Advanced wireless networks 4g technologies phần 6 docx

JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 CDMA CELLULAR MULTIMEDIA WIRELESS NETWORKS 419 0.5 1 1.5 2 2.5 3 3.5 4 10 -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 Blocking probabilities Common divisor of the offered traffic MsCAC-B1 MsCAC-B2 MsCAC-B3 MdCAC-B1 MdCAC-B2 MdCAC-B3 Offered traffic of the three service classes in 4:3:3 MTIP Figure 12.11 MdCAC vs MsCAC. 1 1.5 2 2.5 3 3.5 4 10 -4 10 -3 10 -2 10 -1 10 0 Blocking probabilities Common divisor of the offered traffic MEM-B1 MEM-B2 MEM-B3 MLESS-B1 MLESS-B2 MLESS-B3 Offered traffic of the three service classes in 4:3:3 MTIP MEM: memory(auto-regressive) MsCAC MLESS: memory less MsCAC Figure 12.12 Memory vs memoryless MsCAC systems. that the offered traffic of class 1 is 7λ Erlangs, class 2 is 2λ Erlangs and class 3 is λ Erlangs. Thus, the total offered traffic is 10 Erlangs if λ = 1. Figure 12.11 presents the new-call blocking as well as the handoff failure probability of each service class in MdCAC and MsCAC systems vs the common divisor of the offered traffic intensities of the service classes in 4:3:3 MTIP. This figure confirms that there is no capacity gain using MsCAC for serving CBR services. Figure 12.12 shows the need for stable and reliable measurements in MsCAC systems. The performance characteristics of a memoryless measurement-based and a memory (auto-regressive) measurement-based system are presented also for 4:3:3 MTIP. Estimations with the help of auto-regressive filters may results in better performance, but more complex hardware/software is needed. Figures 12.13–12.15 present the handoff failure and new-call blocking probabilities of the JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 420 ADAPTIVE RESOURCE MANAGEMENT 0.5 1 1.5 2 2.5 3 3.5 4 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 Blocking probabilities Common divisor of the offered traffic B1&F1-MdCAC B2&F2-MdCAC B3&F3-MdCAC B1-SCAC B2-SCAC B3-SCAC F1-SCAC F2-SCAC F3-SCAC Offered traffic of the three service classes in 4:3:3 MTIP Figure 12.13 SCAC vs MdCAC in 4:3:3 MTIP. 0.5 1 1.5 2 2.5 3 3.5 4 10 -14 10 -12 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 Blocking probabilities Common divisor of the offered traffic B1&F1-MdCAC B2&F2-MdCAC B3&F3-MdCAC B1-SCAC B2-SCAC B3-SCAC F1-SCAC F2-SCAC F3-SCAC Offered traffic of the three service classes in 5:4:1 MTIP Figure 12.14 SCAC vs MdCAC in 5:4:1 MTIP. SCAC system in comparison with the MdCAC system for 4:3:3, 5:4:1 and 7:2:1 MTIPs respectively. Figure 12.16 demonstrates that the SCAC system with the incomplete Gamma decision function offers better average communication quality, but slightly worse equi- librium blocking and dropping characteristics compared with the Gaussian function. The freedom of choosing such decision functions to fulfill the performance requirements in- creases the flexibility of the SCAC policy. The 4:3:3 MTIP is used in Figure 12.16. The QoS loss probability vs the offered traffic in 7:2:1 MTIP are listed in Table 12.6 for different JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 0.5 1 1.5 2 2.5 3 3.5 4 10 -15 10 -10 10 -5 10 0 Blocking probab ilities Common divisor of the offered traffic B1&F1-MdCAC B2&F2-MdCAC B3&F3-MdCAC B1-SCAC B2-SCAC B3-SCAC F1-SCAC F2-SCAC F3-SCAC Offered traffic of the three service classes in 7:2:1 MTIP Figure 12.15 SCAC vs MdCAC in 7:2:1 MTIP. 0.5 1 1.5 2 2.5 3 3.5 4 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 Loss probabilities Common divisor of the offered traffic B1-GaussianSCAC B2-GaussianSCAC B3-GaussianSCAC Ploss-GaussianSCAC B1-IncGammaSCAC B2-IncGammaSCAC B3-IncGammaSCAC Ploss-IncGammaSCAC Offered traffic of the three service classes in 4:3:3 MTIP Figure 12.16 Flexibility of choosing decision function in the SCAC system. Table 12.6 QoS loss probability in comparison The three-class RT offered traffic, e.g. in 7:2:1 MTIP Common divisor Traffic 1 1.5 2 2.5 3 MdCAC 0 0 0 0.0001 0.0007 MsCAC 0 0.0002 0.0024 0.0114 0.0315 GaussianSCAC 0 0 0.0001 0.0020 0.0119 IncGammaSCAC 0 0 0.0001 0.0013 0.0072 SCAC with threshold QoSDiff 0 0 0 0.0001 0.0005 SCAC with fracturing QoSDiff 0 0 0 0.0003 0.0021 421 JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 422 ADAPTIVE RESOURCE MANAGEMENT 0.5 1 1.5 2 2.5 3 3.5 4 10 -12 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 Common divisor of the offered traffic Blocking probabilities B1CS B2CS B3CS B21QT B22QT B23QT B11QT B12QT B13QT F1QT F2QT F3QT BxCS: SCAC system without QoS differentiation BxxQT and FxQT: with threshold QoS differentiation Offered traffic of the three service classes in 4:3:3 MTIP Figure 12.17 SCAC system with threshold hard blocking QoS differentiation. CAC systems. Although the SCAC system suffers slight degradation of the communication quality, it yields significant improvements in the handoff failure probability and in the call blocking probability. The traffic shaping gain of the SCAC is clearly illustrated in Figure 12.15, where the traffic intensity of the voice calls is really high. For a predefined performance requirements, e.g. less than 0.1 and 0.5 % handoff failure probability for voice and other calls respectively, less than 1, 5 and 10 % new-call blocking probability for class 1, class 2 and class 3, respectively, and 10 % allowable equilibrium outage probability, the SCAC system overall offers much better Erlang capacity. Moreover, there is no need for redesign of the capacity thresholds in the SCAC system as long as the range of allowable uncertainty is maintained with the help of other control mechanisms such as TPC, link- adaptation, etc. Thus, the robustness is also well improved over the MdCAC system. SCAC has demonstrated an efficient RRU and capacity enhancement. With QoS differentiation, operators can customize the operation of serving networks. Figure 12.17 presents the performance characteristics of the following simple scenario. Users are divided into two user classes: business ( j = 1) and economy class ( j = 2). Re- quests of the business users for any RT services are served immediately as long as there are enough resources for accommodating them. On the other hand, requests of the econ- omy users are served only if less than 70 % of effective resources are occupied by RT traffic, i.e. system load state c less than 0.5. Assume that demands for services of user classes are equal. Thus, arrival rates of new call requests from user classes for each service class are equal. Invoke assumption (3b) with λ l,1k = λ l,2k for k = 1, 2, 3. The offered traffic of e.g. 4:3:3 MTIP above can be split for each user class resulting in 2:2:1.5:1.5:1.5:1.5 MTIP of six traffic classes. The factor a 0 jk (c) of admission probability in (12.56) can be determined by (12.49), where the blocking threshold of business class l 1k is C u and of economic class l 2k is 0.5 for all k. This numerical example clearly demonstrates the effects of QoS differentiation on performance characteristics. The business class not only experiences much better GoS, but also better communication quality during the calls. JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 CDMA CELLULAR MULTIMEDIA WIRELESS NETWORKS 423 Figure 12.18 illustrates the QoS differentiation with load-based fracturing factors for soft- blocking of new calls of the economic class. This is based on a simple scenario as follows. Again we assume handoff calls have the highest priority regardless of associated user class. The business class is served as long as resources are available. The economy class can share the resources equally with the business class if less than 65 % of effective resources are occupied, i.e. c is less than 0.47. Otherwise, if c is less than C l , invoke (12.56) with: a 0 21 (c) = 0.8, a 0 22 (c) = a 0 23 (c) = 0.6. If c is less than C u , a 0 21 (c) = 0.4, a 0 22 (c) = a 0 23 (c) = 0.3. Otherwise, a 0 21 (c) = a 0 22 (c) = a 0 23 (c) = 0. The offered traffic is the same as in the previous scenario. Figures 12.17 and 12.18 show that the performance characteristics of the system can easily be tuned by using either threshold-based hard blocking or fracturing factor-based soft blocking paradigms. For NRT packet radio access, additional parameters are given in Table 12.7. The average upper-limit UL data throughput for packet transmissions with 64 kbs and 10 ms TTI is presented in Figure 12.19 vs different offered traffic intensities of the three RT service classes, which are in 7:2:1, 5:4:1 and 4:3:3 MTIPs. The impacts of bit-rates on average upper-limit throughput with constant T p duration of 10 ms are presented in Figure 12.20. Figure 12.21 illustrates the effects of T p with a constant bit-rate of 64 kbs. Table 12.8 summarizes the mean values of aggregate RT traffic and quasi-stationary free 0.5 1 1.5 2 2.5 3 3.5 4 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 Common divisor of the offered traffic Blocking probabilities B1CS B2CS B3CS B21QF B22QF B23QF B11QF B12QF B13QF Offered traffic of the three service classes in 4:3:3 MTIP BxCS: SCAC system without QoS differentiation BxxQF: with fracturing QoS differentiation Figure 12.18 SCAC system with fracturing soft blocking QoS differentiation. Table 12.7 Parameter summary for packet radio access Definition Values T p Packet transmit duration 10, 20, 30 or 40ms R Bit-rate for packet transmissions 32, 64, 144 or 384 kbs γ p SIR target 3, 2, 1.5 or 1 dB for the above bit-rates, respectively JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 424 ADAPTIVE RESOURCE MANAGEMENT 0.5 1 1.5 2 2.5 3 3.5 4 0 5 10 15 20 25 Common divisor of the RT offered traffic Upper Limit of Throughput 7:2:1 MTIP 4:3:3 MTIP 5:4:1 MTIP Tp=10ms, Rp=64kbps Figure 12.19 Average upper-limit UL data throughput in different RT MTIPs. 0.5 1 1.5 2 2.5 3 3.5 4 0 5 10 15 20 25 30 35 Common divisor of the RT offered traffic Upper limit of throughput RT offered traffic in 7:2:1 MTIP T p = 10 ms 32 kb/s 64 kb/s 144 kb/s 384 kb/s Figure 12.20 Effects of the bit-rates to the throughput. capacity over T p time-interval of 10 ms. Figure 12.20 and 12.21 and Table 12.8 are for 7:2:1 MTIP. One can see that throughput characteristics are affected significantly by the dynamic of RT traffic as well as the packet-transmission parameters. The results give valuable quan- titative merits for studying the design parameters and the performance tradeoffs of packet access control schemes. This explains the motivations of using DFIMA scheme presented above, where content of feedback information provides 1:1 mapping of optimal transport format combination (including TPP, bit-rate, packet-length or TTI) for packet transmission in the next time-slot based on feasible free resource predictions. For example, assume 50 % cell capacity is occupied by the RT traffic of the 7:2:1 MTIP at the end of a given time slot of 10 ms. The possible bit-rates for packet transmissions are 32, 64 and 144 kbs. Consider JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 CDMA CELLULAR MULTIMEDIA WIRELESS NETWORKS 425 0.5 1 1.5 2 2.5 3 3.5 4 2 4 6 8 10 12 14 16 18 20 22 Common divisor of the RT offered traffic Upper limit of throughput RT offered traffic in 7:2:1 MTIP R p = 64 kb/s +Line: T p =10 ms xLine: T p =20 ms *Line: T p =30 ms oLine: T p =40 ms Figure 12.21 Effects of the packet transmission durations. Table 12.8 Means of stationary RT aggregate traffic and quasi-stationary NRT resource availability The three-class RT offered traffic, e.g. in 7:2:1 MTIP Common divisor Traffic 1 1.5 2 2.5 3 E[c] 0.1500 0.2249 0.2988 0.3690 0.4305 E[(z; T p = 10 ms)] 0.4609 0.3793 0.3032 0.2347 0.1772 two cases of the NRT offered traffic: 10 or 50 active data users in the next time-slot. Using Equation (12.65) and (12.66), one can predict the maximum numbers of successful packet transmissions for each possible bit-rate, e.g. in our examples 16.91 packets of 32 kbs, 10.76 packets of 64 kbs and 5.53 packets of 144 kbs. Therefore, in the case of having 10 active users in the next time slot, the feedback information should tell them to transmit their packet immediately with bit-rate of 64 kbs. If 50 users want to transmit their packet, they have to attempt with TPP of 16/50 and bit-rate of 32 kbs at the beginning of the next time slot. The expected throughput in this case is about eight packets since the free capacity is successfully utilized for packet transmissions with the probability of 1/2. The performance of DFIMA can be optimized with respects to TPP, bit-rate, TTI and QoS differentiation paradigms, which is flexible and effective. 12.4.16 Implementation issues Although the MdCAC and the SCAC policies are simple to implement without need for any special software and hardware, they may face a problem because the modeling parameters are required a priori. Owing to the diverse nature of different traffic sources and their JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 426 ADAPTIVE RESOURCE MANAGEMENT often-complex statistics, some of the parameters may be hard to determine without which the modeling-based CAC policies cannot operate. The soft-decision solutions are believed to give more flexibility in determining the modeling parameters, and thus are quite suited to achieving good multiplexing gain and robustness. On the other hand, implementations of the MsCAC policy require advanced hardware and software to ensure the reliability of measurements. For this reason, it is not cost-effective. Moreover, estimation errors in some circumstances may cause significant degradations of the system performance. However, the advantage of MsCAC is that it seems ‘insensitive’ to the traffic nature and the operation is robust. The network can learn and adapt to the statistics of traffic even when the burstiness of traffic is considered as out of control for the modeling-based systems. To gain tradeoff of all design criteria, a hybrid soft-decision/measurement-based implementation is a reasonable choice. Parameters needed for soft-decision functions, i.e. means and variances, can rely on auto-regressive measurements. For such solution, parameters and constraints can simply be thresholds of the UL interference level of cell and connection basis, an allowable outage probability, estimates of the current total received interference level with its mean and variance, etc. These are anyhow needed for the TPC mechanism of CDMA systems. For implementations of the DFIMA scheme, measurements or estimations of RT system load state for CAC can be reused. The NRT offered traffic needs to be measured or estimated for prediction of optimal parameters (e.g. TPP, bit-rate, TTI) that are used as the content of feedback information. Look-up tables for transport format combinations of UL packet transmission can be implemented or configured in both mobile and access network sides in order to minimize the size of feedback information. Eight bit feedback is enough to ensure sufficient exchange of control information in DFIMA, even with QoS differentiation. 12.5 JOINT DATA RATE AND POWER MANAGEMENT As already seen from the previous discussion the radio resource manager (RRM) contains a number of sub-blocks like the connection admission controller, the traffic classifier, the radio resource scheduler and the interference and noise measurements. The main role of the RRM is to manage the different available resources to achieve a list of target QoS. The radio resource scheduler (RRS) is an essential part of the RRM. The RRS has two important radio resources to control: MS transmitting power and transmitted data rate. The RRS uses those two resources to achieve different objectives like maximizing the number of simultaneous users, reducing the total transmitting power, or increasing the total throughput. The conventional way to achieve these objectives is to select one of them as a target to optimize and use other objectives as constraints. More sophisticated algorithms based on multiobjective (MO) optimization and Kalman filter techniques have been also proposed. Here we address the problem of how to combine the power and rate in an optimum way. Even Shannon’s equation shows that the achievable information rate in a radio chan- nel is an increasing function of the signal-to-interference and noise ratio. Increasing the information rate in data communication systems is restricted by the SINR. Increasing the SINR can be done in two ways. The first way is by reducing the total interference and noise affecting that user. This depends on some characteristics of the noise and the interference. For example, if the structure of the interference from other users is known at JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 JOINT DATA RATE AND POWER MANAGEMENT 427 the receiver then, by applying one of the multi-user detection methods, that interference can be reduced. Also if the users are spatially distributed then the interference can be reduced by using a multi-antenna system (see Section 12.1). If the users concurrently use the channel (as in DS-CDMA) then the interference can be reduced using power control techniques. From previous studies we can see that some characteristics of the interference are assumed to be known or can be controlled. There are many sources of interference and noises that cannot be reduced by the first way such as thermal noise, interference from other cells etc. The second way of increasing the SINR is simply by increasing the transmitted power. In a single user communication (point-to-point) or in broadcasting, this can be an acceptable solution and the main disadvantages are the cost and the nonlinearities in the power am- plifiers. However, in a multiuser communication environment, increasing the transmitted power means more co-channel and cross-channel interference problems. Therefore, a joint control of data rate as well as the transmitted power is an important topic in modem communication systems. The modern communication systems (3G or 4G) are supporting the multirate data communication because they are designed not only for voice communication but also for data and multimedia communication. An efficient combining algorithm for the power control and the rate control is required for these systems. The term ‘efficient’ here refers to optimization of the transmitted power and data rate to meet the required specifications. There are many proposed combining algorithms for power and rate control in the literature. The objectives of those algorithms are quite varied. Some algorithms suggest maximizing the throughput; others minimizing the packet delay or minimizing the total power consumption. The 3G/4G mobile communication systems based on WCDMA support the multi- rate transmission. There are mainly two methods to achieve the multi-rate transmission, the multicode (MC) scheme and the variable-spreading length (VSL) scheme. In the MC-CDMA system, all the data signals over the radio channel are transmitted at a ba- sic rate, R b . Any connection can only transmit at rates mR b , referred to as m-rate, where m is a positive integer. When a terminal needs to transmit at m-rate, it converts its data stream, serial-to-parallel, into m basic-rate streams. Then each stream is spread using different and orthogonal codes. In a VSL-CDMA system, the chip rate is fixed at a specified value (3.84 Mb/s for UMTS) and the data rate can take different values. This means that the processing gain (PG) is variable. The processing gain can be defined as the number of chips per symbol. 12.5.1 Centralized minimum total transmitted power (CMTTP) algorithm The mathematical formulation of the CMTTP problem is find the power vector P = [P 1 , ,P Q ] T and the rate vector R = [R 1 , ,R Q ] T minimizing the cost function: J(P) = 1 T P = Q  i=1 P i (12.68) given that the required signal-to-noise ratio is guaranteed to each user R s R i P i G ki Q  j=1 j=i P j G kj + N i ≥ δ * i , ∀i = 1, ,Q (12.69) JWBK083-12 JWBK083-Glisic February 23, 2006 4:54 Char Count= 0 428 ADAPTIVE RESOURCE MANAGEMENT and P min ≤ P i ≤ P max , R i ≥ r i , ∀i = 1, ,Q (12.70) where δ * i is the minimum required SINR for user i, r i is the minimum rate limit for i. The problem presented in Equations (12.68)–(12.70) can be reduced to a system of linear equations. If the constraints Equations (12.69)–(12.70) cannot be achieved, then the problem is called infeasible. In this case either some user should be dropped from this link or some of the constraints should be relaxed. At the optimal solution, all QoS constraints are met with equality. Also, the optimal power vector is the one that achieves all rate constraints with equality. So, the optimum rate vector is R * = [r 1 , ,r Q ] T . The corresponding power vector can be obtained by solving the QoS equation. This is a system of linear equations in power. From Equation (12.69) we have R s r i P j G kj Q  j=1 j=Q P j G kj + N i δ T i , ∀i = 1, ,Q (12.71) where δ T i is the target SINR for user i. Let ˜ r i = δ T i r i /R s and substitute it into Equation (12.71), to obtain P i = ˜ r i ⎡ ⎣ Q  j=1 j=i G kj G ki P j + N i G ki ⎤ ⎦ (12.72) In matrix form P = rHP +ru (12.73) where H ij = ⎧ ⎨ ⎩ 0 i = j G kj G ki > 0 i = j u i = N i G ki (12.74) r = diag{ ˜ r 1 ˜ r Q } (12.75) Then the optimum power vector is P * = [I −rH] −1 ru (12.76) In order to obtain a nonnegative solution of Equation (12.77), the following condition should hold: ρ(rH) < 1 where ρ(A) is the spectral radius of matrix A. 12.5.2 Maximum throughput power control (MTPC) This algorithm has been suggested in Chawla and Qiu [85]. The algorithm is based on the maximization of the total throughput in a cellular system. There is no need to generate all solutions in this method. Since the gain links and the interference of other users are [...]... SHARING IN WIRELESS NETWORKS 439 Table 12.9 Comparison of MTPC and MTMPC algorithms MTMPC algorithm λ1 = 0.0001 and λ2 = 0.9999 User 1 2 3 4 5 MTPC algorithm λ1 = 1 and λ2 = 0 P(dBw) SINR(dB) p(dBw) ¯ SINR(dB) −13.9789 − 16. 8918 −4 .61 87 −12.8725 −14.3 460 Average power (W) = 0.1 16. 8345 −0.8548 36. 09 56 8 .63 83 −5.9278 Sum[SINR(dB)] = 54.78 −0.5580 6. 6072 13.4 264 1.2111 −1.5289 Average power (W) = 5 16. 9295... [85, 87] is used Consider the system with Q = 5 users and the path gain matrix, G, shown below ⎧ ⎫ ⎪ −5.8 −18.2 −55.3 −20.3 −33 .6 ⎪ ⎪ ⎪ ⎪ ⎪− 36. 0 −9.7 −43.5 −22.2 −15.9⎪ ⎨ ⎬ G(dB) = −41 .6 −30.9 −9.3 −38 .6 − 36. 5 ⎪ ⎪ ⎪−14.2 −20 .6 −38.5 6. 8 − 36. 6⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ −22 .6 −23.9 −20.1 − 16. 4 −10.8 The tradeoff factors have been set to {λ1 , λ2 } = {0.9999, 0.0001} In this case we penalize power usage From Table... 54.78 −0.5580 6. 6072 13.4 264 1.2111 −1.5289 Average power (W) = 5 16. 9295 −0.9234 36. 8300 8.5 561 6. 5922 Sum[SINR(dB)] = 54.80 12 .6 DYNAMIC SPECTRA SHARING IN WIRELESS NETWORKS In this section we present schemes for interference suppression in UWB-based WPAM systems when sharing the same band as other communications networks The scheme can be used to significantly improve the performance of UWB systems... interference; b, QPSK interference; c, 16QAM interference; d, 64 QAM interference; e, 256QAM interference; f c = 800 MHz; J: S = 30 dB; SNR = 7 dB; M = 4; = 5 ns; vbTH = 5 Mbt/s; Tframe = 10 ns 0.1 Pe 64 32 200 8 400 T /T j c N 16 0.01 4 60 0 800 2 1000 Figure 12.29 Error probability as a function of OFDM interference bit duration and the number of subcarriers OFDM/16QAM interference; f c = 800 MHz; J :... SHARING IN WIRELESS NETWORKS 453 A (a, b, c, d, e) C (a, b, c, d, e) 0.1 Pe B e d c b a 0.01 -20 0 20 40 60 80 J:S (dB) Figure 12.27 Error probability as a function of interference-to-signal ratio A, without interference rejection; B, with interference rejection circuit; C, with classical LMS interference rejection filter; a, PSK interference; b, QPSK interference; c, 16QAM interference; d, 64 QAM interference;... member 12 .6. 13 Performance examples Figure 12. 26 presents the results for BER as a function of signal to noise ratio SNR, in the presence of different PSK/QAM type interfering signals Additional parameters of the signals are: filter length M = 4, = 5 ns, vbJ = 100 Msymbol/s, vbTH = 5 Mbt/s, 452 ADAPTIVE RESOURCE MANAGEMENT 0.01 e d1 d a Pe f, f1 b, b1 c g 1×10-3 6 8 10 SNR (dB) Figure 12. 26 Error probability... the number of subcarriers is increased The online source [91] gives historical perspective to UWB technologies It lists down the early UWB references and patents from the 1 960 s and 1970s In [92] a comprehensive overview of UWB wireless systems is given It discusses the FCC allocation of 7.5 GHz (3.1–10 .6 GHz) unlicensed band for the UWB devices Potential UWB modulation schemes, multiple access issues,... is the delay in the lth path If we consider signal sampled at chip interval Tc we have: r (k) = r I (k) + jr Q(k) (12.125) r I (t) = r (t) cos ωc t (12.126a) r Q(t) = r (t) sin ωc t t k= Tc (12.126b) where (12.127) DYNAMIC SPECTRA SHARING IN WIRELESS NETWORKS A filter Finger 1 445 + r(k) − Rake combiner and detector B filter Finger 1 A filter Finger Lc Data + − B filter Finger Lc Figure 12.22 Receiver... detrimental to UWB if it is located at the UWB system’s nominal center frequency In the GPS band the DS based UWB system interfered less than the time hopping system DYNAMIC SPECTRA SHARING IN WIRELESS NETWORKS 443 12 .6. 8 Channel estimation/imperfections Channel estimation for time hopping UWB communications is dealt with in [115] Multipath propagation and MAI are taken into consideration Maximum-likelihood... interference, J: S = 40 dB, with interference rejection filter; d, QPSK interference, J: S = 40 dB, with interference rejection filter; e, 16QAM interference, J: S = 40 dB, with interference rejection filter; f, 64 QAM interference, J: S = 40 dB, with interference rejection filter; g, 256QAM interference, J: S = 40 dB, with interference rejection filter; Error probability based on estimated detection variable signal . = ⎧ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎩ −5.8 −18.2 −55.3 −20.3 −33 .6 − 36. 0 −9.7 −43.5 −22.2 −15.9 −41 .6 −30.9 −9.3 −38 .6 − 36. 5 −14.2 −20 .6 −38.5 6. 8 − 36. 6 −22 .6 −23.9 −20.1 − 16. 4 −10.8 ⎫ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎭ The tradeoff. are 32, 64 and 144 kbs. Consider JWBK083-12 JWBK083-Glisic February 23, 20 06 4:54 Char Count= 0 CDMA CELLULAR MULTIMEDIA WIRELESS NETWORKS 425 0.5 1 1.5 2 2.5 3 3.5 4 2 4 6 8 10 12 14 16 18 20 22 Common. 0. 369 0 0.4305 E[(z; T p = 10 ms)] 0. 460 9 0.3793 0.3032 0.2347 0.1772 two cases of the NRT offered traffic: 10 or 50 active data users in the next time-slot. Using Equation (12 .65 ) and (12 .66 ),