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A Concept for Dynamic Neighbor Cell List Planningin a Cellular System
Hikan Olofsson, Sverker Magnusson, Magnus AlmgrenEricsson Radio Sy stems ABS-164 80 Stockholm
Sweden
Abstract In the n ear future, capacity needs will lead to cellu-lar systems with a complex mixture of cells with different sizesand unpredictable coverage areas. Such complex systems willincrease the need for manual radio network planning dramati-cally, unless intelligent tools are develope d to assist in the plan-ning, This paper addresses the problem of determining whichcells arc neighbors to a ccrtain cell. Today this has to be manu-ally determined for each cell in a system. A concept is proposedwhere the neighbor cell lists are dynamically planned duringsystem operation, with little or no manual assistance. The pro-posed class of algorithms uses information about the long termnetwork behavior, and the neighbor cell lists are continuouslyupdated.The proposed concept considerably reduces manual p lanning,and simulation results show that the scheme also improves theoverall system quality by reducing the length of the neighbor celllists.
I. INTRODUCTIONA major issue for cellular operators today and in the nearfuture is system capacity. Th e fast subscriber growth in many
cellular systems forces network operators to increase thenumber of sites and make cells smaller in order to providehigher capacity. Th ere is already a fast developm ent towardshierarchical cell structures (HCS), where large and small cellsprovide coverage for the same area. In that perspective, it iseasy to realize that the effort and cost of manual planning ofthe networks will increase. Hence, there is a need for intelli-gent radio netwo rk algorithms that reduce manual planning.
Creating and maintainin g the frequency reuse plan of a sys-tem is a main task, as frequency planning to a large extentdetermines the quality and capacity of the system. Powerplanning is an other impo rtant field since the output power ofa cell determine s its coverag e. A third important system plan-ning issue, which will be emphasized in this paper, is to deter-mine neighbor cell lists. In todays main digital cellularstandards, GS M (Global System fo r Mobile Communica-tions), D-AMPS (Digital American Mobile Phone System)and PD C (Personal Digital C ellular), a neighbor cell l ist foreach cell is needed. Proper planning of these lists is a prereq-uisite for a well functionin g network.The ultimatc goal would be a self-configuring systemwhere frequ encies and neighbor cell l ists are allocated to all
cells without manual planning. W hereas several method s fordynam ic frequency allocation have been proposed in the li ter-a ture , e .g. [11-[3],dynam ic planning of neighbor cell lists hashardly been addressed at all. The intention of this paper is topropose a concept for dyn amic planning of neighbor cell listswhich solves the problems involved with the manual planningof today. The analysis is done for a TDM A (Time Divis ionMultiple Access) system, but the concept is applicable andequally useful in all systems, regardless of multiple accessmethod. Sim ulations are performed to evalua te the quality ofthe neighbor cell lists thus derived.
11. MO BILE SSISTED A NDO VERThe main digital cellular standards of today all use Mobile
Assisted Handover (MAHO). It consists in letting the mobilestation measure the signal strength on neighboring cellsbroadcast control channels. If the signal strength of a neigh-bor cell is sufficiently high compared to the own link signalstrength, the network initiates a handover to that cell .
To be able to perform measurements, the mobile mustknow which broadcast control channels are used in the neigh-bor cells. Therefore, for each cell the network stores a list ofhandover candidate cells. This list is referred to as the neigh-bor cell list for that particular cell. For each neighbor cell inthe list, the cell identity is stored together with the frequencynumber used by the broadcast channel in the cell. When amobile station enters a cell and needs to know what neighborsto measure, the frequency numbers included in the cellsneighbor cell list are transmitted to the mobile station. Thislist of frequency numbers IS referred to as afrequency list
A sim ple example can be used to il lustrate the use of neigh-bor cell l ists . Consid er Figure 1,where a mobile station enterscell B , through a call setup or a handover. The netw ork storesa neighbor cell list for cell B, in this case including cells A, Cand D 1). A frequency list containing the frequency num bersincluded in the neighbor cell list is transmitted to the mobilestation via the base station 2 ) .Th e mobile station is then ableto perform signal strength measurements on the selected fre-quencies 3) In GSM , the mob ile station also decodes a 6-bitbase station identity code (BSIC) transmitted on each cellsbroadcast channel. The measurements are periodicallyreported to the network 4) together with the received signal
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FIGURE 1. The use of a neighbor cell list in a TDMA systemthat uses Mobile Assisted Handover MA HO .strength on the own link. The reporting interval is i n the orderof one second. In the network, the measurement reports areused for handover purposes (5). The cell selection algorithmsdiffer slightly between manufacturers, but are normally basedon comparing the signal strengths of neighbors and own link.
111. PROBLEMS WITH MANU AL LIST PLANNINGIn commercial cellular syste ms of today, the neighbor cell
lists are planned manually by the cell planner by means oftheoretical cov erag e predictions be fore installation of a basestation. A number of problems with the manually plannedlists can be identified, and the aim of a dynam ic neighbor celllist algorithm is to solv e these problems. In t he following sec -tions the problems will be describe d, together with a descrip-tion of how dynamic planning would help solving them.A. Predictability
Ra dio propagation is highly dependeint on the geographicalenvironment. Statistical models for radio propagation in dif-ferent types of environments exist, and the cell planner nor-mally uses such models together with digitized maps topredict coverage areas and create neighbor cell lists for thecells in a system .However, the maps are not detailed enough to includebuildings etc. which together with the statistical nature ofthe propagation mode ls make s the predictions more or lessinaccurate, especially in micro- and pic olayers.A dynam ic neighbor cell list algorithm based on measure-ments of the actual propagation environm ent will reduce the
need for manual predictions.
B. FlexibilityIn an evolving cellular network, the neighbor environment
of a cell is subject to frequent changes. New base stations areoften installed, each of them requiring manual replanning ofthe neighbor cell lists in a number of surrounding cells. More-over, in micro- and picocell layers the radio propagation isalso affected by new buildings, new w alls etc. Cha nge s of thiskind are even more difficult to handle, since they may beunknown to the network operator.A dynam ic neighbor cell list algorithm based on measure-
ments of the actual propagation environment, which activelytries to discover infrastructural changes, will reduce the needfor time consuming manual replanning of the network.C Measurement accuracy
Since the neighbor environment is difficult to predict, thecell planner must include a large number of cells in the list toensure that all potential neighbors are included.
However, the neighbor cell lists are used to control theMAHO measurements made by the mobiles, and since theirmeasurement capacity is limited long neighbor cell lists causepoor measurement accuracy.
A dynamic neighbor cell list algorithm will identify unnec-essary cells and provide a possibility to keep the lists short,thus improving measurement accuracy.D. Unfavorable neighbor cells
Th e cell planner may make a mistake and include a cell in aneighbor cell list that is unfavorable i n a sense that making ahandover to that cell often means losing the call. In a conven-tional system w ith fixed lists, the unfavorable cell will remaini n the list until a manual redesign is made.A dynamic neighbor cell list algorithm based on event sta-
tistics will identify and remove unfavorable cells from thelist, thereby reducing the number of lost calls and reducingthe need for manual replanning.
1V. C O N C E P TR E S E NT AT IONIn order to solve the problems with manual planning, a
dynamic list planning scheme is proposed according toFigure 2. The basic idea is to let the neighbor cell lists bedynamically built, based on the long term behavior of the net-work. Information that is interesting for neighbor relations iscollected and processed through slow filters, and thus theknowledge of the neighbor relations slowly increases. Theknowledge is then used to update the neighbor cell lists.
In the following sec tions , the different blocks i n Figure 2 isexamined in some detail.A . Neighbor list generator
The neighbor list generator generates a neighbor cell listfor each cell, based on event statistics from the statistics unitand measurements previously collected from mobile stationsand base statio ns. The broadcast c ontrol frequencies of the
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MEAr
Mean mobile speedTraffic time dis tribution
;UREMENT STATISTICSUNITc ORTS
S km/hPoisson
L T l
Shadow fading B S correlationShadow fading spatial correlation
Shadow fading std. dev.
NEIGHBORLIST
GENERATORp = 0 5
I/e at I10 m(short ) / I100m(long)8 dB(4.5 d B short, 6.5 dB long)
I I FREQUENCY LISTNumber of frequencies
Frequency reuse
TABLE 1. System parameters used in simulations.
9 broadcast / 45 traffic frequenc ies9 cell reuse
I Number of simulated cells 81 cells (27 sites with 3 cells each) I
Simulated time slotsHandover hysteresis
DI-opped call ClI threshold
Cell radius I km I
I3 d B5 d B
I Geographical traffic distribution I Uniform
Mean call time 120 sec.I Distance attenuation model I Okumura-Hata: 351og d)
Simulator time step 10sec.
FIGURE 2. Block diagram of the proposed scheme for dynamicneighbor cell list planning.
neighbor cells, the neighborfrequencies are then put in a fre-quency list that is sent to the mobile station via the base sta-tion.B. Test neighbor l st generator
To be able to discover new good neighbor cells, all poten-tial neighbor cells within the system must be tested every nowand then. The test neighbor list generator adds one or moretest frequencies to the frequency list, thus allowing the mobilestation to perform measurements on those frequencies. If atest neighbor cell is measured strongly by a large number ofmo biles , the neighbor list generator takes this into accountand eventually includes this neighbor in the regular neighborcell list. In Figure 2, the test neighbor list generator isincluded i n the neighbor list generator block.C. Statistics unit
The statistics unit provides event statistics to the neighborcell list generator. The events of main interest are handoverfailure rate between two cells, number of handovers betweentwo cells and dropped call rate immediately after handoverbetween two cells.
V. SIMULATIONSTo investigate the performance of the proposed concept,one version of the algorithm has been evaluated in simula-
tions of a TDMA system.The used simulation environment dynamically models alarge number of mobiles moving in an area covered by cells.The simulator includes models of traffic, radio propagationand radio resour ce allocation. Exam ples of output data fromthe simulator are the distribution of C/I sa mples for the ongo-ing calls and statistics of handover and dropped calls.A Simulation environment
A system with 27 equidistant 3-cell sites was simulated,
resulting in 81 cells. To avoid border effects which wouldprevent the use of data from the border cells, a wrap-aroundtechnique was used.
Th e traffic distribution was uniform throughout th e areaand calls were generated accor ding to a Poisson process.
The Okumura-Hata model was used to calculate the pathloss between mobile stations and base stations. No multipathfading was included. Two lognormal shadow fading compo-nents were added to the pathloss, one with long spatial corre-lation distance modeling terrain variations and one with shortcorrelation distance modeling buildings etc. Th e total stan-dard deviation of the shad ow fading was 8 dB. Moreover, thefading values between a mobile station and different base sta-tions was correlated with a correlation coefficient p = 0.5.The resulting cell shapes are indicated in Figure 3.
Each cell used 6 frequencies, one of which was defined asbroadcast frequency. The frequencies w ere reused in every9th cell, which implies that a total number of 54 frequencieswere used in the system. Only one time slot was simulateddue to limited simulation capacity.Cell selection at call setup and handover was based on sig-nal strength, with a handover hysteresis of 3 dB. On average,handover occurred 0.37 times per call. Calls were dropped ifthe CO ratio was lower than 5 dB in either up- or down link.Important system parameters used in the simulations arelisted in Table 1.B Simulation methodology
Simulations were carried out for a system with stat ic infra-structure, i.e. no base stations were added or removed duringthe simulations, and the radio enviro nment remained thesam e.*A imulation run consisted of three main pa rts, all per-formed continuously throughout the simulation:1) collecting information about the network behavior,
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2) designing the neighbor cell lists based on gatheredinformation so far, andmonitoring the quality of the obtained lists.)
In the following, the methods used for the three parts aredescribed and commen ted.Collecting network information
Th e information used to crea te neighbor cell lists was gath-ered by the mobile stations MAHO measurements. A fre-quency list including neighbor frequencies and testfrequencies was sent to each mobile. All frequencies notdefined as neighbor frequenc ies were defined as test frequen-cies. Hence, the total frequency list always consisted of allbroadcast frequencies.
Each cell maintained a filtered value for each other cell,descr ibing its suitability for handovcr, wh ich was updatedduring certain events. The events of interest were those whenthe signal strength of a frequency in the frequency list forwhich BSIC w as decodable exceeded that of the own cell,
Figure 3 illustrates the outcome for one of the cells(shaded). The surrounding cells are marked with filtered val-ues describing how suitable for handover they are. The f i l -tered values essentially correspond to the percentages of thetotal number of handovers from the shaded cell that go toeach of the surrounding cells. Thus, the maximum value is100,corresponding to all handovers g oing to one sing le cell.Designing the neighbor cell lists
At the beginning of each simulation the neighbor cell listswere empty. (This is a pessimistic assumption: in a real net-work there would always exist some kind of initial lists.) Foreach iteration, updated neighbor cell lists were derived fromthe filtered values. For a certain cell A, the neighbor cell listwas created by including the cells for which A had filteredvalues exceeding a certain predefined threshold. The samethreshold was used for all cells. To sirnulate lists of differentaverage length, the threshold w as increased or decreased.Monitoring the quality o he obtained lists
The quality of the obtained neighbor cell lists was moni-tored continuously. As the objective was to observe to whatextent incorrect or incomplete neighbor cell lists result i npoor selection of base stations, the situation for the mobileswho ma de correct base station choices was of little interest.A correct selection of base station is here equivalent to thatwhich would result from having a neighbor cell list includingall other cells. The im porta nt thing to monitor is consequentlywhat mo biles m ake an incorrect (choice, and how thateffects the quality of their connection.
Thus, the simulations were performed i n the followingmanner: For each simulation, with designed lists correspond-ing to a certain threshold value, a parallel reference simula-tion was run in which the mobiles performed in an identicalfashion, apart from the fact that selection of base station was
FIGURE 3. The left plot shows the typical coverage area forsome of the simulated cells. The right plot is part of the simula-tion result: the filtered values show how suitable handovers arefrom the shaded cell. Based on these, a neighbor cell list for theshaded cell can be generated.based on neighbor lists including all other cells. To make thispossible, a mobile station dropped or blocked in either of theparallel simulations was removed from the other as well.Information was then gathered only for mobiles that hadselected different base stations in the parallel simulations.C. Simulation results
In Figure 4, the results from simulations with dynamicallydesigned lists and different list thresholds are plotted. The topplot depicts the average list length as it develops over time.The middle plot shows the percentage of time a mobileselected a different base station than that of its identical twinin the reference system. Finally, the bottom plot shows thedegradation in received signal strength for the differingmobile stations compared to the reference system, which isused as quality measure. The results show that the algorithmconverges faster with a lower threshold. O n the othe r hand,when a steady-state is reached, a low threshold value resultsfn larger performance variance between iterations.
Figure 5 shows the performance when a steady-state isreached as a function of average list length. For comparison,results are also shown for two simulations w here the lists con-sisted of the 6 and 18 geographically closest cells respec-tively. These results are represented by dashed lines.
Some conclusions may be drawn from Figure 5. First of all,note that the number of occasions with different base stationchoices decreases as the neighbor list length increases, just asthe difference in signal strength for those particular mobilescompare d to their refe rence twins. This should not come as asurprise. The quality is poor for very short lists and increasesfast with increasing list length until an avera ge list length of 6is reached. With 6 neighbors in the lists, the system withdynamically designed lists displays a performance similar tothat of the system with the 6 geographically closest cells inthe lists. One should note that the quality improves as thedynamically designed lists expand beyond that length.
From the results in Figure 5 it is easy to draw the hasty
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conclusion that the overall system quality improves withincreased num ber of neighbors in the lists. It may even lookas if the best performance is achieved with neighbor listsincluding all cells. Ho weve r, since long lists have the disad-vantage of poor mea sureme nt accuracy (which is not modeledin the sim ulations), long lists still ha ve to be avoided.
The main result of the simulations is that by gatheringM AH O me asurem ent statistics from a system, it is possible todesign neighbor cell lists without manual planning. T he sig-nal strength performance of a system with such lists is similarto that of systems whose lists include the geographically clos-est cells.
It is worth noting that the results have been obtained in asystem with a simple cell structure. In a system with a mix-ture of cell sizes and more complicated traffic patterns, thedynamic list planning scheme is expected to perform evenbetter relative to system s planned using only geogra phicalinformation.
5
5
00 5 1 0 15 2 5 3 35 40 45 50Simulation teration numbero L . L 4 10 5 1 0 15 2 5 3 35 4 0 45 50Simulation teration number
i I1 5 2 25 3 35 40 45 50
Simulation teration number
l5 1 5 2 25 8 35 40 45 50
Simulation teration numberFIGURE 4 Perform ance of systems with dynam ically designedlists with different average length as they develop over time,compar ed to the reference system. Results are given for threedifferent thresholds, 10 5 and 0.5.
VI. CONCLUSIONA concept for dynamic planning of neighbor cell lists in a
cellular system has been proposed. A number of advantageshave been described, am ong them the considera ble reductionin manual planning needed for the network operator.
By means of simulations it has been shown that the pro-posed schem e has a perform ance similar to that of a manuallywell-planned system. In fact, since the proposed scheme isable to reduce the a verage length of the lists without loss ofsignal strength performa nce, the proposal improve s the over-all system quality, since long lists imply poor measurementaccuracy.
VII. REFERENCES[ I ] Y Furuya, Y Akaiwa, Channel Segregation, A Distributed AdaptiveChannel Allocation Scheme for Mobile Communication Systems, in
Proceedings of the Second Nordic Seminar on Digital Land MobileRadio Communications, Stockholm, pp. 778 78 1987.
[2] H. Andersson, H. Eriksson, A Fallgren, M. Madfors, AdaptiveChannel Allocation in a T I A IS-54 System, in Proceedings of the 42ndIEEE Vehicular Technology Conference, pp. 778-78 1992.M. Almgren, M. Frodigh, B. Hansson, J. Lundequist, K. Wallstedt,Adaptive Channel Allocation in TACS, in Proceedings of the IEEEGlobal Telecommunications Conference, pp. 15 17- S2 I 1995.
[3]
: 4 5 h 5 5 6 55 7 5 s 8 5 bAverage neighbor cell list length
\Dynamically design ed lists i
I4 4.5 5 5 5 6 6 5 7 7 5 8 8 5 95 Average neighbor cell list length
FIGURE 5. Steady-state performance of lists with differentaverage length, The solid lines correspond to a system withdynamically designed lists. The dashed lines correspond to twosystems, including in the neighbor cell lists the 6 and 18geographically closest cells respectively.
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