Advanced Wireless Networks - 4G Technologies

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The results are important for optimizing the overall Erlang capacity and throughputs with differentiated services in WCDMA segments or domains of wireless IP networks for multimedia applications.

A variety of emerging technologies and protocol enhancements has been investigated and designed to provide IP-based multimedia services to mobile users. These research and standardization efforts are summarized in References [45–47]. By using the radio resource management framework, in this section we address some simple solutions for future network operators to customize and optimize the performance of their multimedia networks serving various user and network profiles.

The traffic-engineering framework for wireless multimedia networks must be simple and flexible for supporting a variety of services with QoS differentiation to satisfy wide range of customer demands and to maximize revenues. The operators need to have an in-hand tool to manage their network operation more or less similarly to the airlines customerservice systems where passengers are categorized into classes receiving different QoS on any required routes, but all sharing expensive limited space of planes. The limitation of extremely expensive radio resources is also the bottleneck of future wireless networks. The key in providing the QoS for users, while efficiently sharing radio resources to optimize the GoS for networks, is the access control at the air interface acting as ticket-selling and checkin processes. The access control herein includes CAC for the RT connection-oriented service domain and packet access control for the NRT connectionless service domain. Hence, by using intelligent access control strategies, we can have an effective solution to the twofold objective: (1) to optimize the network utility; and (2) to provide operators freedom in controlling pay-load and group behavior of traffic classes regarding required services as well as subscriber potential classes, e.g. gold, silver and bronze.

To date, a significant number of papers and standards dealing with various issues of CDMA wireless networks have been published. Major efforts have been paid to study detailed features and specific algorithms, e.g. receivers, power control, handoff, etc. Investigations of the CDMA system capacity, radio resource and teletraffic management have been mostly based on single-service ‘worst case’ scenarios, or high-cost simulations [50]. In more sophisticated scenarios, the simplicity requirement is usually ignored despite the fact that only this condition can ensure system efficiency and guarantee fast response to multiple QoS requirements from the users [52]. Examples are References [44, 47, 59], where valuable results are presented for measurement-based adaptive admission control and bandwidth management schemes. However, it has been pointed out, e.g. in Reference [47], that the price of better performance offered by these schemes is an increased complexity of the implementations in both hardware and software. The results on CAC and packet access control in CDMA wireless networks are summarized extensively in References [49, 50, 51, 52, 54].

This section emphasizes practical operating aspects of multimedia CDMA mobile cellular networks, used as segments of the future wireless IP, and the need for simple and effective radio resource and traffic handling mechanisms. This is investigated in contexts of the access control, which is mainly viewed as an optimal interference management problem in CDMA systems. Various CAC policies are considered simultaneously and an effective soft-decision mechanism, namely SCAC, is presented. The motivation behind SCAC is to use probabilistic functions, adapted to inter-cell interference distributions, for making decision upon receipt of a call request in order to optimize the radio resource utilization (RRU) of interference-limited CDMA systems, while keeping overall communication quality under

acceptable outage levels. This method requires neither complex measurements nor mutual exchange of information between the adjacent cells about the state of the network. Thus, the complexity and the cost of implementations can be significantly reduced. This is of importance in the wireless IP networks where a number of segments or domains are involved. The scalability also favors distributed or localized control of radio resources. The normalized load factors, which essentially characterize spread spectrum CDMA transmissions, are used to represent the resource consumption, similarly to the well-known effective bandwidth concepts [50, 68]. Because attributes of the required service and adequate transmit power control (TPC) schemes can provide and insure a priori knowledge and stationary behavior of the resource consumption [41–44, 46, 53], SCAC can be promising in providing good multiplexing gain and robustness. Multiple call-blocking thresholds or load-based fracturing factors are introduced that give operators a simple and flexible means of differentiating and/or prioritizing different types of calls (e.g. new call and handoff call), services and/or subscription classes for desired serving customs. The performance of CAC policies is evaluated with respect to the following:

(1)effectiveness represented by tradeoffs of the simplicity in implementations and the ability in autoconfiguring with various users QoS and networks GoS profiles for maximizing system capacity or revenues; performance is measured by the probabilities of call blocking and handoff failure or some additional cost functions;

(2)reliability measured by the probability of losing communication quality during services, which is also known as the outage probability when the received interference exceeds the tolerable level;

(3)robustness seen against a need for redesign due to uncertainty and changes of the system parameters.

With respect to controlling NRT packet radio access in UL, using the free capacity left by the RT traffic , this section provides

(1)a precise dimensioning of the UL radio resources available for packet access;

(2)estimates of the average upper-limit UL data throughput for different bit-rates and packet-lengths that can be used for tradeoff of the design parameters for packet access control schemes;

(3)the dynamic feedback information-based multiple access scheme (DFIMA), which is suitable for QoS differentiation in background services.

These issues are increasingly important for the efficiency of the RRU in serving mobile messaging services. Examples are short message service or wireless e-mail, which have been taking the consumer market by storm recently.

The analytical models are presented based on modified product-form loss network models and results of the stochastic knapsack problem [64–67]. The priority handoff and the cellcondition of user mobility equilibrium are considered. The UL dynamic resources and upper-limit throughput for NRT packet transmissions are estimated using asymptotic and quasi-stationary analysis of the RT traffic. This provides a reliable and flexible tool for designers and operators in teletraffic engineering of the networks. Only the UL direction is chosen to present the results. The traffic asymmetry between UL and DL is therefore not taken into consideration. The RT services are of CBR classes, whereas the NRT packet

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services are of background classes having a lower priority that can tolerate moderate delays. The VBR and the ABR interactive services, pre-emption in QoS differentiation, are not considered. Despite these limitations, such a practical and comprehensive system model and analysis for radio resource and teletraffic management of multimedia CDMA cellular networks, with discussion of simple and effective SCAC and DFIMA, should be of interest to system designers. Moreover, this section lays the groundwork for investigating more advanced control mechanisms as well as more sophisticated traffic models for future wireless IP networks.

12.4.1 Principles of SCAC

An overview of publications on QoS and resource management including CAC techniques in 3G multimedia networks can be found in References [52, 54]. In general, CAC policies can be divided into two categories: static and dynamic [59]. From the implementation point of view, these can be further divided into modeling-based and measurement-based policies [57]. Under the static CAC category, a call request is accepted only when sufficient resources are available to meet its required QoS and to maintain the QoS of ongoing calls. The admission controller must decide which resource combinations can be accepted into the network. In FDMA/TDMA cellular systems, the fixed channel assignment-based policy [55] is often adopted because of its simplicity and capacity maximization in line with a fixed threshold of the number of channels (cell-capacity). In CDMA cellular systems, such a fixed threshold is hard to determine efficiently because the qualitative and quantitative features of CDMA systems appear to be heavily inter-twined and mutually supportive. Under the dynamic CAC category, a call request can be rejected even if a set of new calls may meet their QoS requirements. On the other hand, a new call can be accepted into the network even if the instantaneous QoS may be violated, but when averaged over states, the service quality is met.

Therefore, the network utility can be increased and various cost-effective and trafficshaping policies can be adopted. The reinforcement learning-based CAC, described by a semi-Markov decision process, is believed to be a good solution for a class of adaptive policies [56, 59]. Later in this section, a much simpler and more effective dynamic SCAC is presented by exploiting the soft-capacity feature of CDMA radios.

Consider the UL of a cell in a CDMA cellular system consisting of multiple, identical and independent cells using omni-directional antennas in BS. This network is supporting M independent connection-oriented RT service classes, each with a constant bit-rate and a target value of signal-to-interference ratio (SIR) for meeting its QoS requirements. Reference [53] describes the spectral efficiency of an interference-limited CDMA air-interface in term of load factors.

The starting point in this concept is the target signal to noise ratio for a given user k defined as

where ρk is the voice activity factor, rk data rate, Pk received power, W signal bandwidth and Itot includes overall interference, from its own cell, other cells and noise. The previous

relation can be also represented as

where wk is referred to as the load factor.

Let K = {1, 2, . . . ,M}. Define: n = (nk , k K) is the occupancy vector, where nk is the number of class-k calls in progress; w = (wk , k K) is the load vector, where wk is the average load factor of a class-k connection. For all k K, let rk be the bit-rate, γtarget k the target SIR and ρk the activity factor of class-k data-sources. The most common way to define the average capacity of a cell in CDMA cellular systems is based on the load equation in the boundary condition of the cell tolerable (required) interference level, I0req. Let Ca be an average capacity of a cell. This represents the spectral efficiency and can be expressed as follows:

where Iown is the own-cell interference level, i.e. the received power caused by signals of active users in the cell, and Itotal is the total interference level, i.e. the total received power in UL including thermal noise and interference caused by other cells. It is shown in References [46, 53] that the mean and the variance of other-cell interference can be approximated by own-cell interference multiplied by a constant coefficient f . Let η be the fraction of the thermal noise density with respect to I0req. The left side of Equation (12.2) can be rewritten as [46]:

for a voice-service connection is around 0.5 %, which means the system can serve atmost nk = Ca/wk = 130 voice calls simultaneously. It has been shown theoretically and by simulations [41–46] that the values of SIR and f can vary significantly due to the changes of propagation parameters, TPC inaccuracy, traffic distributions, etc. For instance, SIR has been found in References [42, 46] as a log–normal random variable with total error standard deviation of the order of 2 dB. The parameter f varies between 25 and 55 % in a microcellular environment with omnidirectional antennas [53]. The fluctuations of local average SIR and other-cell interference distribution have essential impacts on system capacity resulting in the ‘soft-capacity’ feature of CDMA systems.

Based on the knowledge of local average SIR and interference distributions, their bounds, means and variances, the resource consumption of connectionand cell-basis can be modeled as follows. For all k K, the resource consumption of a class-k connection is represented by average load factor of its service class, which is a stationary, independent and bounded

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Table 12.4 Parameters for microcellular multiservice WCDMA system

normal-distributed random variable having mean wk and falling in between a maxand min-guaranteed resource consumption wu,k andwl,k . Similar to the well-known effective bandwidth concepts, this can be easily extended for modeling resource consumption of a VBR or ABR connection based on extensive reported results in analysis of, e.g., ATM networks. The cell-capacity is modeled accordingly with a bounded Gaussian random variable having mean Ca and lying in between Cl and Cu. The mean and boundary values of resource consumption and cell-capacity can be estimated using Equations (12.1) and (12.3) together with mean and boundary values of local average SIR target and f . Further, Cl and Cu constraints can be more relaxed to determine. For example Cu can be set up to (1 − η) in theory.

The most straightforward CAC scheme is to share Ca units of the cell resources completely and statically among calls of different service classes, where a class-k call consumes wk units of resources for its service. This leads to product-formed loss network model with modeling-based static complete-sharing CAC (MdCAC), where a call request for class-k service is accepted immediately if at most (Ca − wk ) units of resources are occupied, otherwise rejected. Such MdCAC in CDMA cellular systems may face the following problems:

(1)the QoS guaranteed scenario results in the ‘worst case’ of system capacity and RRU;

(2)there is a need for redesign of the system-capacity constraint Ca for the changes of CDMA radio channel parameters and traffic statistics;

(3)the advantages of CDMA techniques, such as the ‘soft capacity’ feature, cannot be exploited.

The results of recent research have emphasized that robust CAC policies may require online measurements [57, 59], resulting in far more complex and high-cost SW/HW implementations [47, 49].

The principle of measurement-based CAC (MsCAC) is that a new call is accepted immediately into the system if at least the required resource consumption of its service class is available, otherwise rejected. The decision is made based on online measurements of the related statistics. Analysis and simulation results show that, when the UL traffic is less bursty, the measurement-based CAC has no capacity-gain over the modeling-based CAC described above. It may even suffer degradation due to measurement errors, which agrees with References [49, 57, 58].

To harmonize the advantages and overcome the problems of MdCAC and MsCAC with a simple and effective SCAC policy, let us first reconsider Equations (12.2) and (12.3) in a slightly different way. Owing to the interference-limited nature of CDMA cellular networks, n-forming active-user combinations in the cell shall meet their QoS constraints if:

where cother represents the load factor produced by transforming the other-cell interference; cown = nw represents load factor generated by n-forming active-user combinations in the cell, n is the occupancy vector and w is the load vector defined above. Because it has been shown that cother is well modeled with a Gaussian random variable [41–43, 46], we have:

The above relations imply that, as UL traffic increases beyond the average capacity, whether users experience a degradation of the communication quality depends on the other-cell interference level. Therefore, an optimal way to benefit from this soft-capacity feature is to use a dynamic CAC mechanism having decision functions adapted to interference distributions to compensate for the fluctuation of local average SIR while expanding the admissible region. SCAC is such a solution that works as follows. Instead of using an average cell capacityCa, use an upperand a lower-limit denoted by Cu and Cl, respectively. This improves the system capacity, at the same time enhances the robustness. In principle, the mechanism of SCAC is defined as follows:

(1)a new call of class-k shall be admitted immediately into the system if the current UL load factor is at most (Cl − wk );

(2)else if the current own-cell UL load factor is at most (Cu − wk ), a new call of class-k shall be admitted with a permission probability based on Equation (12.5), which is given in detail later;

(3)otherwise, a new call of class-k shall be rejected immediately;

(4)nonpre-emptive priority is given to handoff calls, i.e. handoff failure of class-k calls occurs only when more than (Cu − wk ) resources have been occupied already.

SCAC policy is supposed to give advantages over both MdCAC and MsCAC policies, i.e. simple, robust and improved capacity utilization. Soft decisions can also be combined with measurement-based techniques to reduce the complexity of estimators, to compensate for bias and to enhance performance. The implementation issues will be addressed later. Moreover, one can notice that SCAC provides better traffic shaping gain than MdCAC and MsCAC. It gives more chance for calls of high resource-consuming classes to access the system when traffic intensity of lower classes is heavy that cannot be improved with the other two.

12.4.2 QoS differentiation paradigms

To regulate the operation of multimedia systems in the way mentioned above, the access control has to consider not only characteristics of requested services but also subscription profiles of user classes. This will provide fair decisions in resource allocation for agreed QoS upon accommodating a new call request. For example, a gold-class customer request for any services shall be served immediately as long as there are enough network resources for that. On the other hand, operators might reject all bronze-class requests as well as resource-consuming requests from silver-class if the current load has already exceeded a certain level, and so forth. However, once a call request is accepted, QoS in terms of BER for that RT connection is assured independently of its associated user class. QoS differentiation paradigms for CBR services specify such serving traffic patterns depending on offered traffic intensity distributions among classes during different busy periods of the day for shaping traffic and maximizing revenues. These therefore need to be simple and easy to reconfigure

and extend for a variety of user and network profile characteristics. QoS differentiation can be viewed as traffic prioritization in CAC. Herein, we use multiple thresholds or load-based fracturing factors for hard or softer blocking of call requests. The methods are similar to the well-known threshold dropping or weighted fair queueing mechanisms, but no actual queues are allowed in this system. Thus, system resources are shared noncompletely among RT traffic classes in a blocked-call-cleared fashion. Define: c = nw is the stationary system load state, which is identical to cown above. Suppose there are J different user classes sorted in the decreasing order of priority, e.g. 1 is gold, 2 is silver, etc. Let J = {1, 2, . . . , J }. Taking M different service classes into account, which are sorted in the increasing order of resource consumption, we introduce a J × M table of prioritized admission probability given in matrix-form as follows:

where a jk (c) is the admission probability of a call request for class-k service from a class- j user, which depends on system load state and needs further modification when combining with SCAC described above. However, it can be considered that there are J × M prioritized traffic classes, which form group behaviors according to user class or service class.

Note that hereafter we use term ‘traffic class’ or class-( j, k) when QoS differentiation is applied to distinguish from service class-k alone in complete-sharing scenarios. In hard threshold blocking case, straightforward, J × M thresholds may be needed, each corresponding to a traffic class. Define a threshold table given in matrix-form as follows:

L = [l jk ] j J, k K, l jk ≥ luv if j ≥ u and k ≥ v

Thus, not taking effects of SCAC into account, a jk (c) can be determined as:

The number of needed thresholds is significantly reduced if only group behavior levels are of interest. That is, J or M thresholds may be needed instead of J × M. For example, the first priority user class has a single threshold up to upper bound Cu of the system capacity regardless of required services, whereas the lowest priority user class has a threshold as lower bound Cl of the system capacity for any services. The access of the lowest priority user class would not be granted for k to M services if system load state exceeded lJ k ≤ Cl. In fractional (soft) blocking case, a jk (c) may not take a hard value of 1 or 0, but in between depending on system load state and priority of traffic class, resulting in load-based fracturing factors for each traffic class or group behavior. As a simple example, let say a call request from a class- j user for class-k service is admitted immediately into the system if the load state after that does not exceed the l jk threshold; else it is accepted with a probability of 0.8 if the system load state after that does not exceed the lower bound Cl of the system capacity; else it is accepted with a probability of 0.2 as long as there are enough resources left for that. Generally, this QoS differentiation paradigm on the one hand gives operators better flexibility to customize and to tune their network performance; on the other hand it allows unified analysis of a class of guard resource schemes for QoS differentiation and CAC. The hard threshold blocking case described previously is in fact a special variation of this fractional rule.

Up to this point, handoff calls have been assumed to have either the highest priority regardless of their associated traffic classes or equal priority as of new calls regarding their

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associated traffic classes. If not so, prioritization of handoff calls can be handled similarly to new calls.

QoS differentiation of the background NRT packet-switched services can be done not only in blocking or dropping fashion, but also in granting throughput-delay of a connection basis. For instance, the transmissions of certain customers should be guaranteed with higher data-rate and lower delay. Detailed paradigms and mechanisms are discussed later.

12.4.3 Traffic model

The traffic model is based on the following assumptions:

(1) Sources are of the ON–OFF nature. Unless stated otherwise, ρk = 1 for all k K. The impacts of employing user activity detection are precisely studied in the example of a single-class system.

(2)TPC is sufficient to ensure that the standard deviation of the local average SIR is always in control. The cell-capacity as well as the resource consumption of each class-k connection for all k K is a bounded Gaussian random variable lying in [Cl, Cu] and [wl,k , wu,k ] respectively with means and variances given in Table 12.1.

(3a) Without QoS differentiation users and calls are served on complete-sharing FCFS (first come first served) basis. For all k K, class-k calls arrive at the corresponding cell according to Poisson process with rates λl,k and λhl,k for the new and handoff calls respectively.

(3b) With QoS differentiation, for all j J and k K, let λl, jk and λhl, jk be Poisson arrival rates for new and handoff calls respectively of traffic class-( j, k).

(4)The mean call-holding time given that there are no handoff failures is 1/μ1, and the outgoing handoff rate per cell per call is μ2, which is commonly valid for all classes of calls. These are the exponentially distributed random variables. Therefore, the service time in the cell, i.e. cell-resident time, is also an exponentially distributed random variable having a mean equal to 1/μ = 1/(μ1 + μ2). In the condition of user mobility equilibrium, i.e. the mean number of incoming mobile terminals equal to the mean number of outgoing mobile terminals per time-unit per cell, there are

two simple models to determine the value for μ2 [60]. Denote: v as the speed of a mobile terminal, L as the length of cell-perimeter and A as the cell-area. In a macrocellular environment, terminals usually move with high speed along the cell in one direction. The linear model can be used to approximate μ2 as follows:

In a microcellular environment represented by a two-dimensional model with user random movement, μ2 is given by:

With assumption (3a), the arrival rate of handoff calls can be approximated also as in [61] and [62]:

where Bk is the new-call blocking probability and Fk is the handoff failure probability of class-k. With assumption (3b), we have:

where B jk is the new-call blocking probability and Fjk is the handoff failure probability of class-( j, k).

(5)Three different CAC policies are adopted alternatively for the performance comparison purposes. The first and the second one are the modeling-based MdCAC and the measurement-based MsCAC of static complete-sharing policies. The third one is the SCAC policy, which is discussed first in complete-sharing and then in QoS differentiation scenarios.

For the complete-sharing SCAC, define: πk (c) ≡ the admission probability of a new call for class-k service when the system is in state c. From Equations (12.45), (12.46) and (12.47), πk (c) can be given by:

To avoid the overestimate of Cu that may increase the system outage probability, some adjustments of the mean and variance, e.g. by increasing them slightly, or other functions, such as the incomplete Gamma, can be used. For the later option, π k (c) becomes:

where (ν, x) is the incomplete Gamma distribution function. The α and ν parameters have the following relations:

fE [c] = ν/α and f Var[c] = ν/α2

In general, E[c] and Var[c] can be calculated by using steady-state solutions and inversion techniques. To reduce the computation complexity, the corresponding mean and variance values of MdCAC system can be reused. This is reliable because, with assumptions (1) and

(2) above, the loss network model can be expected to represent well the stationary behavior of those systems. For SCAC with QoS differentiation, by invoking Equation (12.8) we have:

where the a0 jk (c) factor is the admission probability of traffic class-( j, k) determined by QoS differentiation paradigms; the πk (c) factor comes from optimal interference management of SCAC given by Equation (12.14) or (12.15). The connection-oriented RT traffic has higher priority over the NRT traffic, which is served as background services. For clarity, additional modeling issues and assumptions for NRT packet access and services are discussed later.