TELE4652 Mobile and Satellite Communication Systems

Lecture 2 – Cellular Concepts In this chapter we’ll address the fundamental ideas of cellular network design, channel assignment, power control, and the determination of mobile phone network capacity. The cellular concept, as we touched on in the previous chapter, means designing the network to consist of many low power base stations, each providing radio coverage over a small area, called the cell. The attractions of this are twofold; firstly, it allows the mobile stations to be low power, small and portable, hence mobile; and secondly, it allows frequency re-use and this massively increases system capacity. Thanks to the low power transmission of both the base stations and the mobile stations, we can re-use the same channel in a different geographical cell without having to worry about interference from co-channel users. There are two competing factors in establishing the capacity of a cellular network. Our ultimate aim to increase network capacity requires us to try to re-use each channel, and to simplify discussion for the moment we’ll consider a channel to be a distinct frequency channel in a FDMA system like AMPS, as often and in many cells as possible. The more we use the channel in the network, the more users we can simultaneously accommodate and the greater our capacity. However, the performance of our system is limited by co-channel interference – we cannot re-use the same channel in two neighbouring cells, as the likelihood of interference would be too great to maintain network quality. Thus, our aim is to assign channels to cells in such a way that a channel is re-used as often as possible while maximising the separation between cells using the same channel, to keep interference to a minimum and maintain good system performance. One should also make the point here of the importance of power management for both Mobile Stations and the Base Transceiver Stations. A balance must always be sought between using a high power level to obtain a strong RF link, and the need to lower power level to mitigate co-channel and adjacent channel interference. The solution in most mobile networks is to use feedback power control to ensure that MS are transmitting at the lowest power level needed to maintain a sufficient call quality. We will see later when we study the principles of CDMA in detail that power control is of even greater importance in this network, to overcome the ‘near-far effect’. The final topic in this section is that of handoffs, which are a natural consequence of giving users mobility. We’ll have a look at the different methodologies and techniques for implementing a handoff, such as how to establish when a handoff is advantageous, and who and how in the network can initiate the handoff.

Our first port of call will be to address how the network operator can assign channels to the network in such a way to maximise capacity and system performance. To assign channels to cells we first need a model for the cellular structure of our network.

Cellular Models The important geographic unit in a mobile network is the cell, which is the radio coverage area of each base station. The boundary of the cell would be defined to be where the received power level of either the BTS transmitting to the MS or the MS transmitting to the BTS (and the latter is usually the case, thanks to battery power limitations) drops below the deemed threshold required for successful communication. In an analogue network this threshold is a certain signal to noise ratio determined to result in a sufficiently clear and perceptible conversation, while in a digital system the threshold determined by some bit error rate such that a sufficient quantity of data is successfully recovered. Under perfect free-space conditions and omni-directional antennae the received power level would drop off as the inverse square of the distance from the BTS, and as such cells would be concentric circles with some radius, always centred at the base station. However the RF environment surrounding a base station can never be considered free space, and reflections, obstructions, diffractions, and scattering around obstacles such as buildings, mountains, etc, mean in practise that cells resemble nothing like circles in shape. Moreover real coverage areas and cell geometry must be determined by in the field measurements of received power levels. Nevertheless in a later chapter we’ll look at how the properties of electromagnetic wave propagation (reflections, diffraction, scattering) can be used to obtain models for power loss, and how environmental factors can be accounted for in these models. For the moment, though, we’ll limit ourselves to a very simple model for cell geometry. The factors that determine cell shape are: the power level used by the base station, and permitted by the MS; the type of antenna (or antennae) used at the BTS, as to whether it is omni-directional, or perhaps sectored; and the radio propagation environment (buildings, mountains, atmospheric conditions, geological composition, etc). The network operator is ultimately the party that decides on the placement of base stations, and so defines the cells and coverage area of the network. There are several common methodologies that are adopted to fine tune the coverage area in a practical network. The first is the assignment of one or more ‘microcells’ within a ‘macrocell’. A standard cell could then be sub-divided into a series of smaller cells, where some of the channels are adopted into the smaller, or microcells, which then use much lower power base stations. These microcells increase the level of frequency re-use, and hence the network capacity. Microcells are a common solution in high congestion areas, like major shopping malls, and for temporary sites like at major sporting events, when a greater capacity is needed only for a short period. Fixed microcells can be found along congested city streets and in large public buildings.

Typical parameters for macro and micro cells is shown in the table below.

Macrocell Microcell Cell radius 1 to 20 km 0.1 to 1 km Tx power 1 to 10 W 0.1 to 1 W

Delay spread 0.1 to 10 µs 10 to 100 ns Maximum bit rate 0.3 Mbps 1 Mbps

We will consider the physical quantity, ‘average delay spread’, and how it determines the maximum achievable bit rate, in the chapter on small-scale propagation effects and radio channel modelling. Another, albeit similar solution, is to use umbrella cells. An umbrella cells is a larger cell, operating with a high-powered antenna that acts like an umbrella over an area covered by a standard cell arrangement. Umbrella cells are commonly used in areas with freeways, where high-speed traffic will rapidly change cells requiring many handoffs. In most networks handoffs require a fair amount of signalling and network resources, and as such minimising the occurrence of handoffs in the network is highly desirable. As indicated in the diagram below, umbrella cells would usually be implemented with directional antennae, to provide the most efficient coverage of the rapidly moving traffic.

An alternative cell geometry involves the use of directional antennae and sectored geometry. Such choices would be driven by the availability and cost of base station RF hardware and the local RF environment, as to whether it is more efficient to employ omni-directional antenna with full 360 degree coverage, or if directional antenna with the antenna’s sensitivity focused into a 120 degree sector is preferred. Of course a particular operator would often employ both omni-directional cells and sectored cells in the same network, as demand dictates.

The above discussion indicates the wide diversity of choices available to network operators in the design and application of cells throughout the mobile network, and in a later chapter we will examine the factors of the RF environment that determine the radio coverage area of a cell. However, to introduce the concepts of network planning and frequency assignment we seek a simple model for the network cell geometry. The most common solution is to approximate cells as identical regular hexagons, for no other reason than hexagons are an efficient and simple shape to cover a two-dimensional area.

We then aim to assign our available channels to cells, approximated as same sized hexagons, in such a way to maximise the number of simultaneous users we can accommodate on the network while minimising the adverse effects of co-channel

interference. To maximising capacity we aim to re-use channels as often as possible through the network, while the co-channel interference issue demands that we separate co-channel cells by as large a distance as possible. A very simple model to achieve this frequency assignment is to break our cells into same sized groups called a ‘cluster’, assign all of our available channels to cells within this cluster, and then replicate this cluster throughout the network. An example of this cluster channel assignment is shown in the diagram below for a cluster size of N = 4, 7, and 19.

To replicate this cluster assignment in a uniform way, as constrained by our hexagonal cell geometry, only certain cluster sizes are possible. It is easy to see that the number of cells in a cluster must satisfy

22 jijiN ++= for a pair of integers ( )ji, . This represents the number of cells one moves to find the nearest co-channel cell – cell using the same set of frequencies. One chooses a direction representing one of the six axes of the hexagon, moves i cells along that axis, turns 120 degrees right, then moves j cells along this new axis to the nearest co-channel cell. Maximising network capacity aims to make N as small as possible, thereby the same channel will be used in more cells throughout the network. A common performance metric for the cell assignment is the co-channel re-use ratio, which is the ratio of the distance between the nearest co-channel cell D and the cell radius R. It is shown in the tutorials that this is given by

NRDQ 3==

The smaller this value the greater the network capacity. To determine the network capacity for a given cluster size we will need trunking theory, which is the topic of the next section. For the present we will estimate the signal to interference ratio. We can divide the internal network interference, as in interference resulting from other users in the network, as opposed to external interference and noise, into two different types: co-channel interference and adjacent channel interference. The latter refers to interference resulting from users operating on neighbouring channels, since the non-ideal channel filters used mean there will be some leakage of signal power onto our reference user’s recovered signal. Adjacent channel interference is mitigated through power control and frequency assignment. Power control ensures that no signal mobile station is transmitting an excessive power level sufficient to swamp the signals received from other mobiles. Frequency assignment can play a role in assuring that neighbouring frequency channels are not used in the same cell, and where possible, are maximally separated throughout the network. Co-channel interference refers to interference resulting from other users in different cells in the network using the same channel as our representative user. This factor is ultimately determined by the distance to and number of nearest co-channel cells, and hence our cluster size. It is important to note that these two network interference quantities cannot be reduced by increasing the overall power level, unlike other types of interference and noise – increasing the power level naturally increases the interference power by the same amount. We can define the signal to interference power ratio as

∑=

=0

1

i

iiI

SIS

where S is the signal power level of the desired MS-BTS link, and iI is the power level received from the 0i neighbouring base stations using the same channels. In the hexagonal cell geometry we find that 60 =i . These received signal power levels are determined by how the RF power decays with distance, a relationship that we term the path-loss model. Students will be familiar with the inverse-square law for the path-loss of electromagnetic radiation in free space, for which the power level received will decay as the inverse square of the distance between the transmitter and the receiver. We could express this relationship as

( ) ( )2

00

−

⎟⎟⎠

⎞⎜⎜⎝

⎛=

dddPdP rr

where ( )dPr is the power level received at a distance d from the transmitter, as compared to the power level received at a reference distance 0d , ( )0dPr . In practise we find that the RF environment of cells is not well approximated as free space, and the path-loss can be modelled with the following relationship,

( ) ( )n

rr dddPdP

−

⎟⎟⎠

⎞⎜⎜⎝

⎛=

00

where the path loss exponent n is a parameter determined by the local RF environment of the cell. In a later chapter we will study the factors that determine this path-loss exponent, though typically it is a quantity that is measured in the field. Examples of path-loss exponents for different environments are listed in the table below.

Environment Exponent In building LOS(line of sight) 1.6-1.8

Free space 2 Typical urban 2.7-3.5 Harsh urban 3-5

In building (obstruction) 4-6 It is common to work in decibels in communications, for which the relationship becomes

( )[ ] ( )[ ] ⎟⎟⎠

⎞⎜⎜⎝

⎛−=

0100 log10

ddndBmdPdBmdP rr

The worst case signal to interference ratio occurs when the MS is at the boundary of the cell, a distance of the cell radius R from the BTS. Assuming all co-channel base stations are a distance D away, we can then express the signal to interference ratio as

( ) ( )000

3iN

iRD

DiR

IS

nn

n

n

=== −

−

Hence, we see that the signal to interference is reduced by increasing the cluster size. This presents us with the natural, intuitive trade-off: reducing the co-channel re-use ratio will increase the capacity of the network, at the cost of greater interference and a degraded performance level. There are a number of ways that a network operator can seek to increase the capacity of their network. The most obvious solution is to add more channels, though this will typically require the purchase of more spectrum. An alternative is to dynamically re-allocate the available channels on demand, to assign more channels to cells experiencing high traffic loads. This seeks to dynamically ensure that the capacity is available at the locations of the network at currently need it, as in practise one finds that traffic is not uniformly distributed over the network. Cell splitting is an obvious way to increase capacity, since by reducing the size of the cells the same channel will be used more often throughout the same geographical area. Reducing the cell radius by a factor f increase the number of base stations required by a factor 2f , with an equivalent increase in network capacity. Signal power, noise and interference levels mean that the cell size can at a minimum be of the order of 1.5 km (microcells excluded) in practise. The other disadvantage in cell splitting, reducing the size of cells, is the increase need for handoffs.

Before we turn our attention to trunking theory, let’s just clearly state the assumptions that underlie our simplistic model of cell network structure, and in that regard how these factors are very different in a real network. In a real mobile phone network, the cells will not be of the same size; we do not need to have the same number of channels in each cell (moreover, we can dynamically alter the channel assignment); cells will not have the same number of neighbours; we can have sectored cells, umbrella cells, and microcells; and the capacity and demand will not be uniformly distributed throughout the network. These points illustrate how this question of channel assignment and cellular network design is a very complicated one in reality. Nevertheless our simple model will enable to see the basic, underling ideas of how this problem is solved in practical, existing networks.

Network Capacity In a mobile communication system we have a large number of uncoordinated users sharing a network. The question we would like to answer is, given a certain number of available channels, how many subscribers can we accommodate at our designated quality of service? It is apparent that if we have 100 available channels we can support many more than 100 subscribers, since the typical user will not require network resources all of the time. Trunking theory, also known as Traffic theory, addresses exactly this problem. Traffic theory was first developed in the design of early telephone switches and circuit-switched telephone networks, but exactly the same principles can be applied to a mobile phone network, as they can to many other systems, such as the internet. The basic problem is this: given a system with N available channels, how many users L can the system accommodate. We would naturally seek that NL > , which means that there is a certain probability that when a user attempts to make a call and hence access one of our N channels, all of these channels could already be in use so this call is blocked. There are two options for how to handle this call blocking: either the call can be refused, or alternatively it could be queued. The important performance parameters to determine then are: what is the probability that an attempted call will be blocked, and if queuing is used on blocked calls, what is the average queuing delay? The probability that an attempted call is blocked is known as the Grade of Service (GoS) for the cellular network, as is something that is usually determined by the service operator. As good engineering practise it usual to design the network based on the typical maximum rate of expected traffic. The ITU recommends determining the network capacity and grade of service using the ‘mean busy-hour traffic’, which is the average of the busy hour traffic on the 30-busiest days of the year, where the ‘busy hour’ is defined to be the 60 minute period during the day when the traffic level is the highest. The two different methodologies for handling blocked calls are known as Lost Calls Delayed (LCD), if blocked calls are put in a queue to await a free channel; or Lost Calls Cleared (LCC), if the call is refused and dropped. For the later case it is usually assumed that the user will then wait a random amount of time before attempting the

call again. Most mobile networks employ the LCC strategy, as most students are probably aware from personal experience. The procedure of traffic theory is, given a certain number of channels and an amount of traffic presented to the network, determine the probability that a call is dropped. To do this several assumptions must be made, and depending on the choice of these assumptions, different formulae will be obtained. These assumptions are:

- How blocked calls are dealt with. In mobile networks we’ll normally assume the LCC model.

- How long users will occupy a channel on the network. - How channel assignment is performed. Can any user access any channel if

it is available? (In mobile networks this is not the case, as some channels are assigned purely for control and others purely for data traffic).

- How often users attempt to access channels on the network. - The number of sources.

Given the randomness of the users on a mobile network it is common to build statistical models for each of these quantities, and good statistical models require good data collection – an important role for any competent network operator. The simplest model for this type of network access, and yet practically useful model, results in what is called the Erlang B formula. This formula makes the following assumptions:

- LCC, so blocked calls are rejected and dropped. - Any user can access any available channel. - The average call duration follows an exponential distribution. The

probability that a call will last for duration t is

( ) HteH

tP −=1

where H is the mean call duration. - The probability of users attempting a call is given by a Poisson

distribution. The probability of n users attempting calls within the time interval τ is given by

( ) ( ) λτλττ −= en

nPn

!,

where λ is known as the mean arrival rate per user, essentially the average number of calls made per hour by the average user.

- Infinite sources. This isn’t too bad an assumption if there are many more subscribers than available channels, which is typically the case.

An important point is how do we measure and quantify traffic? The usual measure of traffic is ‘traffic intensity’, measured in a dimensionless quantity called the ‘Erlang’. The Erlang represents the fraction of channel occupancy – 1 Erlang is the amount of traffic needed to keep a given channel completely occupied for a period of time (usually an hour). For example, a traffic intensity of 0.75 Erlang would keep a channel occupied for on average 45 minutes out of an hour. It is easy to see that the average traffic generated by a user in Erlangs is given by

HA λ=

where λ is the average number of calls made per hour and H is the mean holding time of a call, measured in hours. For instance, if users make on average 2 call per hour and each call is on average 3 minutes in duration, this would present the network with a traffic intensity of 2*3/60 = 0.1 Erlang, since we would expect that this traffic would occupy a channel for 6 minutes out of every hour. As will be shown in the lecture overheads, the above assumptions on the call probability distributions for arrival rate and duration can be used to derive the following formula for the probability that a call is blocked by the network, given a traffic level of A and the total number of channels C;

( )∑=

=C

k

k

c

kA

CA

P

0 !

!block

This is known as the Erlang B formula. It is sufficiently complex that it usually tabulated, and a table of values of the Erlang B relationship is shown over the page. Other assumptions lead to slightly different formulae, though the Erlang B relationship will suffice us in this course. For example, a cell with 20 channels when subjected to a traffic intensity of 11.1 Erlangs would result in a blocking probability of 0.005. This means that on average 5 out of every 1000 calls would be blocked. This is not too surprising, since on average each channel will be occupied for 55.5% of the time. As already stated, the blocking probability is something that is ultimately determined by the network operator, based in part on customer expectations and marketing. In GSM the traffic intensity is something that is continually measured and monitored by the Operations and Maintenance Centre (OMC), the data of which can be used to perform dynamic channel assignment. For example, if it is desired that only 2 out of every 1000 calls are to be blocked, and the traffic intensity in the cell is measured to be 25 Erlangs, then at least 20 channels are needed in this cell. The general idea is that, as cell cluster size N is reduced, producing greater frequency re-use, there is on average more channels available per cell. By the Erlang B formula, this would facilitate a greater amount of traffic within the cells, for a specified grade of service. This greater allowed traffic in each cell the greater the number of users that can be accommodated. The procedure for network capacity estimation is then as follows: the level of signal power to co-channel interference power determines the minimum allowed cluster size. This cluster size then establishes the network capacity, in the form of the number of users that can be supported, using Erlang B ideas, depending on the number of channels per cell and the average user traffic intensity. Examples of this will be given in the lecture overheads and in the tutorials. A popular alternative to the Erlang B is the Erlang C, which follows the same assumptions however performs the analysis for LDC, allowing block calls to be held until channels become available. Under this scheme it can be shown that the probability that a call is delayed is

( )∑=

⎟⎠⎞

⎜⎝⎛ −+

=C

k

kC

C

kA

CACA

AP

0 !1!

delayed

The average delay experienced by a call attempt to the network is then

( )AC

HPD−

= delayed

Handoff Issues A handoff, or handover, is the procedure of changing the channel assignment of a mobile unit from one BTS to another as the mobile moves between cells. It is an important and unique characteristic of a mobile network, and we discuss the important issues involving handoffs. There are many different ways that handoffs can be handled, and it depends on many factors. The important considerations in performing a handoff are: - they must be infrequent and transparent to users. - calls should not be lost or dropped in a handover. - the network must decide on the conditions for instigating a handoff. The first point is to distinguish between what are known as ‘hard handoffs’, when the handoff requires a change in channel, and ‘soft handoffs’, when the channel does not need to change, only the base station handling the channel changes. Hard handoffs are implied in the cell planned networks we have been discussing in the previous sections, since neighbouring cells by necessity use different channels. In dynamically assigned systems it is conceivable that the MSC would be able to move the channel into the neighbouring cell with the mobile, though this is rarely performed in practise. Soft handoffs are really only relevant when we begin discussing CDMA and 3G networks, in which frequency assignment is much less of a significant factor. In these networks the channel is defined as a spreading code, which is easy to move into a different cell as the mobile station moves, and it is even possible to have multiple base stations handling a call simultaneously in such systems. A handoff can be initiated by the network alone, based on measurements of the received signal strength from the mobile, or it can be ‘mobile assisted’ (MAHO), in which the mobile partakes in handover decision by providing feedback to the network. MAHO is the common strategy in later networks, and the term was first coined in GSM. This is fairly easy to implement since each mobile station can easily measure the strength it receives from neighbouring base channels, and relay that information to the network. This particularly true in TDMA networks, as this monitoring can be done efficiently during idle timeslots with no cost in performance quality. The decision to perform a handoff is based principally on received power measurements. There are several other factors that can be used in the decision-making as to whether to perform a handover:

- Cell blocking probability. The probability that a new call will be dropped due to the change in load. In this case the decision to perform a handoff is based on traffic capacity, rather than signal quality.

- Call dropping probability. The probability that, due to the handoff, a call is terminated.

- Call completion probability. The probability that a call is not dropped before it is terminated.

- Handoff blocking probability. The probability that the handoff is successfully completed.

- Handoff probability. The probability that a handoff occurs before call termination.

- Rate of handoffs. The number of handoffs per unit time. - Interruption duration. The time during which a mobile will be unable to

connect to either base station. - Handoff delay. The distance the mobile station moves from the point at

which the handoff should occur to the point at which it does occur. As we will further discuss in the following section, the MS and BTS regularly measure the received power level, and in most networks this information is fed back to the network by the mobile station on a control channel to assist the network in its decision making. A common technique is to measure the power level received on the base channel for each base station, making this measurement over a moving window of time to remove the rapid fluctuations in signal level due to multipath effects. In GSM this measurement is called the Radio Signal Strength Indicator (RSSI). There are several ways this RSSI can be used to initiate a handoff. These techniques will be illustrated with the aid of the diagram below, which shows the average power level received from a pair of neighbouring base stations as the mobile station moves along a line connecting the two base stations.

The most obvious technique is to perform the handoff at point 1L , when the RSSI from B is greater than the signal level received from serving base station A. This corresponds to the handoff philosophy to handoff as soon as the power level received from another base station is greater than the current serving base station. This technique is not popular, though, as rapid variations in signal level due to multipath effects, channel irregularities, and movements can result in a ‘ping-pong’ effect, where the MS will be handed back and forth between two base stations. Handovers require a fair level of signalling and system resources, so minimising their occurrence is usually preferable. In GSM, for instance, a handoff can take up to 10 seconds to perform. More common is to introduce a threshold scheme into the decision to perform a handoff. In this case a handoff will only be performed if RSSI drops below a certain

threshold, and if there is another base station for which the RSSI is stronger. The intention is that so long as the signal at the current base station is adequate, a handoff is unnecessary. If the threshold level is 2Th in the diagram above, then the handoff would occur at point 2L . Note that this methodology can result in MS being in cells but currently being serviced by other base stations than the base station that defines it current geographical cell. This is so as to minimise the impact of the signalling required at handoffs, though this can have detrimental impacts on network interference levels. Another idea is to introduce a hysteresis factor. A handoff is initiated if the RSSI from a neighbouring base station is greater than the current by an amount H. In the diagram above the handoff in this case would occur at point 3L . This prevents the afore mentioned ping-pong effect. Any combination of the above mentioned strategies could be implemented to decide on the need to perform a handoff. Even some newer proposals involve using predictive algorithms to estimate future received signal strengths. The important points to emphasise, though, is that the need to perform a handoff is a balance between maintaining a good radio performance and maintaining network capacity, in minimising the demands of excessive signalling to perform a handoff, an acceptable distribution of load, and interference power levels. In many network realisations some channels will be kept free to facilitate handovers and make sure no calls are lost in handoffs. It is unanimously agreed that it is less desirable to drop a call that is currently being serviced than it is to block a new attempt at a call.

Power Control Dynamic power control is always a central issue in every mobile phone network. A balance must be achieved between the need to keep the mobile station power level high above the background noise level to make signal detection easy and with a low error rate, while, on the other hand, the power level should be minimised to reduce the level of co-channel and adjacent channel interference. The general philosophy is to try to have each mobile transmitting at the minimum power level needed to obtain an acceptable call quality. Particularly in the evolution to 3G networks and the need to transport different types of data with different error demands, exactly what this minimum level is depends on the type of data that is being transmitted. The other advantage in having the MS minimise its transmitted power is that it maximises battery life. Naturally this means that it is desirable that mobiles physically close to the base station and with a good RF link transmit with a low signal power, while those MS far away from the BTS transmit with large powers. This demands some form of variable power control, for which the MS can quickly change its power level as the condition demands. There are two methodologies that can be adopted: either open-loop power control or closed loop power control. Open-loop power control involves only the MS itself. In essence the mobile can monitor the received signal strength from the base channel of the serving base station,

and adjust its transmitted power level accordingly. This scheme works well as long as the signal strengths of the forward and reverse channels are highly correlated, which is usually the case. The advantage of open-loop power control is that it can adapt more quickly to changes in the received power level, as it does not need to wait the round trip time for feedback from the BTS. For this reason open-loop power control is popular in Spread Spectrum mobile phone systems, such as CDMA. Power control is very, very important in CDMA to combat something known as the near-far effect. The near-far effect refers to the signal from a mobile station close to the base station swamping the signal of a mobile station far away. This is a particular problem in CDMA since all users share and co-exist in the same RF spectrum. Each user is distinguished by a unique spreading code, and the signal from another user adds to the background noise level experienced by the other users on the system. If the signal level from one user is much greater than the others, then this background noise level will be too high and prevent the signals of other users being correctly recovered. The aim in a CDMA system is to ensure that the received power level is the same at the base station for all serving mobiles, to mitigate the near-far effect and provide a fair allocation of radio resources. It is important that any power adjustments occur very quickly, to compensate for any rapid shadowing events (like disrupting a line of sight situation by moving behind a building). A Closed Loop Power Control system adjusts the signal strength in the reverse (mobile to BTS) channel based on some metric of performance in that reverse channel, like the received power level, bit error rate, or received signal to noise ratio. The BTS makes the power adjustment decision and communicates a power adjustment command to the MS on a forward control channel. It also possible to have closed power control operating on the forward channel power, in which case the BTS is adjusting its power level based on RSSI of serving mobiles. Closed-loop power control is more accurate but slower to react to changes in the channel. It was the solution adopted in GSM. The GSM standard specifies a set of power classes, and RSSI measurements can direct both the BTS and the MS to change its operating power class. The following table lists these power classes:

Power Class Base Station Power (W) Mobile Station Power (W)

1 320 20 2 160 8 3 80 5 4 40 2 5 20 0.8 6 10 7 5 8 2.5