RESOURCE ALLOCATION IN A FREQUENCY HOPPING PCSl900/GSM/DCS1800 TYPEOF NETWORK Thomas Toftegaard Nielsen, Jeroen Wigard, Per Henrik Michaelsen and Preben Mogensen Center for PersonKommunikation Fr. Bajers Vej 7A, DK-9220 Aalborg @st,Denmark E-mail:
[email protected]
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Abstract Resource allocation in a frequency hopping network is even more problematic than in a traditional network. The combined effect from all serving frequencies has to be considered directly in the allocation process. An algorithm doing this for a PCS1900/GSM/DCS1800 type of network is presented. The frequency as well as the interference diversity gain from frequency hopping is included directly in the allocation process. Exploiting a cost function, which includes the gain from frequency hopping, a performance enhancement of around 20% is achieved compared to an operating reference frequency plan. A graphical visualisation tool has been developed as well. This tool uses a network quality measure tightly linked to the FER rather than the traditional C/Z or BER. Using these statistics an increase in network quality is shown.
every 577 ps. In modelling therefore necessary to look frequencies in the serving cell effect from hopping across included in the allocation.
The principle behind the allocation algorithm itself is described in section I1 as well as the limitations chosen in the implementation. Section I11 describes the gain from frequency hopping while section IV describes how the gain has actually been implemented in the allocation process. Some essential results are presented in section V. The graphical visualisation tool is briefly treated in section VI, while a concluding summary is given in section VII. 11. THERESOURCE ALLOCATION ALGORITHM An algorithm, JETTPlan, doing this, by incorporating the frequency and interference diversity gain as well as fractional load gain in the allocation itself, has been developed. The algorithm falls under the category of heuristic algorithms, where a combination of a search tree, dynamic constraint setting and updating and intelligent ordering is exploited. The following characteristics expand the algorithm further:
I. INTRODUCTION Resource allocation is a well documented problem of mathematical origin [1,2]. How to allocate the frequencies, one of the scarcest resources a mobile operator has, is found to be a typical "-hard problem [3]. It is typically one of the most time consuming and challenging tasks in designing and maintaining a high performance PCS 1900/GSM/ DCS 1800 type of network. Many different types of allocation algorithms have been studied, as e.g. the hill climb algorithm, simulated annealing, neural networks etc. [10,11,12,13]. A completely different more practical approach has been taken in [4], where resource allocation in a FH network has been considered. Several quite powerful commercial tools exists, such as PlaNET' or NPS/X.* However, present methods performs the resource allocation by looking at each frequency in each cell individually. In case of frequency hopping (FH) this way of allocating the frequencies is far from optimum, since the quality of the individual frequency does not describe the quality experienced by the mobile station (MS). In frequency hopping networks the MS is hopping across several frequencies during a call. This frequency shift takes place
It has been chosen to split the band between the traffic channels (TCH) and the broadcast control channels (BCCH). In general the splitting of frequencies into different bands result in a loss in trunking efficiency [16] and should therefore be avoided. However, in systems like GSM there is a fundamental difference between the TCH and the BCCH. The interference arising from the TCH is entirely load dependent, while the interference from the BCCH is completely load independent (beacon signal). Furthermore, the BCCH carries information for identifying cells for access, paging and measurements of neighbouring cells [9]. Therefore power control and DTX cannot be used on the downlink BCCH channels. Optimisation goes on for a definite (large) number of loops, while minimising a cost-function. However, the program can be stopped whenever desired.
' PlaNET is a product of Mobile Systems International(MSI) [ 5 ] , and the most widespread frequency planning tool in Europe. NPS/X is a product of Nokia Telecommunications [6]. 0-7803-5565-2/99/$10.0001999 IEEE
the situation better, it is at the quality of all the at the same time. Also, the the frequencies must be
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0
The algorithm can calculate the cost function of an existing frequency plan to be used for bench-marking. A list of cells to be planned as part of a large area can
be used, while still exploiting the interference from the entire large area. It is therefore possible to plan only one cell within a large area, as well as a large cluster of cells within an even larger area. 0
Both CO-channeland adjacent interference is included. Separate cost function for the BCCH and TCH layer.
The general idea is to exploit the cost function to describe the quality of a specific frequency plan. The aim is then to minimise the cost value with a given number of base stations and set of frequencies. The general structure of JETTPlan, is shown in Figure 1.
threshold. A threshold of 9 dB has been used for both TCH and BCCH frequencies, while in principle different thresholds could be used for the two bands.
GAIN 111. FREQUENCY AND INTERFERENCE DNERSITY The main purpose of JETTPlan has been to design a frequency allocation tool that includes the gain from FH directly in the cost function. The effect from FH is typically modelled by estimations of respectively the frequency and interference diversity gain [ 151. Frequency diversity. This type of gain arises from hopping across frequencies that are exposed to uncorrelated fading. The decoding performed in the MS becomes more efficient and a gain is achieved. The frequency diversity gain is highly dependent on the speed of the MS, the number of frequencies in the hopping sequence and the type of datareceiver. A link simulator, developed at Aalborg University, has been used to describe this gain. Numerous simulations of different combinations of the relevant input parameters, such as e.g. random or sequential FH, MS speed from 3 or 50 km/h, hopping across 2,3,4 and 8 frequencies etc., have been carried out. Based on statistics from these simulations the simple model in Figure 3 has been developed. Here the frequency diversity gain is described, for the specific type of data-receiver used (a SOVA type) [7].
Figure 1. Simplified block structure of JETTPlan. I
Only two input files are required. One describing each of the cells in the network, how many and which frequencies are allocated (the carrier database) and one describing the interference relationship between the cells (the interference matrix). A simplified example of the interference matrix (I) is given in Figure 2. Here the interference is based on carried network traffic. 1
3
5
7
9
I1
13
1s
MS Speed [ds]
Figure 3. Thefi-equencydiversity gain from FH.
The frequency diversity is modelled as a linear function of the MS speed. In Figure 3, the gain is described from 1 to 15 m/s (approx. 3 to 50 kmh) by the interference reduction factor. That is, if hopping across 8 frequencies and moving with a speed of 3 4 s the level of interference with FH can be reduced to 39 % compared to the non-hopping case.
Amount of potentiul afSectecl traflic in cell 2 b y cell 3
Figure 2. Simplified example of the interference matrix. The interference matrix is an NONmatrix, with N equal to the number of cells to be considered. The interference matrix describes, for each cell, the absolute amount of affected traffic in a particular cell by interference from all other cells (if the cell uses the same or one of the adjacent frequencies). Therefore the amount of busy-hour traffic carried in each cell is found in the diagonal. Affected traffic means that the traffic has a CII worse than a certain
Interference diversity. This type of gain arises from the effect of the somewhat uncorrelated adjacent- and cochannel interference experienced while using the different frequencies. It is modelled by dividing the contribution in two. They are:
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1. Lowering the interference according to the network load. 2. Fractional loading decoding gain due to the spreading of the interference.
Cost- level(i,a ) = c c o s t-co- channel(k)
+
I
(1)
adj- factor.
cos t- a& channel( j ) J
The gain from lowering the interference due to the actual network load is calculated simply as a linear function of the load. That is, if the load is 50 % the equivalent gain in C/Z is 3 dB and so forth. The gain of fractional loading arises from the fact that the decoder in the receiver performs better when the interference varies on burst level. This effect is enlarged not only by hopping across different frequencies, but also by lower network load. This causes what is referred to as an on-off characteristic with a high variation. The actual performance depends on the number of hopping frequencies as well as the network load. Again numerous simulations using CAPACITY, a network simulation tool combining system and link level developed at Aalborg University [ 141, has been carried out. From statistics the model in Figure 4 was made.
In equation (1) i is the frequency and a is the cell being considered. k is the cells with a co-channel a n d j is the cells with an adjacent channel. From equation (I), each frequency contributes with two factors, an adjacent- and a co-channel contribution. The co-channel factor is calculated based on direct values from the interference matrix, while the adjacent channel contribution is simply the co-channel contribution weighted by an adjacent factor (adj-factor). In this study it has been set to 1.5 %. If denoting the co-channel interference from cell B to cell A (taken directly from the interference matrix) by JBA, the gain from frequency diversity Hopgain and the interference diversity gain Loadgain, the cost function contribution (per frequency) looks like shown in equation (2). Cost _level( i,a J = x ( L o u r l g a i n ( k ) J , /1 Hopgain( a ) + I
Hopgain( k J) +
Loadguin(u) J , 0015 x(Loadguin( J J J , , J
Loudgain( u J J ,
Hopguin(oJ+
, Hopgain( J J)
(2)
To get the complete interaction between each pair of cells, a contribution from both the serving cell, A, and the interfering cell, B, has to be included. This interaction is illustrated in Figure 5. 100
90
80
70
60
50
40
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20
IO
(3
0
Loadgain(A)
Network load [%I
J*,B
Cell A
Figure 4. Thefractional loading gain arising fi-om interference diversity fi-om FH.
Hopgain(Aj
As seen in Figure 4, the gain of fractional loading is modelled by a linear function (in dB) of the load in the system. That is, with a network load of e.g. 50 % and if hopping across 8 frequencies a fractional loading gain in terms of CIZ of 1 dB is achieved. Already while hopping across 3 frequencies most of the gain is achieved. The fractional loading gain with hopping across up to 12 frequencies is always within the range of 0 to 2.1 dB. IV. INCLUDING THE FH GAIN Using the models for respectively the frequency and interference diversity gain it is possible to include the gain from FH directly in the cost function. The idealised contribution (per frequency) to the cost function is shown in equation (1).
J B,A
Loadgain(BJ
Figure 5. The FH factors exploited in the costfunction arising from each pair of servinglinteifering cells.
When including the gain from interference diversity in the cost function (previously illustrated in dB), it is converted to a linear interference reduction factor as was the case with the frequency diversity gain. That is, if e.g. hopping across 8 frequencies with a network load of 50 % the interference diversity gain is 4 dB (3 dB taken directly from the load and 1 dB from the fractional loading). 4 dB corresponds to approx. 0.4, which is then equivalent to the Loadgain factor for the particular frequency in the particular cell.
V. RESULTS Two different set of results have been made. Initially the performance of the search algorithm itself is tested. How
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good is the designed heuristic allocation algorithm compared to existing algorithms. Secondly, the effect of including the gain from FH is investigated.
TCH frequencies, but the cost function has been found to 731e3. For the JEKfPlan allocation the frequency band is split between BCCH and TCH band. Furthermore, an average network MS speed of 3 km/h has been used. The results are shown in Table 1 for various BCCH bandwidths.
Performance of the search algorithm: A high density tele-
traffic area in Denmark, containing between 200 and 300 cells, has been used. The distribution in number of TRX's per cell has been quite uniform, where most of the cells have 3 or 4 TRXs. A frequency plan was made, where the available spectrum was not divided in two parts (BCCH and TCH are treated in exactly the same way). Furthermore, the gain from FH was not taken into account. This corresponds to the way a traditional commercial frequency planning tool allocates the frequencies. In Figure 6 some typical results of the frequency plans made by JETTPlan can be seen. The cost function improvement is shown as a function of time for 3 different runs with JETTPlan. Also, the cost value of the frequency plan the commercial planning tool could come up with (the reference plan), is shown. It should be said that this plan has furthermore been manually optimised and tuned by experienced radio planners. It has a cost function of 1.23e6.
It is seen how the cost function of the network with 15 BCCH frequencies is lowest. With 15 BCCH frequencies the total cost is 576e3. This is a reduction from the reference plan of more than 20 %.
1.4SEM6
?
2
Table 1. The cost of 7frequency plans with different BCCH band sizes.
1.40E+06
9
ii 1.35EM6
3
1
U
VI. GRAPHICAL VISUALISATION
1.30E46
Figure 6. Evaluation of the search algorithm by itself.
In general, the raw bit error rate (in GSM described by RXQUAL), correlates poorly with the quality experienced by the mobile user. The coding gain from FH is not included in RXQUAL and the actual speech quality is therefore not reflected. The same is valid for the C/Z parameter. Since current planning tools use either the RXQUAL or the C/Z to visualise the quality of a frequency plan a problem exist.
The best frequency plan found by JETTPlan for the test area had a cost function value of 1.04e6 and was found after 493 minutes. For this plan an overall reduction in cost function around 15 % has therefore been achieved.
Furthermore, typically only the worst of the C/Z (or RXQUAL) values of the serving frequencies are shown. The following two problems therefore have to be dealt with:
Based on the figures above as well as other similar results, it is concluded that the developed search algorithm (by itself) performs satisfactorily.
1. Only the worst of the serving frequencies is typically shown in each pixel. 2. Typically the C/Z or the raw BER (RXQUAL) is used for the visualisation, of which none include the gain from FH.
1 2
1.25E46
1.20E46
I ISEM6
I
4 0
100
200
300
400
500
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Time used [mi"]
Performance of JETTPlan when including the FH gain: As
in the previous case, a reference area has been used. This area contains 159 cells. However, it was chosen only to optimise on the centre part, containing 125 cells. The available spectrum has been 9 MHz (in GSM equal to 45 frequencies) [17]. Again the existing manually tuned and optimised frequency plan has been used for comparison. It has not been planned with separate bands of BCCH and
With FH the quality experienced by the mobile user is of course depending on the quality of all serving frequencies, i.e. from the quality of all the frequencies in the hopping sequence. Furthermore, the coding gain from FH should be reflected in the quality measure. Based on these two factors it has been decided to use the frame erasure rate (FER), or at least some quality measure tightly correlated
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to the FER (denoted 'pseudo FER') for graphical visualisation of FH networks. The FER is in GSM calculated after the decoding has taken place in the receiver and therefore the gain from FH is included. In this example the C/Z has been available, however the RXQUAL could also have been used. The mapping has therefore been carried out as in the example in Figure 7. C/Iatpixel no. frequency I :
x
C/I at pixel no. X frequency 2 :
FH (e.g. 'pseudo FER? C/I at pixel no. X ; ji-equency 3 :
C/!=7dB Figure 7. Example of mapping from 3 CII values p e r pixel (3fiequencies in serving cell) to one 'pseudo FER' per pixel.
The function able to map an arbitrary set of combinations of CII values (e.g. while hopping across 2, 8 or 12 frequencies, and moving at a speed of 3 or 50 km/h) has been found by using the link simulation tool described previously. Mapping tables have been made, each describing the relationship between one possible combination and the corresponding FER. To be able to evaluate the performance of new frequency plans, where the frequency and interference diversity gain has been included in the allocation process the accumulated FER statistics for a certain frequency plan is found. A typical example is shown in Figure 8, where an improvement in relative cumulative frequency is seen compared to an operating reference frequency plan. In this example a geographical area of approx. 100 km2 has been looked upon. For the frequency plan made with JETTPlan, 3.4 % of the pixels have a FER worse than 10 %, while in the reference case it is 6.6 % of the pixels.
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Figure 8. The cumulative frequency distribution of the pseudo FER p e r pixel of the new plan made by JETTPlan and an operating (reference)frequency plan.
VII. CONCLUSION The problem of resource allocation in a frequency hopping PCS 190O/GSM/DCS1800 type of network is considered. A model describing the gain from frequency hopping has been developed. Using this model the effect from frequency hopping is included directly in the allocation process. Furthermore, an algorithm denoted JETTPlun, doing exactly that has been developed resulting in an overall network quality increase, when compared to operating frequency plans. A second tool, for graphical visualisation of frequency hopping networks, has also been developed. Traditionally the worst C/Z or worst BER per pixel is shown graphically. This way of visualising frequency hopping frequency plans has improved significantly using a network quality measure tightly linked to the FER, which reflects the gain from frequency hopping.
ACKNOWLEDGEMENT We would like to thank Sonofon Denmark and Nokia Telecommunications for co-sponsoring the presented work. REFERENCES Cox D.C. and D.O. Reudink,, A comparison i f some Channel Assignment Strategies in Large Scale Mobile Communications Systems, IEEE Trans. On. Comm., VOL COM-20, N0.2, Feb. 1972. Giortzis A.I. and L.F. Turner, A mathematical programming approuch to the chunnel assignment problem in radio networks, IEEE VTC'96, Atlanta. Strang G., Linear algebra and its applicutions, third edition, Massachusetts Institute of Technology, USA, 1986. Kronestedt F. and M. Frodigh, Frequency plunning strategies for frequency hopping GSM, IEEE VTC, Phoenix, 1997. MSI PIC.,PlaNET version 2.7 - Softwure documentarion, June 1997. Nokia Telecommunications, NPSiX 3.2 Network Planning System product description, June 1998. Knudsen J.B. and A S . Nielsen, Soft Output Viterhi data receiver for Digital Mobile Communications, M.Sc.E.E. thesis, AUC June 1992. Wigard J., T.T. Nielsen, P. Mogensen and P.H. Michaelsen, Frequency Planning for Frequency Hopping Networks, COST 259, Duisburg, Germany, Sept. 23-25, 1998. GSM Recommendations (05.08), Digitul cellular telecommunicutions systrm; Radio subsystem link control, ETSI, Jan., 1997. Aarts E. and J. Korst, Simulated annealing and Boltzmunn Machines, Wiley, 1989. Lochtie G.D., C.A. van Eijl and M.J. Mehler, Comparison of energy minimising algorithms for channel assignment in mobile radio nerworks, IEEE Proc. of PIMRC'97, Helsinki, pp. 786-790. Smith D.H., S.M. Allen, S. Hurley and W.J. Watkins, Frequency Assignment: Methocls and Algorithms, NATO symposium: frequency assignment, Aalborg, Denmark, Oct. 98. Kunz D., Channel ussignment for cellular radio using neural nehwrks, In IEEE Trans. Veh. Tech., Vol. 40, No.1, Feb. 1991, pp.188-193.
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[16] Iversen V.B., Duta- og Teletrajkteori; Lcvrebog (In Danish), Inst. for Telekommunikation,DTU, Jan. 1997. [17] Nielsen, T.T., Wigard, J., Skjaerris S., Jensen C.O., Elling J., Enhancing Network Quality uJing Baseband Frequency Hopping, Downlink Power Control and DTX in a Live GSM Network, In Roc. of IEEE PIMRC 1998, Boston.
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