WO2001072072A1

WO2001072072A1 - Method of cellular network planning and communication system therefor

Info

Publication number: WO2001072072A1
Application number: PCT/RU2000/000096
Authority: WO
Inventors: Evgeny Petrovich Vishnevsky; Alexandr Sergeevich Abikonov; Vladimir Grigorievich Maslov; Konstantin Jurievich Ushakov; Stanislav Evgenevich Vishnevsky
Original assignee: Motorola Inc.
Priority date: 2000-03-21
Filing date: 2000-03-21
Publication date: 2001-09-27

Abstract

The present invention relates to a method of communication network planning in a high population urban area, comprising the steps of forming (2) a plurality of cells (12) depending on grade-of-service requirements for each cell, allocating (3) a base station (7) in each of said cells (12), assigning (4) traffic channels to each of said cells (12), assigning (6) frequencies to each of said cells (12). In order to provide a more accurate planning of such networks it is suggested that the assigning (4) of traffic channels to said cells (12) is effected depending on the subscriber movements (14, 15, 16) and the mean channel holding time of each logged-in subscriber.

Description

METHOD OF CELLULAR NETWORK PLANNING AND COMMUNICATION SYSTEM THEREFOR

The present invention relates to cellular network planning and designing in a high population urban environment under pre- specified graαe-of-serv ce constraints. In particular the in^¬ vention relates to allocating (limited) power, frequency and technical resources to all cells of a communication system.

A communication network, e.g. for mobile raαio communication is designeα so as to satisfy the requirements with respect to the graαe-of-servicε (GOS) and as to maximize tne spectrum efficiency of the system. The GOS is mainly determined by the quality ana reliability of the communication in the network and cellular network planning (CNP) involves consieration of threshold values of the received signal power P_r, _ch and signal- to-noise ratio snr_th (power GOS) , probability of error P_e, th (information GOS) , clocking probabilities of new calls P_bn, _th and of handoff call attempts P_bh, _th (handoff GOS) , and signal-to- mterference ratio snr_th (interference GOS). Key factors in high population urban areas (where communication demand is concentrated) are: spectrum reuse, propagation conditions of the electromagnetic waves (EMW) , distribution of communication channels in time, spatial distribution of communication stations, hanα-over rate of calls between adjacent cells.

Prior art cellular network planning (CNP) rrethoαs are Dased on general assumptions as to the aoove key factors. E.g. statistical models of electromagnetic wave propagation, Rician / Rayleigh cnanne_^ models and regular cell pattern cf the area of interest with tne simplest cell snapes (triangular, rectangular, hexagonal) are employed. Such prior art methods have been publisned, among otners, oy MacDonald V. n .. "Tne cellular concept", Bell. Syst. Tech. J., 58, (1), pp. 15-42, 1979 and by Beca H. 0., Paunovic C C, Stojanovic I. S.: "-. design Concept ror

Radio Networks with s±c Frequence - Hopping Signaling", IEEE J. Sell Areas Commun . , SAC-c, ^ⁱ. Pp. 603-612, 1990 and by LyrnDeropou os D., Kotsopoulos S., o ]ias M. , Kokkinakis G.: "Cellular mobile radio communica ion network design on the basis of analytic traffic measurements," Elec^¬ tronics Letters, 28, (15), pp. 11425-1426, 1992

Consideration of the local environment and its influence on the propagation conditions m high population uroan areas to radio network planning is discussed e.g. by Gunmar, K., in WO 90/10342: "Method for planning radio cells", Mar. 199^, and by Markus, 0., in WO 93/15591: "Method and Apparatus for planning a cellular radio network by creating a model on a digital map adding properties and optimising parameters, oased on statisti^¬ cal simulation results", Oct. 1996.

The mam disadvantage of the prior art techniques is an overestimation of the required power, frequency and technical resources for CNP due to neglecting the above factors in the urban environment. Precisely, tne conventional approaches are based on statistical models of electromagnetic wave propagation, Rician / Rayleigh channel models and regular cell pattern of AOI with the simplest cell snapes (triangular, rectangular, hexagonal) . So the prior art techniques fail to proviαe an optimal solution of the problem with respect to the spectrum efficiency of the cellular radio network. The comparatively low resolution of the used maps prevents the above key factors from being taken into account for networκ planning in urDan areas. Interactive iterative procedures for network planning according to the prior art become cumbersome and unrealistic for network planning in large urban areas wp.en tne number of cells cecomes too great; as a result, tne requirements as to power, frequency and nardware are generally overestimated with these prior art planning methods.

The above problems become even more prohibitive m a network with micro-cells of a decreased ceil size, e.g. cf aDout 200m, which operate at low power of about 20mW with antennas located at lampposts e.g. Conseαuenth, there is a need for a rore accurate CNP of sucn ιetwor As a first aspect of tne invention a CNP method according to claim 1 is provided whicn satisfies the above mentioned need .

Another aspect of the invention is a network with micro- cells in a high population urban environment as defined in claim .

Preferred embodiments of tne invention are subject of the respective dependent claims.

The methoα of CNP ana the resulting network accoramg to the invention is based on refmeα statistical models of the subscriber movements. The refineo statistical models are derived from a digital terrain moαe_ (DTM) of high resolution (2m or less) which may be based e.g. on satellite photographs from the earth. In the method according to the invention subscriber movements in a cell are moαeled as a function of the specific geographic conditions in a micro-cell of the network. In conjunction with a mean channel holαmg time for each logged-m subscriber the model of all possiole movements of the subscriber results in a better ana reliable prediction of the required channel resources m a micro-cell and thus to a precise design of the required cel_ size.

The subsequent channe.

after definition of the cells is carried out iteratively c_^ starting with a fixeα channel assignment (assignment of zerc-th order) for all cells and by adapting i successive steps t~e channel assignment accorα- g to the actual αemand for cn ^-a.. capacity in tne cells.

The method of communication -etwork planning in a high population uroan area accora g :: tne invention, comprising the steps of forming a plurality :: cells depending on grade- of-service requirements for eacn ce_l, allocating a base station in each of said cells, assic-mg traffic channels to each of said cells, assigning freαuences to each of saiα cells, provides a refineα oroced_re " --_- the assigning of traffic channels to said cells is effected depending on the subscriber movements and the mean channel holding time of each logged-in subscriber.

Preferably the step of assigning of traffic channels includes averaging the time for all possible suoscriber movements in said cell, which may be carried out depending on the local street grid said cell.

A communication system according to the invention with a plurality of cells, each having a base station, is designed in such a way that each cell size depends on tne mean dwell time of a subscriber in a cell.

The CNP problem is of particular practical interest for optimal micro-cellular radio network planning in an urban environment. Due to its efficiency power, frequency and technical resources may be saved in the course of CNP, handoff operations are facilitated, and performance studies of the entire planned cellular radio network are permitted by highly accurate DTM based simulations.

These ana other features and advantages of the invention will be understood from the following detailed description of an embodiment, by way of example only, with reference to the accompanying αrawings.

Fig. 1 snows the flowchart of an embodiment of the cellular network planning method according to the present invention.

Fig. 2 snows an example for the determination of various areas defining one of the plurality of network cells.

Fig.3 is a principal diagram of an example for subscriber routes within a network cell.

Fig. 4 is a diagram of the probability of a call drop as a function of a mean detection time for several cells. Fig. 5 is a diagram of the probability of call blocking as a function of a maximum service interruption time for several cells .

Fig. 6 is a diagram of both the probability of a call drop and of call blocking as a function of a call rate density for one of the cells of Fig. 4 and 5.

Fig. 7 is a diagram of both the probability of a call drop and of call blocking as a function of a call rate density for two different cells.

The flowchart m Fig. 1 is one of a plurality of possible realizations of the method of communication network planning according to the invention. In particular, the method discusseα in the following is devised for networks in a high population urban area, i.e. in areas with a high traffic load on network channels. In such environments hundreds or thousands of calls may occur per hour. Many of the calls will necessitate handing- over to neighbor cells, most of the calls will last 10 seconds to 2 minutes only, few of the calls last longer than 10 minutes, depending on the time of the day. The propagation conditions are different from cell to cell due to specific buildings in each of the cells and their shadows and reflections of electromagnetic waves. These and other problems are to be solved when implementing a radio network in an urban area.

In general cellular network planning comprises the steps of reading and collecting all relevant data as input data for the planniⁿg procedure. Such data include all geographic information about the environment of the planned network. The geographic information may be extracted from satellite photographs, e.g Further, data are to be considered about the electromagnetic environment, i.e. electronic noise in the network cells. Still another aspect of preparation work for planning a cellular network is to assess tne expected traffic of the network under operation. The compilation of these data is step 1 of toe flowchart c: Fig 1 Further, in step 1 of Fig. 1 the key factors mentioned the introduction are considered, such as spectrum reuse, which is essential for high throughput inasmuch as the radio band^¬ width for wireless networking is strictly limited; the irregularities of the propagation conditions of electromagnetic waves, which are strongly affected by environmental conditions and by noise (interference levels do not depend on distance ra^¬ tios only) ; communication channels linking a base station and a mobile terminal, which are hardly predictable and highly vari^¬ able in time; spatial variations in traffic density, which lead to a channel demand that varies from cell to cell; dwell time of logged-m subscribers in a cell, i.e. any call may be handed off to a neighboring cell to provide uninterrupted service to a mobile subscriber when crossing a cell boundary and moving into adjacent cell while the call is in progress.

Based on the data of step 1 the formation of a plurality of cells is performed in step 2. The features of each cell, such as size and shape, depend on various grade-of-service requirements for each cell. The grade-of-service requirements define each a particular area in which one of the grade-of- service (GOS) requirements is met. So cell formation is the first step of cellular network planning aimed at constructing a cell for a given base station site in the area of interest under appropriate GOS constraints. The cell is defmeα as the intersection of power, noise, Shannon and error domains each calculated for a given BS site (see below).

The following relations are satisfied in each of said do- ma s- in the power domain tne received signal power P. ≥ P_r ,_r, where P_r is the receiveα signal power; in the noise domain snr ≥ snr_th, /here snr is the signal-to-noise ratio; in the Shannon domain R < C, where P s the transmission rate and C is the Shannon channel capacity; and, finally, in the error domain P_a < P_e, _th, (here P_e is the binary bit error rate.

Fig. 2 shows an example of the determination procedure of a cell In Fig. 2 a base station 7 is located at a suitable point in an area of interest (AOI) of the network. F om the base station 7 a first area 8 is supplied, in which any receiver (not shown) receives a signal from the base station 7 that meets the GOS requirements as to the error rate of the transmitted data (error domain). In a second area 9 any receiver receives a signal which meets the GOS requirements as to power (power domain) . In a third area 10 any receiver receives a signal which meets the GOS requirements as to noise (noise domain) . In a fourth area 11 any receiver receives a signal which meets the GOS requirements as to the channel capacity (Shannon domain) . Only in the intersection area 12 all of the GOS requirements are simultaneously met, i.e. area 12 is the required, usable cell area (shadeα area in Fig. 2).

Each of the above domains is calculated by the threshold domains method through a realistic multipath channel model developed for the received signal, which is based on a digital terrain model (DTM) .

The above channel model is directly applied to calculating power and noise domains. It is also used for deriving the analytical expressions for binary bit error rate for any modem and codec schemes. Then, the error domain is calculated on the basis of the expression obtained. Finally, the Shannon domain is calculated by using the generalised formula for C whicn extenαs the classical Shannon formula to tne DTM-based channel model.

where

P ^N β = k

¹ n *=1

Here, N_d is the total numoer of αeterministic signal components, each with an appropriate transmission factor _{r r} P₀ is the mean ^power of the emitted s. nal, is the mean power of noise; K is the extended Pic: defined as the ratio of the mean power of deterministic and statistical Rayleigh signal components .

Referring to Fig. 1 again, after said cell 12 has been de^¬ termined a base station 7 is allocated in each of said cells 12 in step 3. Base Station allocation (BSA) deals with BS posi^¬ tioning within the AOI. The major problem of conventional BSA procedures is that m real-world situation the cell shapes are irregular, strongly depend on BS sites ana often overlap each other. BSA is performed in optimal manner to minimize the total cell area which is the spatial part of E_s . Firstly, the initial redundant set of BS sites in AOI with the appropriate cells is formed automatically by selecting the desired BS locations (e.g. street corners, building roofs, etc.). At this point the cells are calculated by the method of cell formation of step 2. It is to be noted that BSA is performed only once, namely at the beginning of the cellular network planning, and it results in a fixed BS allocation and appropriate cell Pattern of AOI.

In step 4 traffic channels are assigned to each of said cells 12. Channel Assignment (CA) - in a FDMA system e.g. - aims at allocation of duplex frequency channels fairly among the cells throughout the entire network in accordance with a given traffic distribution. CA is performed in optimal way to minimize the total channel number and to provide the pre- specified handoff GOS. The optimal solution of CA may oe obtained by any prioritized channel assignment method valid for a network with arbitrary cell pattern and minimizing the channel number in each cell under prescribed handoff GOS. (The prioritized channel assignment method is e.g. αescribed by Se-Hyun Oh, Dong-Wan Tcha "Prioritized channel assignment in a cellular radio network", IEEE Trans. Commun., vol. 40, no. 7, pp. 1259-1269, 1992.) By this means the total cnannel number AOI is minimized. The cnoice of a prioritized C.ⁿ scheme is the most suitable for this planning step since the nandoff problem is essential for micro-cellular network planning.

The mean cnannel holding time plays t~e Key role m any CA method. In the prior art this quantity is assumed to be cor- stant in the whole AOI. But in real micro-cells with a big variety of street grid structures this value varies from cell to cell. In the present invention the mean channel holding time is obtained for each cell using the DTM-based structure of its street grid. An explanatory diagram for the consideration of subscriber movements is shown in Fig.3. When assigning 4 of traffic channels to said cells 12 depends on the subscriber movements a current location 13 of the logged-m subscriber is determined. From this subscriber location 13 and the geographic information about the cell, i.e. tne street grid, all possible movements 14, 15, 16 of the subscriber are modeled. In conjunction with the movement velocity and a mean channel holding time of each logged-m subscriber the expected dwell time of the subscriber in the cell is calculated. For example, the subscriber in Fig. 3 can either take route 14 to the right in Fig. 3 or route 15 down in Fig. 3 or route 16 to the left in Fig. 3. Each of the routes results in a different dwell time of the subscriber m the cell (assuming that the cell coincides with the map of Fig. 3) . The individual dwell times for each of the routes may be calculated by assuming a constant velocity for all subscriber movements in the cell. For the channel assignment the average of the three dwell times may then be taken as a basis.

The advantage of the consideration of route-dependent dwell times is that new calls and calls that are handed-off are taken into account for each cell. Tie required mean channel holding time is obtained by averaging the appropriate times related to individual possible routes of subscriber movements in a cell.

The mean channel holding time, so obtained, varies from cell to cell in accorαance with the street grid structure. With regard to a small dimension micro-cell the above algorithm of calculating the mean channel holding time is economically attractive which was proved by simulation results.

CA of step 4 results in a fixed channel demand plan in the AOI. At the beginning of cellular network planning CA is re-

SUBSTΓΓUTE SHEET (RULE 26) lated to the initial traffic data, then the channel oe.nand plan may be easily recalculated for new traffic data in accordance with daily or long-term variations of traffic data ιr the AOI. The need for a re-calculation is assessed in step 5. In step 5 the current traffic data or demands are investigated, and if the demands are not met by the actual channel assignment - due to a change of the traffic data - the method branches oack to a point between the end of step 4 and the beginning of step 4.

Referring again to Fig. 1, in step 6 frequencies are assigned to each of said cells 12. Frequency Assignment FA) is the final step of CNP and consists in finding a compatible assignment of carrier frequencies to radio cells (frequency plan) . FA is performed in optimal way to maximize the ~ean reuse factor R and to provide for the pre-specified interference GOS. The optimal solution of FA may be obtained by a frequency assignment method related to a network with arbitrary cell pattern and minimizing the required number of available frequencies under the prescribed interference constraints. By this means the mean re-used factor R_f is maximized. (An example of a frequency assignment method is e.g. given by Gamst A., "A resource allocation technique for FDMA systems", Alta Frequenza, vol. 57, no. 2 pp. 89-96, 1988.)

The compatibility matrices for uplink and downl -: interferences play the key role in any FA method. In conventional approaches these guantities are obtained using different statistical models of radio signal propagation or signal strength measurements .

After step 6 the required optimal cellular networi consisting of BS allocation plan, the appropriate cell pattern of AOI, channel demand plan and frequency plan is achieved, that satisfies the above optimization criteria. In particular the resulting communication system with a plurality of cells 12, each having a base station 7, is an optimal cellular network, since each cell size and shape depends on the mean dwel_ time of a subscriber in a cell 12. _Λ „, ,„„„ PCT/RUOO/00096

WO 01/72072

1 1

υnce the network is set up step 1 to 3 of Fig. 1 may be skipped, so that the flowchart begins more or less directly with assigning channels (step 4) to the cells and (new) traffic data can be taken into account for adapting the network later on .

In a preferred embodiment of the invention the step 4, assigning of traffic channels, includes averaging the time for all poss±ble subscriber movements 14, 15, 16 in said cell 12. This may be carried out depending on the local street grid in

As an example for the above the CNP problem for a frequency division multiple access (FDMA) protocol will be considered. In this case, each active user is assigned to one radio frequency carrying traffic signals.

From the general definition of E_s (in Erlang/MHz/km²) we get an exact formula for E_s in a FDMA cellular system with an arbitrary cell pattern of AOI, namely

where N is a total number of cells in AOI, S_x (km²) is the area of an l-th cell, n_{Cι x} is a nu oer of channels assigned to an l-th cell, (V(MHz) is a oanαwιαtτ per channel and A_{c lol} is a traffic carried in AOI. The mean reuse factor R_f m (1) is defined as

where N_f is a total numoer of frequencies used in AOI, and r-, is a re-use factor related to a -th frequency. Maximizing of spectrum efficiency E_s, given by (1), is

N achieved by minimizing the total cell area __ S, at the base station allocation (spatial planning), via minimizing the total

N channel number __ n_{c l} during channel assignment and via maximiz- ing the mean reuse factor R_f in the process of frequency assign^¬ ment (two last steps are related to frequency planning) .

As a summary, the basic features of the invention are as follows: the invention is based on a unified treatment of CNP in urban areas using a high resolution geographic model; the traffic is rigorously defined for a given multiple access protocol; the exact analytical expression of the spectrum efficiency is derived for arbitrary cell patterns of the area of interest; the CNP process is based on consecutive spatial and frequency / time planning; the specific form and contents of its successive steps are subject to the appropriate GOS constraints and to the requirements of maximizing the spectrum efficiency derived from the above analytical expression. Thus, the planning is performed in an optimal manner to guarantee the pre-specifled GOS levels and to provide the maximum spectrum efficiency of the whole system. Further, the high resolution geographic information is used to provide the required high accuracy of CNP and to account for the above Key factors in urban areas. With the high resolution geographic information a realistic radio channel model is used for the received radio signal. Thus, the method of CNP according to tne invention improves the efficiency of utilisation of power, frequency and technical resources in urban areas. Moreover, it guarantees the pre-speciflea levels of GOS standards (power, information, handoff, and interference) and provides the maximum in spectrum efficiency of tne system an optimal manner It should be emphasized that the prescribed constraints o" probabilities of call blocking and call dropping are always satisfied in such systems due to inherent features of the above cell formation and channel assignment processes of CNP. This is not the case when conventional CNP with regular cells is applied in a real world situation. Extensive GSM-simulations nave shown that call loss in conventional systems seriously degrade due to specific situations in the environment This call loss degradation increases with growing traffic load. The method of CNP according to the invention permits to avoid these problems. Further, complex topological structures of urban areas are taken into account by the high resolution geographic information. The fine structure of electromagnetic fields in a complex urban environment is also taken into account, since the main physical mechanisms of electromagnetic wave propagation (diffraction and multiple reflection) may be exactly modeled. A realistic model of dynamic multi-path channels, predictable in every point of AOI for arbitrary BS site, forms the basis for the method of claim 1 Traffic dynamics within a frequency domain are considered m step 5, so that the network is able to react on varying traffic demands. No interactive iterative planning procedures or field strength measurements are used for network planning; the interactive simulation tools are used only as an auxiliary means for visual illustration of the results and their analysis while the cellular network planning is performed automatically.

The extensive simulation results show that the spectrum efficiency of micro-cell radio networks as planned by the method according to the invention is increased by nearly an order of magnitude compared to conventional planning models. For example, in the case of FDMA the extensive simulation shows the spectrum efficiency is about 10 Erlang/MHz/km² or more for various design parameters. Note that in present cellular systems this value is about 1 Erlang/MHz/km².

In order to quantify the advantages of the cellular network planning (CNP) according to the invention as opposed to the prior art an additional call loss (CL) analysis was carrieα out on the basis of GSM-simulation in an area of about 1.5 km x 1.5 km .

Call loss in conventional personal communication systems (PCS) with regular cells is well known to depend on system parameters such as mean detection time τ_A and maximum allowed service interruption time r_ma for call blocking (CB) and call dropping, respectively. The analysis was performed under conventional grade-of-service (GOS) constraints, i.e. P_B <2% for call blocking probability, P_Jr ≤ 2% for call drop probability with various τ_A and r_max in each cell of the cluster in the con^¬ sidered area of interest (AOI).

The probability of a call drop as a function of a mean detection time is shown for cells 1 , 2, 3, 4, 5, 6, 7 in the dia^¬ gram of Fig. 4. The probability of call blocking as a function of a maximum service interruption time is shown in the diagram of Fig. 5 for same cells 1 , 2, 3, 4, 5, 6, 7 as in Fig. 4. From

Fig. 4 and 5 it is clear that the above GOS constraints are satisfied only when τ_A and r_max exceed the appropriate threshold values which vary from cell to cell. For these threshold values P_B and P_dr may be expressed as a function of new call rate density Λ_fl [call/s-km²] for every cell in the cluster, respectively.

A typical example is shown in Fig. 6 for the first cell 1 in

Fig. 4 and 5. In the diagram of Fig. 6 both the probability of a call drop and of call blocking in said first cell 1 is shown as a function of said call rate density A_a . From this diagram it is clear that the above GOS constraints are satisfied only when the values of Λ_α are below the threshold values for call blocking and any call drop which vary from cell to cell as follows from the simulation.

The results of the above call loss analysis in the cluster are summarized in Table 1. These results show that for conventional PCS with regular cells it is formally possible to satisfy the pre-specified GOS constraints on P_B and P_dr by choosing the appropriate threshold values of τ_A and r_max for restricted traffic load in the AOI. I the considered case, as follows form the Table 1, τ_λ ≥ 2.5 s , and r_max > 7.5 s for A_a ≤ 2.7 [call/s-km] in the whole AOI. The constraint on r_max seems to be unacceptable in practice.

Table 1. The results cf call loss analysis in the cluster

Since, however, the above GOS constraints occur m the appropriate channel allocation procedure as optimization criteria in the DTM-based CNP the above GOS constraints are always guaranteed in the AOI for any traffic load.

Tne typical examples of P_B and P_lr as functions of \₀ are given in Fig. 7 wnich is a diagram or both the probability of a call drop and of call blocking as a function of a call rate density for two different cells A and B (not consiαereα above) . As can oe seen from Fig. 7 the probabili ies P_B and P_dr never exceed the pre-specifled GOS constraints any of the DTM- based cells in the AOI αespite variations of the probabilities with A_a .

Tnus, the GSM-simulation shows that call loss characteristics can essentially deteriorate when conventional PCS with regular cells are used in a dense populat-on urban environment unless tne a^ppropriate constraints are imposed on said system parameters r., and r_max wnich otherwise a/ achieve unacceptable values, as s-iown acove. On the other hand, there is no such problem when DTM-based

PCS with irregular cells are used since tne GOS constraints on P_B and P_dr are inherent parameters of the DTM-based CNP.

2072

17

Reference signs

1 read input data

2 cell formation step

3 base allocation

4 channel assignment

5 query: new traffic data available?

6 frequency assignment

7 base station

8 first area

9 second area

10 third area

11 fourth area

12 cell = intersection of first through fourth areas

13 current location of logged-in subscriber 4 first route 5 second route 6 third route

Claims

5 1. Method of communication network planmnq ± - high population urban area, comprising the steps of forming (2) a plura_^ty of cells (12) depends - on grade- of-service requirements tor each cell, allocating (3) a base station (7) m each of -__α c^Ωils C (12), assigning (4) traffic channels to each of sai cells (12), assigning (6) frequeⁿcies to each of said ce±_ '12 , wherein the assigni g (4) of traffic channels Sdiα cells (12) is effected depending on the subscriber ^~ "ements 5 (14, 15, 16) and the mear channel holding time of eacn logged- in subscriber.

2. Method according to claim 1, wherein the steo of assigning (4) of traffic channels includes averaging tie time for 0 all possible subscriber movements (14, 15, 16) in sa_d cell (12) .

3. Method according to claim 2, wherein the steo of averaging the time for all possible subscriber movements ^1 , 15, 16) is carried out depending on the local street gr_α m said cell (12) .

4. Communication system with a plurality of ce.^s (12), each having a base station (7), wherein each cell s_ze depends on the mean dwell time of a subscriber in a cell (1_