Advanced Wireless Networks - 4G Technologies

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a data-only system and Viterbi [48] proposed a 1-bit feedback scheme in an integrated voice/data system. The implementations might vary from one to another, but they all agreed that the feedback schemes should be simple and effective. Based on the results from the previous subsection, TPP and optimized bit-rate for packet transmissions in the next timeslot can be predicted. In the case of QoS differentiation, TTI or packet-length can be changed as well. These parameters can be transmitted in the content of feedback information. For instance, let the service criteria be to serve as many users simultaneously as possible with optimal throughput-delay tradeoff and no QoS differentiation. The number of active data users in next time-slot, D = N , and load state c* of the RT traffic at the end of current time-slot are supposed to be known. Based on Equations (12.65) and (12.66) together with assumption (4a), a bit-rate can be chosen so that the S(c*)/D factor is as close to 1 as possible. TPP for users attempting to transmit their packet in next time-slot is set equal to min[1, S(c*)/D]. Thus, the maximum number of users can be served with optimum UL throughput and reasonable average packet-delay of S/D, where S is given by Equation (12.67). However, one can notice that, if a terminal has a packet to send, it needs to wait until next time-slot to attempt its transmission. Therefore, access delay to the first attempt per packet can be up to 1 time-slot. A packet transmission can be synchronized to the beginning of a mini-slot (e.g. a 10 ms radio frame can be further divided into 15 mini-slots as in 3GPP standards) to reduce the delay. This results in a spread-slotted asynchronous multiple access system with feedback control, which is similar to the schemes reported in References [71, 75–79] for a data-only CDMA radio system. DFIMA can be extended to provide flexible means of QoS differentiation. This can be done through setting different TPP or/and TTI or/and bit-rate for different user classes in feedback information depending on load state of the RT traffic and offered load of the NRT traffic. DFIMA can be expected to outperform the well-known ALOHA and CSMA in throughput-delay tradeoff to the same extend as shown in Rappaport [71]. Also, this approach should overcome the hidden terminal problem using feedback, and be simpler and more flexible than PRMA.

12.4.15 Performance examples

A number of numerical and simulation results are presented in this section in order:

to quantify the system performance and the benefits of using the described SCAC and the QoS differentiation;

to study the effects and tradeoffs of the design parameters in the access control on the system behaviors and the performance characteristics through various system scenarios;

to demonstrate the flexibility and the accuracy of the analytical methods used to evaluate the teletraffic performance of WCDMA cellular systems in this section.

The results are presented for a cellular system supporting three RT service classes: class 1 is 12.2 kbs voice, class 2 is 64 kbs video and class 3 is 144 kbs multimedia calls. The parameters are given in Table 12.4, which are in agreement with Ariyavisitakul [53]. Owing to limited space, we present results for offered traffic intensities of the three service classes in following proportions: 7:2:1, 5:4:1 and 4:3:3 of class 1, class 2 and class 3, respectively. These proportions are called multimedia traffic intensity profiles, MTIP. In words, let λ be a so-called common divisor of the three-class offered traffic; 7:2:1 MTIP, for instance, means

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Figure 12.13 SCAC vs MdCAC in 4:3:3 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 equilibrium blocking and dropping characteristics compared with the Gaussian function. The freedom of choosing such decision functions to fulfill the performance requirements increases 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

Figure 12.15 SCAC vs MdCAC in 7:2:1 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

Figure 12.18 illustrates the QoS differentiation with load-based fracturing factors for softblocking 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 Cl , invoke (12.56) with:

a0 21(c) = 0.8, a0 22(c) = a0 23(c) = 0.6. If c is less than Cu, a0 21(c) = 0.4, a0 22(c) = a0 23(c) = 0.3. Otherwise, a0 21(c) = a0 22(c) = a0 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 Tp duration of 10 ms are presented in Figure 12.20. Figure 12.21 illustrates the effects of Tp with a constant bit-rate of 64 kbs. Table 12.8 summarizes the mean values of aggregate RT traffic and quasi-stationary free

Common divisor of the offered traffic

Figure 12.18 SCAC system with fracturing soft blocking QoS differentiation.

Table 12.7 Parameter summary for packet radio access

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

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

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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 channel 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