Advanced Wireless Networks - 4G Technologies

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Figure 17.13 Cells with reuse factors of 2, 3 and 4.

B3dB = 60◦, while in Figure 17.13(c) B3dB = 30◦. In Figure 17.13(b), better-quality antennas are considered, which would achieve the same sidelobe rejection at 1.5 B3dB, driving frequency reuse higher for the same number of sectors.

17.4.2 Alternating polarization

Figure 17.14(a) shows how a high reuse factor of 6 can be achieved in 12 sectors by alternating the polarity in sectors. The lines’ orientations show the polarization, horizontal

(H) or vertical (V). The amount of discrimination that can be achieved depends on the environment. Although the antenna technology may provide for polarization discrimination of 30–40 dB, we shall consider that the combination of depolarization effects raises the cross-polarization level to p = −7 dB.

The interference is reduced, so the same frequency at the same polarization comes only in the fourth sector. The problem is that a deployment has to allow for gradual sectorization – start by deploying minimum equipment, then split into more sectors as demand grows. However, if alternating polarization is employed in sectors, the operator would have to visit the subscriber sites in order to reorient the antennas. A more conservative approach would be to set two large areas of different polarity, as in Figure 17.14(b). This does not reduce the close-in sidelobes but reduces overall interference and also helps in the multiplecell design.

(a)

FR = 4

(b)

FR = 6

Figure 17.14 Twelve-sector cells with cross-polarization.

Following is an approximation for the S/I :

where Si are the transmission powers in other sectors using the same subband, Ii the interferers, and NR the receiver input noise. Later the case will be considered where all transmission powers in sectors are equal to Si = S, and, NR is neglected. N1 and N2 are the number of sectors with the same polarization and the cross-polarized ones, respectively. As well, the worst case values α and p will be taken for the sidelobe gains and crosspolarization:

Thus, in a first approximation [24], by applying Equation (17.24), the S/I level (or co-channel interference, CCI) at the subscriber receiver is given in Table 17.2. The type of modulation and the subsequent modem S/I specification have to be specification have to be

Table 17.2 The S/I level at the subscriber receiver

Figure 17.13(a) Figure 17.13(b) Figure 17.13(c) Figure 17.14(a) Figure 17.14(b)

The need to move away from this type of frequency planning has been expressed in the literature [28] as well as being emphasized by ETSI in the selection criteria for the UMTS air–interface technique. The main reason for this departure is the need for very small cell sizes in urban areas with highly varying morphology, making traditional frequency planning difficult. Another reason lies in the difficulty associated with the addition of new base stations to the network, which currently requires extensive reconfiguration. In view of these two arguments, a desirable solution would require the use of unconfigured base transceiver stations (BTSs) at all sites; these BTSs are installed without a predefined set of parameters and select their operating characteristics on the basis of information achieved from runtime data. For instance, they may operate at all the available carriers and select their operating frequencies to minimize mutual interference with other BTSs [28].

The increasing demand for data services means that the next generation of communication networks must be able to support a wide variety of services. The systems must be locationand situation-aware, and must take advantage of this information to dynamically configure themselves in a distributed fashion [29].

There will be no central control strategy in 4G, and all devices will be able to adapt to changes imposed by the environment. The devices are intelligent and clearly employ some form of self-organization [30, 31]. So far in our previous discussion, coverage and capacity have been the two most important factors in cellular planning. To have good coverage in both rural and urban environments is important so as to enable customers to use their terminals wherever they go. Coverage gaps mean loss of revenue and can also lead to customers moving to a different operator (which they believe is covering this area better). On the other hand, some areas may not be economically viable to cover from the operator’s point of view due to a low population density. Other locations, for instance a sports stadium, may only require coverage at certain times of the day or even week.

Capacity is equally important. Without adequate capacity, users will not be able to enter the network even though there might be suitable coverage in the area. Providing the correct capacity in the correct location is essential to minimize the amount of infrastructure, while ensuring a high utilization of the hardware that has actually been installed. Based on the traffic distribution over the duration of a day reported in Lam et al. [32], an average transceiver utilization of only 35 % has been estimated. Improving this utilization is therefore of great interest.

A flexible architecture is essential to enable the wide variety of services and terminals expected in 4G to co-exist seamlessly in the same bandwidth. In addition, future upgrades and reconfigurations should require minimum effort and cost. The initial investment, the running costs and the cost of future upgrades are expected to be the three most important components in determining the total cost of the system.

17.5.2 Networks self-organizing technologies

Capacity, both in terms of bandwidth and hardware, will always be limited in a practical communication system. When a cell becomes congested, different actions are possible. The cell could borrow resources, bandwidth or hardware, from a neighboring cell. It could also make a service handover request to a neighbor in order to minimize the congestion. Thirdly, a service handover request could be made to a cell in a layer above or below in the hierarchical cell structure. Finally, the cell could try to reduce the path loss to the mobile

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terminal to minimise the impact of other cell interference. If neighboring cells are unable to ‘assist’ the congested cell, the options left for the cell are to degrade the users’ service quality (if it is interference limited) or to try and influence the users’ behavior. This can be achieved through service pricing strategies. The pricing scheme can be regarded as a protection mechanism for the network. Since it cannot create capacity, only utilise what it already has, it needs to force the users to adapt their behavioral pattern until the network is upgraded or there is more capacity available. Self-organizing technologies fall into one of these categories.

17.5.2.1 Bunching of base stations

In a microand picocellular environment there will be severe fluctuations in traffic demand, user mobility and traffic types. This highly complex environment will require advanced radio resource management (RRM) algorithms and it will be beneficial to have a central intelligent unit that can maximize the resource utilization. The bunch concept has been proposed as a means to deal with this issue. It involves a central unit (CU) that controls a set of remote antennas or base stations (which have very little intelligence). The central units will deal with all decisions on channel allocation, service request and handover. Algorithms for layers 1 and 2 (such as power control) may be controlled by the remote unit itself. The bunch concept can be viewed simply as a very advanced base station with a number of small antennas for remote sensing. The central unit will therefore have complete control over all the traffic in its coverage area and will be able to maximize the resource utilization for the current traffic. This provides opportunities for uplink diversity and avoids intercell handovers in its coverage area. The bunch approach will typically be deployed in city centres, large buildings or even a single building floor.

17.5.2.2 Dynamic charging

The operators need to encourage users to utilize the network more efficiently, something that can be achieved through a well thought-out pricing strategy. Pricing becomes particularly important for data services such as e-mail and file transfer as these may require considerable resources but may not be time-critical. A large portion of e-mails (which are not timecritical) could, for example, be sent during off-peak hours, hence improving the resource utilization. In this area two main approaches are used, user utility method and maximum revenue method.

User utility algorithms assume that the user associates a value to each service level that can be obtained. The service level is often referred to as the user’s utility function and it can be interpreted as the amount the user is willing to pay for a given quality of service. It is assumed that the user acts ‘selfishly’, always trying to maximize their own utility (or service). The whole point with a pricing strategy is to enable the operator to predict how users will react to it, something which is not trivial. Current proposals do not try to determine the exact user’s utility function, but rather to postulate a utility function which is based on the characteristics of the application or service. Two prime examples are voice and data services, which exhibit very different characteristics. Although speech applications are very sensitive to time delays, they are relatively insensitive to data errors. Similarly, although data services are relatively insensitive to time delays, they are very sensitive to data errors.

M

M M

M M

M

M

M

Figure 17.16 An intelligent-relaying overlay.

The maximum revenue method suggested in Bharghavan et al. [36] is based on letting the network optimize its revenue by allocating resources to users in a manner which is beneficial for the network. The two main principles are to maximize the resources allocated to static flows and to minimize the variance in resources allocated to mobile flows. These rules are based on the assumption that, whereas a static user’s preference is for maximum data rate, mobile users are more concerned with the variance in service quality as they move from cell to cell. The actual revenue model is based on a 4-tuple, <A, T, Ca, F>, namely admission fee, termination credit, adaptation credit and revenue function. The network uses these parameters to optimize its revenue. Maximum revenue is calculated according to the following rules: (1) the flow pays an admission fee once it is granted its minimum requested resource allocation; (2) if a flow is prematurely terminated by the network, the latter pays the flow a termination credit; (3) the network pays the flow an adaptation credit if the resource allocation is changed during the transmission, regardless of whether the allocation is increased or reduced; (4) the flow pays a positive but decreasing marginal revenue for each extra unit granted by the network. The flow does not pay for resources allocated above its maximum requested resource allocation.

Intelligent relaying is a technique that can minimize the amount of planning and the number of base stations required in a cellular network. A network employing intelligent relaying includes mobiles that are capable of passing data directly to other mobiles, rather then directly to a base station, as for a conventional cellular network. In order to plan a network incorporating intelligent relaying, it is convenient to consider each mobile as a ‘virtual cell’, acting as a base station at its center. The coverage area of this virtual cell, as seen in Figure 17.16, can be varied according to the circumstances, as the mobile changes its transmit power and according to the mobility of the user. The mobile will set the radius of its virtual cell according to the number of other mobiles in the vicinity available to relay data; the size of the virtual cell will be minimized to improve frequency reuse.

Context awareness in 4G will, for example, be a scenario in which devices such as personal digital assistants (PDAs) should be able to communicate at short range with a number of other devices. These devices might be a fax machine, a computer, a mobile phone, a printer or a photocopier. Assuming all these devices have a low-power radio, then

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the PDA would be able to engage any of the devices. The task could be printing documents, downloading documents, faxing a message or uploading data for storage. To the user, the interdevice communication will be transparent. The user will only be concerned with the task they are performing, not the devices they are connected with. This sort of communication between electronic devices can be achieved through transmission of beacon signals, which provide relevant information such as device capability and identification. As manufacturers throughout the world are incorporating the Bluetooth technology into their products, this concept is about to materialize [41].

The context or situation awareness concept can also be exploited in cellular networks. In current cellular systems, base stations transmit information on their broadcast control channel, which can be used to implement this idea. Assuming this information includes I = [id, x lat, y long, Tx] (base station identity, position in latitude and longitude, broadcast control channel transmit power), then this would enable the network to reconfigure its base station coverage areas when a base station is removed or added to the network.

Dynamic cell sizing is another emerging technique that has received considerable interest in the literature [40]. By dynamically adjusting the coverage areas of the cells, optimum network performance can be achieved under any traffic conditions. When a single cell is heavily occupied, whilst the surrounding cells are lightly loaded, it is possible with this scheme to reduce the cell size of the loaded cell and to increase the size of the surrounding cells. In this manner, more users can be accommodated in the centre cell. This can be implemented in such a way that the base station controls its attachment area by increasing or decreasing its beacon transmit power, hence increasing or decreasing the area in which mobiles will connect to the base station. Under congestion the cell will contract such as to limit its service area and reduce the inter-base-station interference. This will enable it to serve more users closer to the base station. In the limit, the cell is so small that the interference contribution of neighboring base stations can be ignored and maximum capacity is achieved. In light traffic conditions it will expand and hence improve the network coverage with cells overlapping the same area. The cell to which the user connects will therefore be a function not only of the path loss between the base station and the user but also of the beacon transmit power.

Intelligent handover (IH) techniques can be made more intelligent, also considering parameters such as resource utilization. A fast-moving mobile user who is currently served by a cell may run into problems in the next cell because it is fully congested. An intelligent handover algorithm would be able to recognize this problem and try to solve it by handing the user to the microcellular layer. The user will stay in this layer until the blocked cell has been passed, upon which it will be handed back to the macro layer. Similarly, if there is no coverage on the user’s home network in the area in which they are moving, then the IH algorithm should seek to maintain the connection by handing over to a competitor’s network or a private network offering capacity to external users. Performance evaluation of the above techniques can be found in Spilling et al. [42].

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