that at the BS node due to the relatively low transmission power and the small number of MRs with low user density. For example, when there are totally 100 users, using CPC plus MRC, the power consumption at the BS is 8.69W while this number at the MR nodes is 0.85W and 0.68W with and without signal processing power consumption, respectively. Therefore, in case of low user density, the signal processing power consumption at the MR nodes could be ignored when investigating the energy efficient CM. However, if high user density is concerned, the impact of the signal process power consumption could be more significant and should be considered in the evaluation. Finally, in terms of spectrum efficiency per watt, the energy efficiency of the proposed two-stage CM with try-best MR selection is compared to that with MRS[15] (a SMS becomes
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MR only if it receives requests from UMSs) and that with the scheme in [5] (all SMSs become MRs, denoted as Scheme[5]). All schemes ensure a coverage ratio of 95%. When MRS[15] is considered, the communication range of a UMS is set to 100 meters, within which the success probability of a SMS receiving the request can be approximated to be 100%. CPC plus MRC is employed with MRS[15]. Scheme [5] employs CPC but MRC cannot be used since the transmitted data at the 1st and 2nd stages may be differently encoded and modulated to provide different data rates. Simulation results are shown in Fig. 8. First of all, it can be seen that given same conditions, the power efficiency of SCA plus MRC is close to that of CPC plus MRC when the user density is low. As the user density increases, the performance gap becomes obvious. Next, with the same signal processing of CPC plus MRC, the try-best MR selection scheme provides better power efficiency than MRS[15]. This is because MRS[15] may introduce more than one MR to a UMS when there are multiple SMSs in its communication range. Moreover, the proposed two-stage CM with try-best with or without MRC could always provide much higher energy efficiency than that of Scheme[5] and the performance gap gets larger with N . This is because the total power consumption of Scheme[5] increases with the number of MSs by using all SMSs as MRs. In summary, the proposed scheme is more energy efficient than both MRS[15] and Scheme[5]. V. CONCLUSIONS This paper investigated the effect of user density on the energy consumption of two-stage CM. A try-best MR selection scheme has been proposed for low user density and the coverage performance analysis has been carried out for the twostage CM. A lower bound of user density could be numerically obtained for the two-stage CM to be energy efficient. All analytical results are verified by simulations. The following conclusions are drawn: 1) There exists a lower bound of user density, below which two-stage CM could not provide the same performance as the
conventional one-stage multicast with less energy consumption, and conventional schemes should be employed. 2) For a cell with a radius of 1500m and a multicast data rate of 0.45bps/Hz, a lower bound of user density is found to be 13, which decreases with the increasing of the MR transmission power. 3) The total power consumption of two-stage CM reduces significantly with the increasing of the number of MSs. For N =100, a reduction of 50% can be achieved compared to the power consumption of conventional ones. 4) With low user density, the performances of SCA and practical signal processing are close to each other. Thus the SCA assumption is reasonable and could provide an accurate estimation for real system performance. Moreover, MRC could effectively reduce the lower bound of user density from 24 to 13. 5) The proposed two-stage CM with try-best MR selection provides better energy efficiency than the existing schemes such as MRS[15] and Scheme[5] do. A PPENDIX A The target of appendix A is to show that (6) is non-negative, which is given by ( ) SCM,2 (y, x0 ) = P SN R2 (y, x0 ) ≥ SN Rtwo { γ γ γ λ2 y exp(−λ1 xγ 0 )−λ1 x0 exp(−λ2 y ) , λ2 y γ ̸= λ1 xγ0 ; γ −λ xγ λ y 2 1 = 0 γ γ 1 − (1 + λ1 x0 )exp(−λ1 x0 ), λ2 y γ = λ1 xγ0 . It can be seen that λ1 xγ0 and λ2 y γ are both positive values Rtwo ·σ 2 SN Rtwo ·σ 2 since λ1 = SN PBS,C ·A1 and λ2 = PM S ·A2 . We firstly γ investigate the expression when λ2 y ̸= λ1 xγ0 . If λ2 y γ > λ1 xγ0 , exp(−λ1 xγ0 ) will be larger than exp(−λ2 y γ ) since the function f (x) = exp(−x) is a monotone decreasing function. Thus the numerator will be a positive value and (6) is positive, too. Similarly, if λ2 y γ < λ1 xγ0 , (6) is also positive. Next, when λ2 y γ = λ1 xγ0 , a new function f (x) = 1 − (1 + x)exp(−x) is defined firstly, the derivation of which is given ′ ′ by f (x) = xexp(−x). Obviously, f (x) ≥ 0,when x ≥ 0. Thus f (x) = 1 − (1 + x)exp(−x) is a monotone increasing function. Note that f (x) = 0 for x = 0, so (6) is nonnegative when λ2 y γ = λ1 xγ0 ≥ 0. In conclusion, (6) is always nonnegative for any values of λ1 xγ0 and λ2 y γ . R EFERENCES [1] S. Carson, I. Godor, P. Kersch, A. Kalvmark, G. Lemne and P. Lindberg. (2015, June 3). Ericsson Mobility Report: on the pulse of the networked society [Online]. Available:http://www.ericsson.com/res/docs/2015/ericsson-mobilityreport-june-2015.pdf. [2] Evolved Universal Terrestrial Radio Access (E-UTRAN) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Overall description, TS 36.300 v10.3.0, 2011. [3] B. Niu, H. Jiang and H. Zhao, “A Cooperative Multicast Strategy in Wireless Networks,” IEEE Trans. Veh. Technol., vol. 59, no. 6, pp. 31363143, Jul. 2010. [4] Y. Zhou, H. Liu, Z. Pan, L. Tian, J. Shi and G. Yang, “Two-Stage Cooperative Multicast Transmission with Optimized Power Consumption and Guaranteed Coverage,” IEEE J. Sel. Areas Commun., vol. 32, no. 2, pp. 274-284, Feb. 2014. [5] F. Hou, L. Cai, P.H. Ho, X. Shen and J. Zhang, “A cooperative multicast scheduling scheme for multimedia services in IEEE 802.16 networks,” IEEE Trans. Wireless Commun., vol. 8, no. 3, pp. 1508-1519, Mar. 2009.
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[6] H. Zhao and W. Su, “Cooperative wireless multicast: performance analysis and power/location optimization,” IEEE Trans. Wireless Commun., vol. 9, no. 6, pp. 2088-2100, Jun. 2010. [7] X. Ge, B. Yang, J. Ye, G. Mao, C.-X. Wang and T. Han, “Spatial Spectrum and Energy Efficiency of Random Cellular Networks,” IEEE Trans. Commun., vol. 63, no. 3, pp. 1019 - 1030, Mar. 2015. [8] X. Ge, H. Cheng, M. Guizani and T. Han, “5G Wireless Backhaul Networks: Challenges and Research Advances,” IEEE Network, Vol. 28, No. 6, pp. 6-11, Nov. 2014. [9] L. Xiang, X. Ge, C.-X. Wang, Frank Y. Li and Frank Reichert, “Energy Efficiency Evaluation of Cellular Networks Based on Spatial Distributions of Traffic Load and Power Consumption,” IEEE Trans. Wireless Commun., vol. 12, no. 3, pp. 961-973, Mar. 2013. [10] T. Han and N. Ansari, “Energy efficient wireless multicasting,” IEEE Commun. Lett., vol. 15, no. 6, pp. 620-622, Jun. 2011. [11] H. Eghbali, S. Muhaidat, S. Hejazi and Y. Ding, “Relay Selection Strategies for Single-Carrier Frequency-Domain Equalization MultiRelay Cooperative Networks,” IEEE Trans. Wireless Commun., vol. 12, no. 5, pp. 2034-2045, May. 2013. [12] Y. Li, Q. Yin, W. Xu and H.-M. Wang, “On the Design of Relay Selection Strategies in Regenerative Cooperative Networks with Outdated CSI,” IEEE Trans. Wireless Commun., vol. 10, no. 9, pp. 3086-3097, Sep. 2011. [13] C. W. Xing, S.D. Ma, Z.S Fei, Y. C. Wu and V. Poor, “A General Robust Linear Transceiver Design for Multi-Hop Amplify-and-Forward MIMO Relaying Systems,” IEEE Trans. Signal Process., pp.1196-1209, Mar. 2013. [14] S. M. Elrabiei and M. H. Habaebi, “Energy efficient cooperative multicasting for MBS WiMAX traffic,” IEEE ISWPC2010, pp. 600-605, May. 2010. [15] J. Lee, Y. M. Lim, K. Kim and S. G. Choi, ”Energy Efficient Cooperative Multicast Scheme Based on Selective Relay,” IEEE Commun. Lett., vol. 16, no. 3, pp. 386-388, Mar. 2012. [16] Y. Zhou, H. Liu, Z. Pan, L. Tian, J. Shi, “Spectral - and energy-efficient two-stage cooperative multicast for LTE-advanced and beyond,” IEEE Wireless Commun., vol. 21, no. 2, pp. 34-41, Apr. 2014 [17] H. Zhu and J. Wang, “Chunk-based Resource Allocation in OFDMA Systems - Part II: Joint Chunk, Power and Bit Allocation,” IEEE Trans. Commun., vol. 60, no. 2, pp. 499-509, Feb. 2012. [18] R1-132316, Device to Device Channel Model, Nokia, 3GPP TSG-RAN WG1 Meeting #73, Fukuoka, Japan, May. 2013. [19] I. A. Junglas and R. T. Watson, “Location Based Services”, ACM Commun., vol. 51, no. 3, pp. 65-69, Mar. 2008. [20] G. Wang, Q. Liu, R. He, F. Gao and C. Tellambura, “Acquisition of channel state information in heterogeneous cloud radio access networks: challenges and research directions,” IEEE Wireless Commun., vol. 22, no. 3, pp. 100-107, Jun. 2015. [21] Z. Zhang, K. Long, J. Wang, F. Dressler, “On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches”, IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 513-537, FIRST QUARTER 2014. [22] F. Gao, J. Li, T. Jiang and W. Chen, “Sensing and Recognition When Primary User Has Multiple Transmit Power Levels, ” IEEE Trans. Signal Process., vol. 63, no. 10, pp. 2704-2717, May 2015. [23] S. Y. Baek, Y-J. Hong and D. K. Sung, “Adaptive Transmission Scheme for Mixed Multicast and Unicast Traffic in Cellular Systems,” IEEE Trans. Veh. Technol., vol. 58, no. 6, pp. 2899-2908, Jul. 2009. [24] R. Hu, Y. Qian and C. Li.(2013, Apr 10). Spectrum and energy efficient heterogeneous wireless networks [Online]. Available: http://wcnc2013.ieee-wcnc.org/WCNC.T5.Slides.pdf. [25] T. Chen, Y. Yang, H. Zhang, H. Kim and K. Horneman, “Network energy saving technologies for green wireless access networks,” IEEE Wireless Commun., vol.18, no.5, pp.30-38, Oct. 2011.
Yiqing Zhou (S’03-M’05-SM’10) received the B.S. degree in communication and information engineering and the M.S. degree in signal and information processing from the Southeast University, China, in 1997 and 2000, respectively. In 2004, she received the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong, Hong Kong. Now she is a professor in Wireless Communication Research Center, Institute of Computing Technology, Chinese Academy of Sciences. Dr. Zhou has published over 80 papers and three book/book chapters in the areas of wireless mobile communications. Dr. Zhou is the associate/guest editor for IEEE Trans. Vehicular Technology (TVT), IEEE JSAC (Special issue on Broadband Wireless Communication for High Speed Vehicles and Virtual MIMO), WCMC, ETT and JCST. She is also the TPC co-chair of ChinaCom2012, symposia co-chair of IEEE ICC2015, symposium co-chair of ICC2014, tutorial co-chair of ICCC2014 and WCNC2013, and the workshop co-chair of SmartGridComm2012 and GlobeCom2011. She received Best Paper Awards from IEEE PIMRC2015, ICCS2014 and WCNC2013. She also received the 2014 Top 15 Editor Award from IEEE TVT.
Hang Liu received the B.S. degree in Mathematics and Applied Mathematics from Northwestern Polytechnical University in 2010 and the M.S. degree in Wireless Communications from Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) in 2013. He is currently a Ph.D. candidate in University of Chinese Academy of Sciences. He is the receiver of Best Paper Award of IEEE WCNC2013. He has been serving as a reviewer for multiple international journals and conferences. His research focuses on cooperative multicast, vehicular network,interference management and network information theory.
Zhengang Pan , IEEE Senior Member, senior director of 5G program of Spreadtrum, is now leading a team working on the key technologies of next generation (5G) wireless communication systems. Dr. Pan was also the vice-chair of technical WG of China IMT-2020 PG. Before join Spreadtrum, Dr. Pan was a principle staff of China Mobile Research Institute (CMRI), where he ran the 5G research team for more than three years. Dr. Pan has also been working with HongKong ASTRI for more than 6 years where he has been 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. Dr. Pan 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. Dr. Pan received his Ph.D degree in year 2004, from Department of Electrical and Electronic Engineering, the University of HongKong. Dr. Pan is expertised in many technical fields including time/frequency/sampling synchronization technology for singlecarrier/ multi-carrier(OFDM/A) based system, channel estimation, forward error correction coding, multiple antennas systems (MIMO) and space-time processing/coding, cross layer optimization and so on. Dr. Pan has published more than 60+ papers in top journals and international conferences, and filed 50+ patents with 30+ granted so far.
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Lin Tian (M’07) 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 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.
