Advanced Wireless Networks - 4G Technologies

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discussed in Madani and Aghvami [40], the DCCA system results in lower probability of forced termination of calls. Computer simulation showed a drastic reduction in the number of handoffs and almost 50 % less forced termination of calls compared with the ARP scheme.

12.2 RESOURCE MANAGEMENT IN 4G

Based on the discussion presented so far in Chapters 11 and 12 an integrated bandwidth management approach is now presented that can be implemented in next generation wireless networks that support multimedia services (data, voice, video, etc.). The main principles of the approach are summarized as follows:

(1)The system supports multiple classes of service that require various levels of quality that may range from strict to flexible and soft QoS requirements.

(2)Advanced bandwidth reservation is performed to the cell a mobile user is moving towards in order to assist and support a seamless handoff process.

(3)User mobility is introduced into the reservation scheme and process in order to optimize the efficiency of handoff mechanisms and minimize, if not eliminate, the unnecessary reservation of resources and, therefore, improve the system capacity and throughput.

(4)Bandwidth reconfiguration processes are used that may allow the efficient resource redistribution in a cell to satisfy the QoS requirements of all the mobile users in the cell, especially when users with flexible QoS requirements are supported in the system.

(5)A mobile agent based framework is used to facilitate the efficient implementation of the above integrated approach.

One of the elements in future resource management is mobile locationing and tracking. Position location technology can be loosely classified into two major categories: mobile-based solution and network-based solution. The global positioning system (GPS) is a worldwide radio-navigation system formed from a constellation of 24 satellites and their ground stations. As an alternative to mobile-based approaches, cellular networks can be used as the sole means of providing location services, where the MSs are located by measuring the signals traveling to and from a set of fixed cellular BSs. The signal measurements are used, for example, to determine the length and/or direction of the individual radio paths, and then the MS position is computed from geometric relationships. Basically, radiolocation systems can be implemented based on either signal strength, angle of arrival (AOA), or time of arrival (TOA) measurements or their combinations [41, 42].

However, there are several problems associated with the existing network-based geolocation systems which limit and prevent the use of location information of mobile users for network management purposes. First, the triangulation measurement data collecting procedure is quite complicated. At least two or three BSs should be involved in locating the position of a mobile user. Each BS will perform some triangulation measurement. Then, all this data has to be sent to some device to carry out the calculation. This procedure requires some specialized protocol to support the information exchange. This also would waste some

bandwidth in data transmission and introduce some delay to the overall location algorithm. Nowadays, for some practical systems, it may take several seconds to locate a mobile user. So, if the user moves with high speed, we cannot obtain an accurate position information in real time. Furthermore, performing the calculation at BS instead of MS raises some difficulties in obtaining accurate radio parameters of an MS that may vary with time and position. Finally, if all the mobile users in the network need geolocation information, the computational load at one BS becomes very heavy. Such an overload condition may lead to high processing delays or failures of the location module in the BSs.

By moving the measurement and calculation functionality to the mobile station, all the information needed for the geolocation can be gathered almost at one time and then be treated locally. In order to allow the geolocation system to have more flexibility so that the MS can smoothly roam among different networks with different geolocation approaches, a mobile agent-based system framework can be used. The use of an approach based on code-on-demand increases the flexibility of the system and maintains agents simple and small at the same time. This is an element of a broader concept of active networks that will be discussed later in Chapter 16. In this section, one of the basic ideas introduced is that, when the MS is turned on or roaming into a network with different geolocation approach, it sends a message agent (MA) to the BS. Then, the BS will generate a geolocation agent (GA) and send it to the mobile station. The GA should contain the signal processing algorithm for signal measurement and the triangulation algorithm. By using this framework, mobile stations can obtain all the data for triangulation calculation almost at the same time and carry out the geolocation calculation locally.

12.3 MOBILE AGENT-BASED RESOURCE MANAGEMENT

In general, a major incentive for an agent-based approach is that policies can be implemented dynamically, allowing for a resource-state-based call admission, reservation and network management strategy. Agents are used to discover resources available inside the network and claim resources on behalf of customers according to some ‘figures of merit’ [43], which represent tradeoffs between bandwidth claimed and loss risk incurred due to high utilization. Agents are able to trigger adaptation of applications inside the network on behalf of customers. This allows for an immediate response to resource shortages, decreases the amount of useless data transported, and reduces signaling overhead. Mobile agents provide the highest possible degree of flexibility and can carry application-specific knowledge into the network to locations where it is needed. In this section, we provide a detailed description of the mobility-assisted handoff/bandwidth reservation/call admission control scheme which can provide a flexible QoS management strategy in wireless networks. We also present a mobile agent-based framework and demonstrate its use in the implementation of the integrated strategy.

The agents used in the system and their corresponding roles are as follows:

Message agents (MAs) are used to exchange information and management data among agents and managers. They are created and received by message managers (MMs). MM can also forward messages contained by MAs to the corresponding managers or agents in the same network element.

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Geolocation agents (GAs) are created by geolocation managers (GM) in base stations and are sent to mobile stations. They contain the signal processing algorithm for signal measurement and the triangulation algorithm used in the current networks.

Bandwidth reservation agents (BRAs) are created by bandwidth reservation managers (BRMs) and are sent to mobile stations. They contain the bandwidth reservation algorithm used in the current networks.

Call admission control agents (CACAs) are created by call admission control managers (CACMs) embedded in base stations and are sent to mobile stations. The CAC strategy can be flexibly deployed in this way. CACAs will collect customer requirement and send necessary information by MAs to CACMs. The CAC algorithm is carried by CACMs.

When a mobile station is turned on, an MA is sent out to the base station containing initial information about the mobile station. MM in the base station relays the information in MA to CACM, GM and BRM. CACA will create a new CACA and send it back to the mobile station. CACA will collect user information about the QoS requirement and send it with call-in requirement back to CACM by MA. The CAC algorithm embedded in CACM will make the decision of accepting the new call or not. Once the new call is accepted, GM and BRM also will create a new GA and a new BRA, respectively, and send them back to the mobile station. Within the mobile station, GA can output the position of mobile station itself periodically and this position information is input into the position information processing module. This module then provides mobility information about mobile stations to BRA. BRA can use mobility information to calculate the bandwidth required for handoff purposes. The bandwidth reservation is also sent to the BS by MA. Finally, BRM at base station will summarize the bandwidth needed by each of its neighboring cells and send each neighboring cell an MA containing the corresponding bandwidth reservation requirements. BRM at base station in the neighboring cell can use this result to perform the bandwidth reservation process. In the case of handoff, a handoff requirement and also the QoS requirement will be sent by MA to the CACM embedded in the target base station. The pool used by bandwidth reconfiguration procedure is maintained by CACM. After bandwidth reallocation, CACM will send out message agents to MSs informing them about the reconfiguration results.

For the mobility predictive resource management scheme in a centralized management architecture, the following two modules are required: geolocation module and bandwidth reservation computation module. In CS (traditional centralized client–server) approach, the management primitives are often low level and fixed, and no semantic compression is allowed to be performed. As a result, these two modules work independently. The only entity that these two module can operate with is the management information base (MIB) embedded in the mobile station. The geolocation module periodically updates the position information in MIB, while the bandwidth reservation module will access the MIB for position information to carry out the prediction and calculate the bandwidth required by the mobile devices under consideration. In the mobile agent based approach, the agent has the ability to perform the semantic compression. This means that all the simple steps that should be processed one by one in CS mode now can be integrated into some agent that can carry out a more complicated task autonomously. The agents move to the mobile station and carry out the functionality of geolocation, bandwidth prediction locally. As a result, we should expect that the interactive traffic between mobile station and network management station can be reduced dramatically.

The various steps involved in the centralized management approach and the corresponding control messages involved are summarized as follows:

(1)Geolocation – whenever a mobile station needs to update its position information in the MIB, it sends out a request Imn to the corresponding NMS (network management station), which in turn sends out requests Inb to β different base stations near the mobile station (the number of β depends on the geolocation technology). After each one of these base stations obtains the corresponding measurement results, they return the measurements Rbn to the NMS. The NMS then uses the results to perform the triangulation computation and estimate the position of the mobile station. Finally,

the position result Rnm is returned to the mobile station for MIB update. The traffic generated in this phase is: Cgeo = Imn + β Inb + β Rbn + Rnm .

(2)Position prediction and bandwidth reservation calculation – when the NMS wants to predict the mobility of each mobile station, the current position and a few historical positions of MS are required. Since in the CS approach no semantic compression is allowed to be performed, every request for the position information (current or historical) of mobile stations will generate an inquiry Inm to mobile station to search its MIB. The mobile station then sends back the results Rmn one by one to the NMS. Finally, the NMS can use this information to do the prediction and calculate the bandwidth needing to be reserved in neighboring cells.

So in this phase, the traffic generated by CS mode management is: Cpos = α Inm + α Rmn , where α is the total new updates (recent positions and current position in the MIB of mobile station) required for prediction. Please note here that the prediction and reservation interval maybe different from the position calculation/update interval and, as a result, its is possible that some recent positions (stored in the MIB of the mobile node) along with the current one may have to be sent to the NMS as required by the bandwidth reservation algorithm.

Assuming that the average call life time is Tl and the geolocation/prediction update interval is tu, then in the CS mode the total control traffic generated for the resource management is:

C(cs) = Tl (Cgeo + Cpos)

tu

=Tl (Imn + 3Inb + 3Rbn + Rnm + α Inm + α Rnm )

tu

Similarly, the various steps involved in the mobile agent-based approach and the corresponding control messages involved can be summarized as follows:

Initial phase – agent(s) transport. When a mobile station is initialized, or whenever the mobile station enters a new network domain that supports different geolocation method or bandwidth calculation/reservation method, the resource management agents (including geolocation agent Ageo, bandwidth reservation agent Abwrsv and call admission control agent Acac) will be downloaded from the NMS. The total traffic introduced in this initial phase is equal to: Ageo + Abwrsv + Acac.

Phase 1 – geolocation phase. Since the geolocation module is included in the agent and is executed locally, no traffic is generated in the network. The geolocation results can be written into the MIB locally.

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Phase 2 – prediction phase. Since the prediction module has already been included in the agent code, the prediction can be made locally without generating any traffic in the network.

Phase 3 – bandwidth reservation. The bandwidth reservation agent calculates the bandwidth needed to be reserved in a certain cell and sends the reservation result Rmb via the message agents to BS.

Therefore, after initialization phase of agent download, the total communications control traffic generated for resource management purposes is only: C(ma) = Tl Rmb/tu. We should note here that, once the agents are embedded (downloaded) in the mobile station, unless a new resource management strategy is needed (when a management system is upgraded or the mobile stations travels to a new foreign domain), we do not need to transport the code of the agent again. Since all the involved agents are, in general, very small in size (up to a few kbytes), except for the graphical interface which, however, can vary from implementation to implementation and in any case does not need to migrate, it is expected that the migration times (e.g. message agents) and corresponding consumed energy are very low as well. Based on the above discussion and arguing that the size of Rmb is of the same order of message Rmn , we can easily conclude that, after the initialization phase, the control traffic generated between the mobile stations and base stations is reduced significantly by taking advantage of the mobile agent technology.

Security is another crucial aspect of agent systems. An adequate security model is required in order to insure a high level of protection to the agents and the nodes, so that nodes can be protected from the attacks of unsafe agents, and the agents can run on the nodes of the network without being damaged [44]. These issues are discussed in Chapter 15.

12.3.1 Advanced resource management system

In this section we discuss performance of an advanced resource management system (arm) using:

(1)The predictive mobility-based bandwidth reservation scheme (PMBBR), as described in Chapter 11; and

(2)QoS management with flexible bandwidth sharing (FBS).

For the system with two classes of traffic, FBS operates as follows:

Class 1 (higher priority) – the desired bandwidth for this kind of traffic is BW1u. If class 1 traffic cannot obtain the desired bandwidth, it may have the option to continue at a lower bandwidth requirement BW1l . For instance, this can be achieved by adjusting the coding rate so that the video/audio quality is still acceptable (i.e., real-time traffic).

Class 2 (lower priority) – the desired bandwidth for this kind of traffic is BW2u and there are no strict QoS requirements. However, some flexible QoS requirements are defined for such a service. The user could specify a set of acceptable QoS levels that correspond to bandwidth requirements that range from a lower bound bandwidth requirement BW2l to a maximum bandwidth requirement BW2u and expect a QoS varying in the specified range (i.e. nonreal-time traffic).

Such an arm system is compared with the corresponding results of a conventional system where the fixed bandwidth reservation is implemented, in terms of achievable new call (Pnb) and handoff Phb call blocking probabilities. Instead of two or more classes of users, the

2 users, respectively. The conventional system uses the fixed bandwidth reservation, where the reservation value represents a fixed percentage of the total capacity of the cell. In the following study, the corresponding reservation value of the conventional system for class 1 users is BWres1 = 30 kps and for class 2 users is BWres2 = 50 kps. The reservation values are selected based on experimentation with the objective of keeping similar handoff blocking probability for both the arm system and the conventional system. The call admission control procedure for a conventional system is the same except that the bandwidth reconfiguration is not used. From the figure, we observe that, for the given parameters, the arm system and the conventional system have similar handoff call blocking probabilities.

However, the arm system can significantly decrease the new call blocking probabilities for both user classes, which demonstrates that it can admit more users than the conventional system while still guaranteeing the same level of QoS for handoff calls. One should bear in mind that the connection level QoS improvement achieved by the arm system is obtained at the cost of slightly decreasing the bandwidth actually used by class 2 users. Table 12.3 lists the average used bandwidth by each class 2 user.

Figure 12.8 presents the corresponding numerical results for TP2 where 50 % of the new calls belong to class 2 traffic. Under this traffic configuration, the arm system can achieve an even better performance improvement.

Figure 12.9 shows the corresponding blocking probabilities that can be achieved by the arm system for different mobility patterns (high vs low) under TP1. From the figure, we observe that the arm scheme is capable of achieving good performance even when the users move in the low-speed pattern. The arm scheme achieves very low handoff failure rates for both user classes, which is similar to the results obtained under the high-speed mobility pattern.

12.4 CDMA CELLULAR MULTIMEDIA WIRELESS NETWORKS

In cellular segments of wireless IP networks, there is a need for simple radio resource and teletraffic management solutions that allow operators to customize various network and user profiles, optimize the grade of service (GoS) while fulfilling the QoS requirements. In such solutions, CAC for real-time (RT) services and packet access control for nonreal-time (NRT) services play key roles. This section

(1)presents a comparative study of CAC policies in the uplink (UL) of WCDMA cellular networks used as segments of the wireless IP networks;

(2)introduces a simple, effective and robust soft-decision CAC policy that exploits the UL interference distributions to compensate fluctuations of the local average SIRs dominating QoS while expanding call-admissible region to maximize the system capacity; the method requires neither complex measurements nor mutual exchange of information between adjacent CDMA cells about the state of the network; this is therefore especially applicable in wireless IP networks, consisting of number of