Non-orthogonal Direct Access for Small Data Transmission in Cellular MTC Networks

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Non-orthogonal Direct Access for Small Data Transmission in Cellular MTC Networks"

Transcription

1 Non-orthogona Direct Access for Sma Data Transmission in Ceuar MTC Networks Keng-Te Liao, Chia-Han Lee, Tzu-Ming Lin, Chien-Min Lee, and Wen-Tsuen Chen Academia Sinica, Taipei, Taiwan Industria Technoogy Research Institute, HisnChu, Taiwan Abstract In conventiona LTE networks, user equipments (UEs) access the network via a four-step random access procedure. When sma amount of data is transmitted, such as in the scenarios of machine-type communications (MTC), signaing overhead resuted from the random access procedure becomes a critica issue. To enabe an efficient sma data transmission, a direct access scheme utiizing code division mutipexing (CDM) is proposed in this paper. Instead of transmitting on the resource bocks indicated by enb, UEs randomy seect resource bocks for direct data transmission without signaing enb, with CDM used to resove the coision probem. Under the proposed architecture, however, the data transmission is inherenty non-orthogona and the enb has to detect the data without any information from UEs, making the receiver too compex to impement. To ease the burden of the receiver, we design a pseudo-random resource seection scheme such that the search space of the data detection is reduced. The anaysis and simuation resuts show the advantage of using the proposed direct access scheme to improve the efficiency of sma data transmission in ceuar networks. I. INTRODUCTION In current LTE networks, a user equipment (UE) acquires UE-specific resources for transmission by a four-step random access procedure [1]. First, the UE transmits a random access preambe generated by a Zadoff-Chu (ZC) sequence on the upink random access channe (RACH). The transmission of the random access preambe is used to inform the enb the attempt of random access. Then, the UE waits for the random access response (RAR). Once the enb decodes the preambe successfuy, the enb repies an RAR containing preambe identifier, initia upink grant, and the assignment of the ce radio network temporary identifier (C-RNTI) on the downink shared channe (DL-SCH). After receiving the RAR, the UE conveys connection request with the UE identifier on the upink shared channe (UL-SCH). Finay, the contention resoution is sent by the enb to inform the UE that the connection is estabished. When sma amount of data is transmitted, such as in the scenarios of machine-type communications (MTC) [2], the efficiency probem arises due to signaing overhead. The extensive signaing between UE and enb foowing the four-step random access procedure for transmitting sma amount of data is extremey inefficient. To achieve ow signaing overhead for sma data transmissions in ceuar networks, we propose a direct access scheme in this paper. UEs transmit data on the resource bocks (RBs) seected randomy, instead of on the RBs instructed by the enb via the four-step random access procedure. Due to the inherent non-orthogona transmissions, a coision may happen when there are more than one UE that use the same RBs. To cope with the coision probem, code division mutipexing (CDM) is used on top of the singe-carrier frequency-division mutipe access (SC-FDMA) to form SC-FDMA-CDM for aowing different UEs to share the same resources by choosing different spreading codes. CDM not ony prevents different UEs from coision but aso provides spreading gain, aowing using higher channe code rate with negigibe performance degradation. Nevertheess, without any signaing between UE and enb under the proposed architecture, in which the resources used by UEs are unknown to the enb, the enb has to try a possibe combinations of RBs and spreading codes (due to CDM) in order to detect the data, incurring a significant receiver burden. To reduce the receiver compexity, a pseudo-random resource seection scheme is proposed. The UE chooses the resource bocks and spreading codes for transmission using the mapping tabe generated according to a UE-specific pseudorandom mapping. The proposed scheme significanty reduces the compexity with negigibe performance oss, as proved by the anaytica and simuation resuts. There exist some works that try to reduce the signaing overhead caused by the random access procedure. In a paper by Zhou et a. [3], a contention-based random access scheme is proposed. UEs transmit data on the RBs seected randomy, and the information about which resource bocks are seected by the UEs is transmitted on the physica upink contro channe (UCCH). Then, the enb preserves resources to perform a coision-free access for the coided UEs. Athough their scheme is (semi-)contention-based, signaing exchange between UE and enb is sti required. In a paper by aiva et a. [4], devices which need to access enb periodicay avoid the random access procedure by informing the enb the next time(s) when they need a connection. Athough their proposed scheme reduces the signaing overhead, it ony appies to stationary devices with periodic transmissions. In a paper by Bas et a., binary BCH codes are optimized for sma data transmissions [5], but their proposed scheme does not reduce signaing overhead. II. SC-FDMA-CDM As shown in Fig. 1, the proposed SC-FDMA-CDM system is buit on top of the traditiona LTE upink network [6], [7], [8], [9], [1] by incorporating CDM into the SC-

2 Fig. 2. seection. roposed transmitter architecture with pseudo-random resource Fig. 1. Bock diagram of the LTE upink shared channe (UL-SCH) and the proposed SC-FDMA-CDM physica upink shared channe (USCH). The grayed bock is the new functiona bock in SC-FDMA-CDM that does not appear in the traditiona LTE USCH. FDMA system. The spreading matrix is defined as S L = [s [,...,s i,...,s L 1 ] T, where L ] is the spreading factor, s i = 2π 1 i 2π (L 1) i j 1,e L j,...,e L and ( ) T means transpose. Under the proposed system, the number of occupied RBs is increased by the spreading factor L. To achieve spectrum efficiency, we et the SC-FDMA-CDM signas occupy the same number of RBs with the same number of data bits as in the conventiona SC-FDMA system by increasing the channe code rate by L. Thus, given a channe code rate R in the SC-FDMA system, the corresponding channe code rate in the SC-FDMA-CDM system with the spreading factor L is L R. III. SEUDO-RANDOM RESOURCE SELECTION The proposed direct access scheme for removing the signaing procedure in the conventiona random access reies on the UEs to perform data transmission on the randomy seected resource bocks with randomy seected spreading codes. Nevertheess, the combination of choices of RBs and spreading codes produces a arge search space and yieds high receiver compexity. A simpe soution is to et UEs choose a segment of continuous RBs for transmission and determine the spreading codes according to some pre-determined rues to reduce the search space. However, using continuous RBs may cause high coision probabiity and resut in poor system performance, as wi be shown ater. As a resut, we propose a resource seection scheme which pseudoy randomizes the utiization of RBs and spreading codes such that the search space is sma compared to the random seection approach and the system performance is much better than the continuous seection scheme. A. Transmitter Design The transmitter architecture for the pseudo-random resource seection is iustrated in Fig. 2. The goa is to randomize both the RB utiization and the spreading code seection. To randomize the RB utiization, a UE needs first to choose a cycic shift vaue α N randomy from to α max (the defaut vaue of α max is the number of subcarriers minus one, e.g., 11) as a parameter for generating the reference signa and the RB mapping tabe. The mapping tabe is generated by using a UE-specific mapping matrix. Let B be the number Fig. 3. Exampe of the generation of resource bock mapping tabe. α =1, B =15, and the size of the mapping matrix is 3 5 (N v =3and M v =5). of resource bocks. The set of ordered RB index (from the smaest to the argest) I = {1, 2,,B} is shifted circuary according to the cycic shift vaue α chosen by the UE and becomes I α. The indices are then written row-wise into a mapping matrix v V with size N v M v chosen randomy from a set of matrices V. If the number of entries of the mapping matrix is arger than the tota number of avaiabe RBs, dummy zeros are appended to the input. For exampe, if the ordered indices [1, 2,...,15] are appied with a 4 4 matrix, the sequence to be written into the mapping matrix is [, 1, 2,...,15]. Fig. 3 iustrates an exampe of the generation of the resource bock mapping tabe. If UE chooses 1 as the cycic shift vaue, i.e., α = 1, then the shifted indices become [15, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14]. The shifted indices are written into a 3 5 matrix row-wise and read out coumn-wise, resuting in a resource bock mapping tabe [15, 5, 1, 1, 6, 11, 2, 7, 12, 3, 8, 13, 4, 9, 14]. In order to further increase the randomness of the resource pattern, the writein order at even rows of the mapping matrix may be atered according to the row cycic shift vaue u determined by the UE. For exampe, the write-in order [6, 7, 8, 9, 1] may be changed to [7, 8, 9, 1, 6] in the second row. After this step, the RB mapping resut I α,v,u is obtained by reading out the indices from the mapping matrix coumn-wise. The dummy zeros are ignored during the read-out. After the resource bock mapping tabe is constructed, the UE picks a number τ (virtua index) such that a segment of continuous RBs with a ength of K indices starting from the τth eement of I α,v,u is chosen. Let the coection of the τth to the (τ +K 1)th eements of I α,v,u be denoted as I α,v,u,τ,k. The RBs indexed by I α,v,u,τ,k (physica index) are used for data transmission. For exampe, if α =1, v is a 3 5 matrix, u =, τ =4, and K =6, then I 1,v,,4,6 =[1, 6, 11, 2, 7, 12].

3 Fig. 4. roposed receiver architecture for the pseudo-random resource seection scheme. The demoduation reference signa (DM-RS) used in the conventiona upink, however, is not suitabe for SC-FDMA- CDM. In the conventiona SC-FDMA, the ength of DM-RS is equa to the number of occupied RBs. For exampe, if there are 3 RBs, the ength of DM-RS is 36. However, in the proposed scheme, due to the remova of signaing procedures, there is no information about the parameters used by the UE for the enb to determine the reference signa. Moreover, different UEs may occupy the same resource bocks and corrupt the orthogonaity of reference signas. Therefore, the reference signas are designed to be bunded with the resource bock seection in the proposed SC-FDMA-CDM system: reference signas are right (circuary) shifted α within the RB if α is chosen in the resource bock seection. To randomize the spreading code seection, we propose to use the RB index I α,v,u,τ,k, the entry number of RB indices, and the cycic shift α to generate the code index C i of the ith corresponding RB with the formua C i = mod(i α,v,u,τ,k [i]+i + α, L), i =1, 2,...,K. (1) For exampe, assume I α,v,u,τ,k =[1, 6, 11, 2, 7, 12] and α = 1. Then, the spreading code pattern can be derived as C = mod([1, 6, 11, 2, 7, 12] + [1, 2, 3, 4, 5, 6] + [1, 1, 1, 1, 1, 1], 3) = [,,, 1, 1, 1], which corresponds to using spreading codes s, s, s, s 1, s 1, and s 1, respectivey. Data is then transmitted on the RBs using the seected spreading code pattern through the upink physica ayer (UL-HY) SC-FDMA-CDM signa processing chain. B. Receiver Design At the receiver side, as shown in Fig. 4, the received signa first goes through the reference signa detection, which checks the RBs with every known reference signa pattern to indicate which cycic shift α is used and whether an RB is used for data transmission or not. The receiver first assumes an α vaue and starts decoding the data. A α vaues are then tried in order to infer by which combinations the decoded data can pass the CRC. Mathematicay, by fixing α, the detector output Q α,i of the ith RB is Q α,i = α max k= r i[k]xα[k], where r i is the received reference signa at the ith RB, X α is the known reference signa with cycic shift α, and * means conjugate. Q α,i is normaized to Q α,i and then compared to a threshod γ for a RBs. Let J α be the coection of RB indices that Q α,i γ (γ is set to.5 in this paper). The receiver then finds the continuous segment of RB indices resuted from the cycic shift α, the mapping matrix v, and the row cycic Fig. 5. Exampe of the derivation of τ. shift u that match J α (but maybe in different order). That is, τ =argmax τ I α,v,u,τ,k J α, where the operation searches for the common eements and the operation counts the number of eements. After this step, the estimated index τ indicating the starting point of the continuous segment of virtua indices can be found. Fig. 5 iustrates how the vaue τ is derived using the exampe in Fig. 3, assuming no noise and equa received power from both UEs. The reference signas from two different UEs are overapped on some RBs, and the receiver detects signas on RBs 1, 2, 3, 6, 7, 11, and 12, i.e., J α =[1, 2, 3, 6, 7, 11, 12]. One can easiy check that the two τ vaues that match this detection resut are τ =4and τ =5, with the former τ corresponds to [1, 6, 11, 2, 7, 12] and the atter corresponds to [6, 11, 2, 7, 12, 3]. Now the spreading codes can be derived since the cycic shift α is assumed, the mapping matrix is known, and the index τ can be estimated. Then the receiver can decode the data and check whether the decoded data passes the CRC. By trying a the combinations of cycic shift vaue α, mapping matrix v, and row cycic shift u, the UE data can be decoded. C. arae Interference Canceation (IC) Since the interference may deteriorate the system performance, parae interference canceation can be appied. The time-domain received signa r is first converted to the frequency-domain signa by performing DFT after removing the cycic prefix. During the th iteration, the receiver appies the same procedure as at the transmitter side to the decoded bits of UE m from the ( 1)th iteration in order to ˆb ( 1) m generate the CDM-spread signa ˆ D() m. The re-generated CDMspread signa is then passed through the channe H m, which is assumed known. The interference from UE m can then be ˆb () i estimated as I m () = H ˆ D() m m. The new data bits for UE i are obtained by passing the interference-canceed signas r () i = r i M m=1 m i I() m, i =1,...,M to the detector, where M represents the number of UEs. Note that in the proposed receiver (Fig. 4), the IC is optiona. IV. ANALYSIS OF COLLISION ROBABILITY AND COMLEXITY In this section, we derive the coision probabiity and compare the compexity of different schemes. The foowing three cases are considered: (a) : Both RBs and spreading codes are seected randomy, (b) : RBs

4 Fig. 6. Exampe of RB distribution for B =6, M =4, and K =3. are determined by seecting a segment of continuous RBs, and spreading codes are determined by a pre-determined rue, and (c) seudo-random: RBs are determined by a UE-specific mapping matrix, and spreading codes are determined by a predetermined rue. This is the proposed scheme. A. Anaysis of Coision robabiity A coision happens when different UEs occupy the same RB with the same spreading code used. We begin with anayzing the coision probabiity of the random resource seection scheme. We seect one UE as the UE of interest (UOI) and discuss the coision scenarios caused by the remaining M 1 UEs. First, et us define the RB coision indication matrix R with entries being r i,j,i =1,...,M 1,j =1,...,K, and the RB coision number vector d =[d 1,d 2,...,d K ], where d q = M 1 p=1 r pq, 1 q K (i.e., d q indicates the number of coided RBs on the qth RB of UOI). Fig. 6 iustrates an exampe of RB distribution for six avaiabe RBs, four UEs, and three RB utiization per UE, i.e., B =6, M =4, and K =3. By assuming that UE 1 is the UOI and there are respectivey three and two RBs coiding with the second and third RBs of UE 1, as shown in Fig 6, then the RB coision indication matrix is R = and the RB coision 1 1 number vector is d =[, 3, 2]. The number of ( possibe RB ) coision number vectors, N d,is M + K 1 given by N d =. Since different permutations K of R may have the same d, we denote the RB coision number vector and the RB coision indication matrix respectivey as d (i) and R (j,i) where 1 i N d and 1 j N (i) N (i) R R ; is the number of permutations of the RB coision indication [ matrices corresponding ] to d (i). Denote t (j,i) = t (j,i) 1,t (j,i) 2,...,t (j,i) M 1 as the number of 1 s in each row of R (j,i), i.e., the tota number of coided RBs with the UOI for each UE. Then, the number of RB combinations corresponding to d (i) is derived as ( )( N (i) B K CR = K χ ) N (i) R j=1 M 1 =1 ( ) B K K t (j,i) N (j,i), (2) where χ is the number of RBs used by the UOI that are coided, and N (j,i) is the number of different permutations of R (j,i). In (2), χ can be computed as χ = G r (F rz ((d (i) ) T )), where the operator F rz ( ) is to remove the zeros in the vector and the operator G r ( ) is to count the number of rows in a matrix. Let d (i) = F rz ((d (i) ( ) T ) and R (j,i) = F ) ur R (j,i), where the operator F ur ( ) is to output the unique rows in a matrix. Then, N (j,i) can be computed as N (j,i) χ! (M 1)! = ( ) ω G s d (i)t,d ω (i) ( ),! ζ G s R (j,i), r (j,i) ζ! (3) where d (i) ω are the entries of d (i), r (j,i) ζ are the row vectors of R (j,i), and the operator G s (A, v) is to count the number of row vectors of matrix A that is equa to the vector v. Now, we consider the coision of spreading codes. From d, we construct a code coision indication matrix Z with entries being z i,j,i=1,...,n z,j =1,...,K, whose N z row vectors represent the possibe code coision scenarios under the RB coision condition indicated by d. For the exampe described in Fig. 6, the code coision indication matrix is given by Z with the row vectors [ 1 1], [ 1 ], [ 1], and []representing the four possibe code coision scenarios due to the RB coisions indicated by d =[, 3, 2]. Let ξ (i) be the number of ones in the th coumn of R (i) and η = K =1 z(i) q be the number of code coisions. Note that R (j,i) is simpified to R (i) because R (j,i) j have the same code coision indication matrix. The number of code combinations Ξ (i) q,,η according to the RB coision condition indicated by the th coumn of R (i) and the code coision condition indicated by the qth row of Z (i) can be derived as L M, if d (i) =, z (i) q =, (L 1) ξ(i) L M ξ(i), if d (i) >, z (i) q =, Ξ (i) q,,η = ξ (i) [( ξ (i) j ) (L 1) ξ(i) j j=1 ] L M ξ(i), if d (i) >, z (i) q =1. (4) Therefore, the probabiity of having η spreading codes coided can be computed as η = i Q N (i) CR q Q K =1 Ξ(i) q,,η [( B K ) L K ] M, (5) { where Q = (i, q) G r (F rz ((d (i) ) T )) η, } K =1 z(i) q = η. For the continuous and pseudo-random seection schemes, it is difficut not ony to write down a cosed form but aso to perform computer-based simuations due to the arge search space, in particuar for the pseudo-random seection scheme. Therefore, we turn to investigate the access probabiity. Fig. 7 shows the simuation resuts of access probabiity for different RB indices. The simuation parameters are: the number of UEs M is 6, the tota number of avaiabe RBs B is 15, the number of RBs occupied per UE K is 6, the spreading factor L is 3, the number of avaiabe cycic shifts for the spreading code seection N s = α max +1 is 12, and the mapping matrices V are {3 5, 4 4, 5 3}. In Fig. 7, we can find that both the access probabiities of the random and pseudo-random

5 Access probabiity RB index Size of search space Number of RBs occupied per UE (a) Fig. 7. Access probabiity of different resource seection schemes robabiity (ana.), (approx.) (ana.) (sim.) (sim.) (sim.) Fig. 9. Size of search space Tota number of avaiabe RBs (b) Comparison of compexity for different resource seection schemes Number of coisions Fig. 8. Anaytica and simuated coision probabiities of different resource seection schemes. Note that the anaytica resut of the random seection scheme is used to approximate the proposed pseudo-random seection scheme. seection schemes are uniformy distributed, but the access probabiity of the continuous seection scheme varies with the RB index. Therefore, it can be expected that the anaytica resut of the random seection scheme can approximate the resut of the pseudo-random seection scheme. Fig. 8 compares the anaytica and simuation resuts of coision probabiity for different resource seection schemes. It indeed shows that the coision probabiity of the pseudo-random seection scheme can be approximated by the random seection scheme. It aso shows that the continuous seection scheme, which has the owest compexity, has a high probabiity of zero coision but aso has a high probabiity of six coisions (note that the number of RBs occupied per UE is six). This is why the continuous seection scheme performs worse, as shown ater. B. Compexity Anaysis To understand how much detection compexity can be saved using the proposed pseudo-random resource seection scheme, we compare the compexity in this subsection. The detection compexity is measured by the size of the search space, which is the number of the combinations of RBs and spreading codes. The detection compexity of the random seection scheme is given by considering randomy choosing K ordered RBs from B avaiabe RBs and randomy picking a spreading code for B! each chosen RB, i.e., K! (B K)! K! LK = B! LK (B K)!. For the continuous seection scheme, the spreading code is determined according to (1) and the compexity is (B K +1) N s, where (B K +1) is the number of possibe continuous segments. For the pseudo-random seection scheme, we take the number of avaiabe mapping matrices V (the cardinaity of V) and the row cycic shift operation into account. The detection compexity of the pseudo-random seection scheme is (B K+1) N s β, where β is the tota number of coumns of avaiabe mapping matrices, i.e., β = V =1 N co(); N co is the number of coumns of the mapping matrix chosen by UE. Fig. 9(a) compares the compexity of the three schemes using the same parameters as we evauate the coision, except that the number of coumns of matrix N co is 5 and we ony consider one mapping matrix with size 3 5. When the number of RBs used by each UE is arge, the detection compexity of the random seection scheme is prohibitivey high whie the compexity of the proposed pseudo-random seection scheme stays at a moderate eve. The same situation appies when different number of RBs are used, as shown in Fig. 9(b). V. ERFORMANCE EVALUATION In this section, we present the performance evauation of the proposed system. We first consider the effect due to CDM and then investigate the performance of the proposed pseudorandom resource seection scheme under the EVehA mutipath channe. The performance is evauated by the bock error

6 BLER Throughput (%) UE w/o CDM (CR =.119), C 2 UEs w/ CDM (CR =.238), C 3 UEs w/ CDM (CR =.357), C 1 UE w/o CDM (CR =.119), EC 2 UEs w/ CDM (CR =.238), EC 3 UEs w/ CDM (CR =.357), EC 1 UE w/o CDM (CR =.238), C 1 UE w/o CDM (CR =.357), C (a) Bock error rate 1 UE w/o CDM (CR =.119), C 2 UEs w/ CDM (CR =.238), C 3 UEs w/ CDM (CR =.357), C 1 UE w/o CDM (CR =.119), EC 2 UEs w/ CDM (CR =.238), EC 3 UEs w/ CDM (CR =.357), EC 1 UE w/o CDM (CR =.238), C 1 UE w/o CDM (CR =.357), C (b) Throughput Fig. 1. erformance of using CDM under the EVehA mutipath channe. CR, C, and EC respectivey represent code rate, perfect channe estimation, and estimated channe. rate (BLER) and the throughput (in percentage). The 1% throughput is defined as the throughput which can be achieved by a singe UE since our goa is to increase the throughput aong with the increase in the number of UEs. As a resut, it is possibe that the resuting throughput is more than 1%. A. Effect of CDM First, we compare the performances of the systems with and without using CDM. The RB usage and spreading codes are assumed known by the receiver. The EVehA channe with speed 3 km/hr is chosen for demonstration. The simuation parameters are the foowing. Bandwidth is set to 1.4 MHz, moduation is QSK, the number of RBs per UE is 6, data is 152 bits ong, the size of DFT is 72, the size of IDFT is 128, and the spreading factors are 2 (for 2 UEs) and 3 (for 3 UEs). Three cases are considered: singe UE without CDM, 2 UEs with CDM, and 3 UEs with CDM. The bock error rates are compared in Fig. 1(a). When the same channe code rate is appied, the system with CDM outperforms the system without CDM due to the spreading BLER BLER with IC with IC with IC (a) 7 UEs with IC with IC with IC (b) 2 UEs Fig. 11. erformance comparison of different resource seection schemes with and without IC under the EVehA mutipath channe. gain. It can be found that the performance degradation due to the higher channe code rate is compensated by the spreading gain. However, the case of 3 UEs with the channe code rate.357 suffers sight performance degradation due to the mutipath effect. To understand how channe estimation affects the system performance, we aso compare the BLER performances of the case when the channe is perfecty known and of the case when the frequency domain east square is used for channe estimation and the minimum mean square error (MMSE) criterion is used for equaization. In Fig. 1(a), we observe that the imperfect channe estimation degrades the system BLER and resuts in error foors. However, the BLER is sti in the usabe range. The throughput performances are compared in Fig. 1(b). It is shown that, even under the non-perfect channe estimation, more than 1% throughput performance can be achieved (note again that the 1% throughput is defined as the throughput which can be achieved by a singe UE) and the throughput scaes amost ineary with the number of UEs.

7 BLER Throughput (%) UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () (a) Bock error rate 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 7 UEs () 1 UEs () 2 UEs () 1 UEs 2 UEs 7 UEs (b) Throughput Fig. 12. erformance comparison of different resource seection schemes under the EVehA mutipath channe. erfect channe estimation is assumed. B. erformance of seudo-random Resource Seection Now we present the performance of the proposed pseudorandom resource seection scheme. The EVehA channe with user speed 3 km/hr is again chosen for demonstration. Bandwidth is set to 1 MHz, moduation is QSK, the number of RBs per UE is 6, data is 152 bits ong, the spreading factor is 3, and the sizes of mapping matrices are 5 1, 6 9, and 1 5. Fig. 11 shows the performance with and without IC for different number of UEs. When no IC is appied, a schemes perform worse due to interference; when IC is appied, both the random and the proposed pseudorandom seection schemes outperform the continuous seection scheme. Fig. 12(a) and Fig. 12(b) compare the BLER and the throughput performance respectivey. erfect channe estimation is assumed. It is shown that the random seection scheme provides the best performance. This is expected since it has the argest compexity. In the cases of 7 UEs and 1 UEs, the continuous seection scheme provides acceptabe BLER performance. However, for the case of 2 UEs which corresponds to the 8% LTE system oading, using the continuous seection scheme cannot achieve the.1 BLER requirement specified by LTE. On the other hand, the pseudo-random seection scheme not ony outperforms the continuous seection scheme in a cases, but aso provides a performance very cose to the random seection scheme in terms of throughput. Remember that the compexity of the proposed pseudo-random seection scheme is much ower than the random seection scheme. This demonstrates the superiority of the proposed pseudo-random resource seection scheme. VI. CONCLUSIONS In this paper we have proposed a direct access system with pseudo-random resource seection for sma data transmission in ceuar networks. Code division mutipexing has been introduced on top of the conventiona SC-FDMA system to aow the same resources to be shared among different UEs. UEs seect RBs and spreading codes for data transmission without signaing enb in order to reduce overhead. With the proposed pseudo-random resource seection scheme, the receiver compexity can be reduced significanty compared to the random seection of resource bocks and spreading codes. The anaytica and simuation resuts have shown that the performance of the proposed pseudo-random resource seection scheme is cose to that of the random seection scheme with much ower compexity, and demonstrated the feasibiity of the proposed CDM-based system for direct access in ceuar networks. ACKNOWLEDGEMENT The support from Industria Technoogy Research Institute (ITRI) and Nationa Science Counci (NSC), Taiwan (under grant MOST E-1-2) is gratefuy acknowedged. REFERENCES [1] 3G TS V1.., Evoved universa terrestria radio access (E-UTRA) medium access contro (MAC) protoco specification, Dec. 21. [2] 3G TR V11.., Study on RAN improvements for machinetype communications, Sept [3] K. Zhou, N. Nikaein, R. Knopp, and C. Bonnet, Contention based access for machine-type communications over LTE, in roc. IEEE Vehicuar Technoogy Conference (VTC Spring), May 212, pp [4] R. aiva, H. Wihem, M. Saiy, D. Navrati, and M. Taponen, Overoad contro method for synchronized MTC traffic in GERAN, in roc. IEEE Vehicuar Technoogy Conference (VTC Fa), Sept. 211, pp [5] J. Bas, F. Vazquez-Gaego, C. Gavrincea, and J. Aonso-Zarate, Energy and deay anaysis of binary BCH codes for machine-to-machine networks with sma data transmissions, in roc. IEEE IMRC, Sept. 213, pp [6] 3G TR V1.5., Mutipexing and channe coding, Mar [7] 3G TS V1.1., Evoved universa terrestria radio access (E-UTRA) physica channes and moduation, Mar [8] E. Dahman, S. arkva, and J. Skod, 4G: LTE/LTE-Advanced for Mobie Broadband. Esevier, May 211. [9] S. Sesia, I. Toufik, and M. Baker, LTE - The UMTS Long Term Evoution: From Theory to ractice, 2nd ed. Wiey, Juy 211. [1] J. Bumenstein, J. C. Ikuno, J. rokopec, and M. Rupp, Simuating the ong term evoution upink physica ayer, in roc. Internationa Symposium ELMAR, Zadar, Croatia, 211.

Secure Network Coding with a Cost Criterion

Secure Network Coding with a Cost Criterion Secure Network Coding with a Cost Criterion Jianong Tan, Murie Médard Laboratory for Information and Decision Systems Massachusetts Institute of Technoogy Cambridge, MA 0239, USA E-mai: {jianong, medard}@mit.edu

More information

Lecture 7 Datalink Ethernet, Home. Datalink Layer Architectures

Lecture 7 Datalink Ethernet, Home. Datalink Layer Architectures Lecture 7 Dataink Ethernet, Home Peter Steenkiste Schoo of Computer Science Department of Eectrica and Computer Engineering Carnegie Meon University 15-441 Networking, Spring 2004 http://www.cs.cmu.edu/~prs/15-441

More information

Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.

Fast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing. Fast Robust Hashing Manue Urueña, David Larrabeiti and Pabo Serrano Universidad Caros III de Madrid E-89 Leganés (Madrid), Spain Emai: {muruenya,darra,pabo}@it.uc3m.es Abstract As statefu fow-aware services

More information

LTE technology introduction

LTE technology introduction LTE technoogy introduction My business card ROHDE & SCHWARZ Taiwan Ltd. 14F,No.13,Sec. 2,Pei-Tou Road, Taipei, 112,Taiwan, R.O.C. Lance Yang ( 楊 聯 甫 ) Appication Engineer Appication & System Support Phone:

More information

LTE. measurement. Clark Lin Application and system support Rohde & Schwarz, Taiwan

LTE. measurement. Clark Lin Application and system support Rohde & Schwarz, Taiwan LTE Market and LTE RF measurement Cark Lin Appication and system support Rohde & Schwarz, Taiwan Technoogy evoution path 2005/2006 2007/2008 2009/2010 2011/2012 2013/2014 GSM/ GPRS EDGE, 200 khz DL: 473

More information

An FDD Wideband CDMA MAC Protocol for Wireless Multimedia Networks

An FDD Wideband CDMA MAC Protocol for Wireless Multimedia Networks An FDD ideband CDMA MAC Protoco for ireess Mutimedia Networks Xudong ang Broadband and ireess Networking (BN) Lab Schoo of Eectrica and Computer Engineering Georgia Institute of Technoogy Atanta, GA 3332

More information

A Distributed MAC Scheme Supporting Voice Services in Mobile Ad Hoc Networks 1

A Distributed MAC Scheme Supporting Voice Services in Mobile Ad Hoc Networks 1 A Distributed MAC Scheme Supporting Voice Services in Mobie Ad Hoc Networks 1 Hai Jiang, Ping Wang, H. Vincent Poor, and Weihua Zhuang Department of Eectrica & Computer Engineering, University of Aberta,

More information

Simultaneous Routing and Power Allocation in CDMA Wireless Data Networks

Simultaneous Routing and Power Allocation in CDMA Wireless Data Networks Simutaneous Routing and Power Aocation in CDMA Wireess Data Networks Mikae Johansson *,LinXiao and Stephen Boyd * Department of Signas, Sensors and Systems Roya Institute of Technoogy, SE 00 Stockhom,

More information

3.3 SOFTWARE RISK MANAGEMENT (SRM)

3.3 SOFTWARE RISK MANAGEMENT (SRM) 93 3.3 SOFTWARE RISK MANAGEMENT (SRM) Fig. 3.2 SRM is a process buit in five steps. The steps are: Identify Anayse Pan Track Resove The process is continuous in nature and handed dynamicay throughout ifecyce

More information

A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation

A Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation A Simiarity Search Scheme over Encrypted Coud Images based on Secure Transormation Zhihua Xia, Yi Zhu, Xingming Sun, and Jin Wang Jiangsu Engineering Center o Network Monitoring, Nanjing University o Inormation

More information

REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP

REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP REAL TIME IMPLEMANTATION OF LMS BEAMFORMER FOR cdma2000 3G SYSTEM USING TI TMS320C6701 DSP Kerem Küçük, Mustafa Karakoç, and Adnan Kavak + Kocaei University + Kocaei University Eectronics and Computer

More information

Australian Bureau of Statistics Management of Business Providers

Australian Bureau of Statistics Management of Business Providers Purpose Austraian Bureau of Statistics Management of Business Providers 1 The principa objective of the Austraian Bureau of Statistics (ABS) in respect of business providers is to impose the owest oad

More information

Virtual trunk simulation

Virtual trunk simulation Virtua trunk simuation Samui Aato * Laboratory of Teecommunications Technoogy Hesinki University of Technoogy Sivia Giordano Laboratoire de Reseaux de Communication Ecoe Poytechnique Federae de Lausanne

More information

HYBRID FUZZY LOGIC PID CONTROLLER. Abstract

HYBRID FUZZY LOGIC PID CONTROLLER. Abstract HYBRID FUZZY LOGIC PID CONTROLLER Thomas Brehm and Kudip S. Rattan Department of Eectrica Engineering Wright State University Dayton, OH 45435 Abstract This paper investigates two fuzzy ogic PID controers

More information

Advanced ColdFusion 4.0 Application Development - 3 - Server Clustering Using Bright Tiger

Advanced ColdFusion 4.0 Application Development - 3 - Server Clustering Using Bright Tiger Advanced CodFusion 4.0 Appication Deveopment - CH 3 - Server Custering Using Bri.. Page 1 of 7 [Figures are not incuded in this sampe chapter] Advanced CodFusion 4.0 Appication Deveopment - 3 - Server

More information

Face Hallucination and Recognition

Face Hallucination and Recognition Face Haucination and Recognition Xiaogang Wang and Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {xgwang1, xtang}@ie.cuhk.edu.hk http://mmab.ie.cuhk.edu.hk Abstract.

More information

Multi-Robot Task Scheduling

Multi-Robot Task Scheduling Proc of IEEE Internationa Conference on Robotics and Automation, Karsruhe, Germany, 013 Muti-Robot Tas Scheduing Yu Zhang and Lynne E Parer Abstract The scheduing probem has been studied extensivey in

More information

Normalization of Database Tables. Functional Dependency. Examples of Functional Dependencies: So Now what is Normalization? Transitive Dependencies

Normalization of Database Tables. Functional Dependency. Examples of Functional Dependencies: So Now what is Normalization? Transitive Dependencies ISM 602 Dr. Hamid Nemati Objectives The idea Dependencies Attributes and Design Understand concepts normaization (Higher-Leve Norma Forms) Learn how to normaize tabes Understand normaization and database

More information

Teamwork. Abstract. 2.1 Overview

Teamwork. Abstract. 2.1 Overview 2 Teamwork Abstract This chapter presents one of the basic eements of software projects teamwork. It addresses how to buid teams in a way that promotes team members accountabiity and responsibiity, and

More information

Iterative Water-filling for Load-balancing in Wireless LAN or Microcellular Networks

Iterative Water-filling for Load-balancing in Wireless LAN or Microcellular Networks terative Water-fiing for Load-baancing in Wireess LAN or Microceuar Networks Jeremy K. Chen Theodore S. Rappaport Gustavo de Veciana Wireess Networking and Communications Group (WNCG), be University of

More information

Pay-on-delivery investing

Pay-on-delivery investing Pay-on-deivery investing EVOLVE INVESTment range 1 EVOLVE INVESTMENT RANGE EVOLVE INVESTMENT RANGE 2 Picture a word where you ony pay a company once they have deivered Imagine striking oi first, before

More information

Traffic classification-based spam filter

Traffic classification-based spam filter Traffic cassification-based spam fiter Ni Zhang 1,2, Yu Jiang 3, Binxing Fang 1, Xueqi Cheng 1, Li Guo 1 1 Software Division, Institute of Computing Technoogy, Chinese Academy of Sciences, 100080, Beijing,

More information

Art of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0

Art of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0 IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922 2005 Pubished by the IEEE Computer Society Vo. 6, No. 5; May 2005 Editor: Marcin Paprzycki, http://www.cs.okstate.edu/%7emarcin/ Book Reviews: Java Toos and Frameworks

More information

On Capacity Scaling in Arbitrary Wireless Networks

On Capacity Scaling in Arbitrary Wireless Networks On Capacity Scaing in Arbitrary Wireess Networks Urs Niesen, Piyush Gupta, and Devavrat Shah 1 Abstract arxiv:07112745v3 [csit] 3 Aug 2009 In recent work, Özgür, Lévêque, and Tse 2007) obtained a compete

More information

(12) United States Patent Rune

(12) United States Patent Rune (12) United States Patent Rune US006304913B1 (10) Patent N0.: (45) Date of Patent: US 6,304,913 B1 on. 16, 2001 (54) INTERNET SYSTEM AND METHOD FOR SELECTING A CLOSEST SERVER FROM A PLURALITY OF ALTERNATIVE

More information

With the arrival of Java 2 Micro Edition (J2ME) and its industry

With the arrival of Java 2 Micro Edition (J2ME) and its industry Knowedge-based Autonomous Agents for Pervasive Computing Using AgentLight Fernando L. Koch and John-Jues C. Meyer Utrecht University Project AgentLight is a mutiagent system-buiding framework targeting

More information

Application-Aware Data Collection in Wireless Sensor Networks

Application-Aware Data Collection in Wireless Sensor Networks Appication-Aware Data Coection in Wireess Sensor Networks Xiaoin Fang *, Hong Gao *, Jianzhong Li *, and Yingshu Li +* * Schoo of Computer Science and Technoogy, Harbin Institute of Technoogy, Harbin,

More information

(12) Patent Application Publication (10) Pub. N0.: US 2006/0105797 A1 Marsan et al. (43) Pub. Date: May 18, 2006

(12) Patent Application Publication (10) Pub. N0.: US 2006/0105797 A1 Marsan et al. (43) Pub. Date: May 18, 2006 (19) United States US 20060105797A (12) Patent Appication Pubication (10) Pub. N0.: US 2006/0105797 A1 Marsan et a. (43) Pub. Date: (54) METHOD AND APPARATUS FOR (52) US. C...... 455/522 ADJUSTING A MOBILE

More information

Load Balancing in Distributed Web Server Systems with Partial Document Replication *

Load Balancing in Distributed Web Server Systems with Partial Document Replication * Load Baancing in Distributed Web Server Systems with Partia Document Repication * Ling Zhuo Cho-Li Wang Francis C. M. Lau Department of Computer Science and Information Systems The University of Hong Kong

More information

GREEN: An Active Queue Management Algorithm for a Self Managed Internet

GREEN: An Active Queue Management Algorithm for a Self Managed Internet : An Active Queue Management Agorithm for a Sef Managed Internet Bartek Wydrowski and Moshe Zukerman ARC Specia Research Centre for Utra-Broadband Information Networks, EEE Department, The University of

More information

WHITE PAPER BEsT PRAcTIcEs: PusHIng ExcEl BEyond ITs limits WITH InfoRmATIon optimization

WHITE PAPER BEsT PRAcTIcEs: PusHIng ExcEl BEyond ITs limits WITH InfoRmATIon optimization Best Practices: Pushing Exce Beyond Its Limits with Information Optimization WHITE Best Practices: Pushing Exce Beyond Its Limits with Information Optimization Executive Overview Microsoft Exce is the

More information

SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci

SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH Ufuk Cebeci Department of Industria Engineering, Istanbu Technica University, Macka, Istanbu, Turkey - ufuk_cebeci@yahoo.com Abstract An Enterprise

More information

CONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS

CONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS Dehi Business Review X Vo. 4, No. 2, Juy - December 2003 CONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS John N.. Var arvatsouakis atsouakis DURING the present time,

More information

COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION

COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION Františe Mojžíš Department of Computing and Contro Engineering, ICT Prague, Technicá, 8 Prague frantise.mojzis@vscht.cz Abstract This

More information

Vendor Performance Measurement Using Fuzzy Logic Controller

Vendor Performance Measurement Using Fuzzy Logic Controller The Journa of Mathematics and Computer Science Avaiabe onine at http://www.tjmcs.com The Journa of Mathematics and Computer Science Vo.2 No.2 (2011) 311-318 Performance Measurement Using Fuzzy Logic Controer

More information

Artificial neural networks and deep learning

Artificial neural networks and deep learning February 20, 2015 1 Introduction Artificia Neura Networks (ANNs) are a set of statistica modeing toos originay inspired by studies of bioogica neura networks in animas, for exampe the brain and the centra

More information

A quantum model for the stock market

A quantum model for the stock market A quantum mode for the stock market Authors: Chao Zhang a,, Lu Huang b Affiiations: a Schoo of Physics and Engineering, Sun Yat-sen University, Guangzhou 5175, China b Schoo of Economics and Business Administration,

More information

ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT

ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT Initiatives Strategic Mapping Success in The Food System: Discover. Anayze. Strategize. Impement. Measure. ELEVATING YOUR GAME FROM TRADE SPEND TO TRADE INVESTMENT Foodservice manufacturers aocate, in

More information

Chapter 2 Traditional Software Development

Chapter 2 Traditional Software Development Chapter 2 Traditiona Software Deveopment 2.1 History of Project Management Large projects from the past must aready have had some sort of project management, such the Pyramid of Giza or Pyramid of Cheops,

More information

CLOUD service providers manage an enterprise-class

CLOUD service providers manage an enterprise-class IEEE TRANSACTIONS ON XXXXXX, VOL X, NO X, XXXX 201X 1 Oruta: Privacy-Preserving Pubic Auditing for Shared Data in the Coud Boyang Wang, Baochun Li, Member, IEEE, and Hui Li, Member, IEEE Abstract With

More information

eg Enterprise vs. a Big 4 Monitoring Soution: Comparing Tota Cost of Ownership Restricted Rights Legend The information contained in this document is confidentia and subject to change without notice. No

More information

Maintenance activities planning and grouping for complex structure systems

Maintenance activities planning and grouping for complex structure systems Maintenance activities panning and grouping for compex structure systems Hai Canh u, Phuc Do an, Anne Barros, Christophe Berenguer To cite this version: Hai Canh u, Phuc Do an, Anne Barros, Christophe

More information

Fixed income managers: evolution or revolution

Fixed income managers: evolution or revolution Fixed income managers: evoution or revoution Traditiona approaches to managing fixed interest funds rey on benchmarks that may not represent optima risk and return outcomes. New techniques based on separate

More information

Load Balance vs Energy Efficiency in Traffic Engineering: A Game Theoretical Perspective

Load Balance vs Energy Efficiency in Traffic Engineering: A Game Theoretical Perspective Load Baance vs Energy Efficiency in Traffic Engineering: A Game Theoretica Perspective Yangming Zhao, Sheng Wang, Shizhong Xu and Xiong Wang Schoo of Communication and Information Engineering University

More information

A New Statistical Approach to Network Anomaly Detection

A New Statistical Approach to Network Anomaly Detection A New Statistica Approach to Network Anomay Detection Christian Caegari, Sandrine Vaton 2, and Michee Pagano Dept of Information Engineering, University of Pisa, ITALY E-mai: {christiancaegari,mpagano}@ietunipiit

More information

Scheduling in Multi-Channel Wireless Networks

Scheduling in Multi-Channel Wireless Networks Scheduing in Muti-Channe Wireess Networks Vartika Bhandari and Nitin H. Vaidya University of Iinois at Urbana-Champaign, USA vartikab@acm.org, nhv@iinois.edu Abstract. The avaiabiity of mutipe orthogona

More information

A Supplier Evaluation System for Automotive Industry According To Iso/Ts 16949 Requirements

A Supplier Evaluation System for Automotive Industry According To Iso/Ts 16949 Requirements A Suppier Evauation System for Automotive Industry According To Iso/Ts 16949 Requirements DILEK PINAR ÖZTOP 1, ASLI AKSOY 2,*, NURSEL ÖZTÜRK 2 1 HONDA TR Purchasing Department, 41480, Çayırova - Gebze,

More information

Betting Strategies, Market Selection, and the Wisdom of Crowds

Betting Strategies, Market Selection, and the Wisdom of Crowds Betting Strategies, Market Seection, and the Wisdom of Crowds Wiemien Kets Northwestern University w-kets@keogg.northwestern.edu David M. Pennock Microsoft Research New York City dpennock@microsoft.com

More information

Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction

Spatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction Spatio-Tempora Asynchronous Co-Occurrence Pattern for Big Cimate Data towards Long-Lead Food Prediction Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Cohen, David Sma and Shafiqu Isam Department of Computer

More information

TERM INSURANCE CALCULATION ILLUSTRATED. This is the U.S. Social Security Life Table, based on year 2007.

TERM INSURANCE CALCULATION ILLUSTRATED. This is the U.S. Social Security Life Table, based on year 2007. This is the U.S. Socia Security Life Tabe, based on year 2007. This is avaiabe at http://www.ssa.gov/oact/stats/tabe4c6.htm. The ife eperiences of maes and femaes are different, and we usuay do separate

More information

WIRELESS Mesh Networks (WMNs) have recently attracted

WIRELESS Mesh Networks (WMNs) have recently attracted 3968 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 2, NO. 8, AUGUST 23 A New MPLS-Based Forwarding Paradigm for Muti-Radio Wireess Mesh Networks Stefano Avaone and Giovanni Di Stasi Abstract Routing

More information

Safety Manual VEGAPULS series 60

Safety Manual VEGAPULS series 60 Safety Manua VEGAPULS series 60 4 20 ma/hart Radar Contents Contents 1 Functiona safety 1.1 In genera............................ 3 1.2 Panning............................. 5 1.3 Instrument parameter adjustment...........

More information

Priority-Based Congestion Control Algorithm for Cross-Traffic Assistance on LTE Networks

Priority-Based Congestion Control Algorithm for Cross-Traffic Assistance on LTE Networks Priority-Based Congestion Control Algorithm for Cross-Traffic Assistance on LTE Networks Lung-Chih Tung, You Lu, Mario Gerla Department of Computer Science University of California, Los Angeles Los Angeles,

More information

Leakage detection in water pipe networks using a Bayesian probabilistic framework

Leakage detection in water pipe networks using a Bayesian probabilistic framework Probabiistic Engineering Mechanics 18 (2003) 315 327 www.esevier.com/ocate/probengmech Leakage detection in water pipe networks using a Bayesian probabiistic framework Z. Pouakis, D. Vaougeorgis, C. Papadimitriou*

More information

Optimizing QoS-Aware Semantic Web Service Composition

Optimizing QoS-Aware Semantic Web Service Composition Optimizing QoS-Aware Semantic Web Service Composition Freddy Lécué The University of Manchester Booth Street East, Manchester, UK {(firstname.astname)@manchester.ac.uk} Abstract. Ranking and optimization

More information

Mapping of Secondary Virtual Networks onto Wireless Substrate based on. Cognitive Radio: multi-objective formulation and analysis

Mapping of Secondary Virtual Networks onto Wireless Substrate based on. Cognitive Radio: multi-objective formulation and analysis Mapping of Secondary Virtua Networks onto Wireess Substrate based on Cognitive Radio: muti-obective formuation and anaysis Andson Marreiros Baieiro 1 and Kevin Lopes Dias Center of Informatics (CIn) Federa

More information

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 12, DECEMBER 2013 1 Scaabe Muti-Cass Traffic Management in Data Center Backbone Networks Amitabha Ghosh, Sangtae Ha, Edward Crabbe, and Jennifer

More information

Pricing Internet Services With Multiple Providers

Pricing Internet Services With Multiple Providers Pricing Internet Services With Mutipe Providers Linhai He and Jean Warand Dept. of Eectrica Engineering and Computer Science University of Caifornia at Berkeey Berkeey, CA 94709 inhai, wr@eecs.berkeey.edu

More information

Early access to FAS payments for members in poor health

Early access to FAS payments for members in poor health Financia Assistance Scheme Eary access to FAS payments for members in poor heath Pension Protection Fund Protecting Peope s Futures The Financia Assistance Scheme is administered by the Pension Protection

More information

Comparison of Traditional and Open-Access Appointment Scheduling for Exponentially Distributed Service Time

Comparison of Traditional and Open-Access Appointment Scheduling for Exponentially Distributed Service Time Journa of Heathcare Engineering Vo. 6 No. 3 Page 34 376 34 Comparison of Traditiona and Open-Access Appointment Scheduing for Exponentiay Distributed Service Chongjun Yan, PhD; Jiafu Tang *, PhD; Bowen

More information

ONE of the most challenging problems addressed by the

ONE of the most challenging problems addressed by the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 9, SEPTEMBER 2006 2587 A Mutieve Context-Based System for Cassification of Very High Spatia Resoution Images Lorenzo Bruzzone, Senior Member,

More information

The IBM System/38. 8.1 Introduction

The IBM System/38. 8.1 Introduction 8 The IBM System/38 8.1 Introduction IBM s capabiity-based System38 [Berstis 80a, Houdek 81, IBM Sa, IBM 82b], announced in 1978 and deivered in 1980, is an outgrowth of work that began in the ate sixties

More information

Network/Communicational Vulnerability

Network/Communicational Vulnerability Automated teer machines (ATMs) are a part of most of our ives. The major appea of these machines is convenience The ATM environment is changing and that change has serious ramifications for the security

More information

Enhanced continuous, real-time detection, alarming and analysis of partial discharge events

Enhanced continuous, real-time detection, alarming and analysis of partial discharge events DMS PDMG-RH DMS PDMG-RH Partia discharge monitor for GIS Partia discharge monitor for GIS Enhanced continuous, rea-time detection, aarming and anaysis of partia discharge events Unrivaed PDM feature set

More information

Introduction to XSL. Max Froumentin - W3C

Introduction to XSL. Max Froumentin - W3C Introduction to XSL Max Froumentin - W3C Introduction to XSL XML Documents Stying XML Documents XSL Exampe I: Hamet Exampe II: Mixed Writing Modes Exampe III: database Other Exampes How do they do that?

More information

Ricoh Healthcare. Process Optimized. Healthcare Simplified.

Ricoh Healthcare. Process Optimized. Healthcare Simplified. Ricoh Heathcare Process Optimized. Heathcare Simpified. Rather than a destination that concudes with the eimination of a paper, the Paperess Maturity Roadmap is a continuous journey to strategicay remove

More information

Inductance. Bởi: OpenStaxCollege

Inductance. Bởi: OpenStaxCollege Inductance Bởi: OpenStaxCoege Inductors Induction is the process in which an emf is induced by changing magnetic fux. Many exampes have been discussed so far, some more effective than others. Transformers,

More information

SNMP Reference Guide for Avaya Communication Manager

SNMP Reference Guide for Avaya Communication Manager SNMP Reference Guide for Avaya Communication Manager 03-602013 Issue 1.0 Feburary 2007 2006 Avaya Inc. A Rights Reserved. Notice Whie reasonabe efforts were made to ensure that the information in this

More information

Precise assessment of partial discharge in underground MV/HV power cables and terminations

Precise assessment of partial discharge in underground MV/HV power cables and terminations QCM-C-PD-Survey Service Partia discharge monitoring for underground power cabes Precise assessment of partia discharge in underground MV/HV power cabes and terminations Highy accurate periodic PD survey

More information

Pricing and Revenue Sharing Strategies for Internet Service Providers

Pricing and Revenue Sharing Strategies for Internet Service Providers Pricing and Revenue Sharing Strategies for Internet Service Providers Linhai He and Jean Warand Department of Eectrica Engineering and Computer Sciences University of Caifornia at Berkeey {inhai,wr}@eecs.berkeey.edu

More information

Hybrid Interface Solutions for next Generation Wireless Access Infrastructure

Hybrid Interface Solutions for next Generation Wireless Access Infrastructure tec. Connectivity & Networks Voker Sorhage Hybrid Interface Soutions for next Generation Wireess Access Infrastructure Broadband wireess communication wi revoutionize every aspect of peope s ives by enabing

More information

Storing Shared Data on the Cloud via Security-Mediator

Storing Shared Data on the Cloud via Security-Mediator Storing Shared Data on the Coud via Security-Mediator Boyang Wang, Sherman S. M. Chow, Ming Li, and Hui Li State Key Laboratory of Integrated Service Networks, Xidian University, Xi an, China Department

More information

TCP/IP Gateways and Firewalls

TCP/IP Gateways and Firewalls Gateways and Firewas 1 Gateways and Firewas Prof. Jean-Yves Le Boudec Prof. Andrzej Duda ICA, EPFL CH-1015 Ecubens http://cawww.epf.ch Gateways and Firewas Firewas 2 o architecture separates hosts and

More information

Sorting, Merge Sort and the Divide-and-Conquer Technique

Sorting, Merge Sort and the Divide-and-Conquer Technique Inf2B gorithms and Data Structures Note 7 Sorting, Merge Sort and the Divide-and-Conquer Technique This and a subsequent next ecture wi mainy be concerned with sorting agorithms. Sorting is an extremey

More information

Dynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud

Dynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud Dynamic Pricing Trade Market or Shared Resources in IIU Federated Coud Tongrang Fan 1, Jian Liu 1, Feng Gao 1 1Schoo o Inormation Science and Technoogy, Shiiazhuang Tiedao University, Shiiazhuang, 543,

More information

An Integrated Data Management Framework of Wireless Sensor Network

An Integrated Data Management Framework of Wireless Sensor Network An Integrated Data Management Framework of Wireess Sensor Network for Agricutura Appications 1,2 Zhao Liang, 2 He Liyuan, 1 Zheng Fang, 1 Jin Xing 1 Coege of Science, Huazhong Agricutura University, Wuhan

More information

Bite-Size Steps to ITIL Success

Bite-Size Steps to ITIL Success 7 Bite-Size Steps to ITIL Success Pus making a Business Case for ITIL! Do you want to impement ITIL but don t know where to start? 7 Bite-Size Steps to ITIL Success can hep you to decide whether ITIL can

More information

Spherical Correlation of Visual Representations for 3D Model Retrieval

Spherical Correlation of Visual Representations for 3D Model Retrieval Noname manuscript No. (wi be inserted by the editor) Spherica Correation of Visua Representations for 3D Mode Retrieva Ameesh Makadia Kostas Daniiidis the date of receipt and acceptance shoud be inserted

More information

Leadership & Management Certificate Programs

Leadership & Management Certificate Programs MANAGEMENT CONCEPTS Leadership & Management Certificate Programs Programs to deveop expertise in: Anaytics // Leadership // Professiona Skis // Supervision ENROLL TODAY! Contract oder Contract GS-02F-0010J

More information

Order-to-Cash Processes

Order-to-Cash Processes TMI170 ING info pat 2:Info pat.qxt 01/12/2008 09:25 Page 1 Section Two: Order-to-Cash Processes Gregory Cronie, Head Saes, Payments and Cash Management, ING O rder-to-cash and purchase-topay processes

More information

Fast b-matching via Sufficient Selection Belief Propagation

Fast b-matching via Sufficient Selection Belief Propagation Fast b-matching via Sufficient Seection Beief Propagation Bert Huang Computer Science Department Coumbia University New York, NY 127 bert@cs.coumbia.edu Tony Jebara Computer Science Department Coumbia

More information

The definition of insanity is doing the same thing over and over again and expecting different results

The definition of insanity is doing the same thing over and over again and expecting different results insurance services Sma Business Insurance a market opportunity being missed Einstein may not have known much about insurance, but if you appy his definition to the way existing brands are deveoping their

More information

Chapter 1 Structural Mechanics

Chapter 1 Structural Mechanics Chapter Structura echanics Introduction There are many different types of structures a around us. Each structure has a specific purpose or function. Some structures are simpe, whie others are compex; however

More information

Learning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2

Learning framework for NNs. Introduction to Neural Networks. Learning goal: Inputs/outputs. x 1 x 2. y 1 y 2 Introduction to Neura Networks Learning framework for NNs What are neura networks? Noninear function approimators How do they reate to pattern recognition/cassification? Noninear discriminant functions

More information

Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey

Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey Distribution of Income Sources of Recent Retirees: Findings From the New Beneficiary Survey by Linda Drazga Maxfied and Virginia P. Rena* Using data from the New Beneficiary Survey, this artice examines

More information

2.5. Type 90. MediaManagement system the modern handling of hazardous substances

2.5. Type 90. MediaManagement system the modern handling of hazardous substances 2.5 MMS System 192 2.5 Type 90 MediaManagement system the modern handing of hazardous substances 193 The MediaManagement System (MMS) is a diverse, mutifunctiona and high-quaity soution for modern aboratory

More information

Vibration Reduction of Audio Visual Device Mounted on Automobile due to Gap Vibration

Vibration Reduction of Audio Visual Device Mounted on Automobile due to Gap Vibration Vibration Reduction of Audio Visua Device Mounted on Automobie due to Gap Vibration Nobuyuki OKUBO, Shinji KANADA, Takeshi TOI CAMAL, Department of Precision Mechanics, Chuo University 1-13-27 Kasuga,

More information

Overview of Health and Safety in China

Overview of Health and Safety in China Overview of Heath and Safety in China Hongyuan Wei 1, Leping Dang 1, and Mark Hoye 2 1 Schoo of Chemica Engineering, Tianjin University, Tianjin 300072, P R China, E-mai: david.wei@tju.edu.cn 2 AstraZeneca

More information

Chapter 3: JavaScript in Action Page 1 of 10. How to practice reading and writing JavaScript on a Web page

Chapter 3: JavaScript in Action Page 1 of 10. How to practice reading and writing JavaScript on a Web page Chapter 3: JavaScript in Action Page 1 of 10 Chapter 3: JavaScript in Action In this chapter, you get your first opportunity to write JavaScript! This chapter introduces you to JavaScript propery. In addition,

More information

LADDER SAFETY Table of Contents

LADDER SAFETY Table of Contents Tabe of Contents SECTION 1. TRAINING PROGRAM INTRODUCTION..................3 Training Objectives...........................................3 Rationae for Training.........................................3

More information

ENERGY AND BOLTZMANN DISTRIBUTIONS

ENERGY AND BOLTZMANN DISTRIBUTIONS MISN--159 NRGY AND BOLTZMANN DISTRIBUTIONS NRGY AND BOLTZMANN DISTRIBUTIONS by J. S. Kovacs and O. McHarris Michigan State University 1. Introduction.............................................. 1 2.

More information

NCH Software FlexiServer

NCH Software FlexiServer NCH Software FexiServer This user guide has been created for use with FexiServer Version 1.xx NCH Software Technica Support If you have difficuties using FexiServer pease read the appicabe topic before

More information

Oligopoly in Insurance Markets

Oligopoly in Insurance Markets Oigopoy in Insurance Markets June 3, 2008 Abstract We consider an oigopoistic insurance market with individuas who differ in their degrees of accident probabiities. Insurers compete in coverage and premium.

More information

Design of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation

Design of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation Design of Foow-Up Experiments for Improving Mode Discrimination and Parameter Estimation Szu Hui Ng 1 Stephen E. Chick 2 Nationa University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Technoogy

More information

Journal of Economic Behavior & Organization

Journal of Economic Behavior & Organization Journa of Economic Behavior & Organization 85 (23 79 96 Contents ists avaiabe at SciVerse ScienceDirect Journa of Economic Behavior & Organization j ourna ho me pag e: www.esevier.com/ocate/j ebo Heath

More information

Chapter 3: e-business Integration Patterns

Chapter 3: e-business Integration Patterns Chapter 3: e-business Integration Patterns Page 1 of 9 Chapter 3: e-business Integration Patterns "Consistency is the ast refuge of the unimaginative." Oscar Wide In This Chapter What Are Integration Patterns?

More information

PREFACE. Comptroller General of the United States. Page i

PREFACE. Comptroller General of the United States. Page i - I PREFACE T he (+nera Accounting Office (GAO) has ong beieved that the federa government urgenty needs to improve the financia information on which it bases many important decisions. To run our compex

More information

GreenTE: Power-Aware Traffic Engineering

GreenTE: Power-Aware Traffic Engineering GreenTE: Power-Aware Traffic Engineering Mingui Zhang zmg6@mais.tsinghua.edu.cn Cheng Yi yic@emai.arizona.edu Bin Liu iub@tsinghua.edu.cn Beichuan Zhang bzhang@arizona.edu Abstract Current network infrastructures

More information

Introduction the pressure for efficiency the Estates opportunity

Introduction the pressure for efficiency the Estates opportunity Heathy Savings? A study of the proportion of NHS Trusts with an in-house Buidings Repair and Maintenance workforce, and a discussion of eary experiences of Suppies efficiency initiatives Management Summary

More information

FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy

FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS Kar Skretting and John Håkon Husøy University of Stavanger, Department of Eectrica and Computer Engineering N-4036 Stavanger,

More information