UMTS Optimum Cell Load Balancing for Inhomogeneous Traffic Patterns
Mario García-Lozano, Silvia Ruiz and Juan J. Olmos Department of Signal Theory and Communications
Universitat Politècnica de Catalunya (UPC) C/ Jordi Girona 1-3, D4, 08034. Barcelona (Spain)
{mariogarcia, silvia, olmos}@tsc.upc.es
Abstract— This paper addresses the problem of adjusting simultaneously electrical downtilt of Base Station antennas and the transmitted power assigned to Common Pilot Channel (CPICH) signals in the framework of a UTRA-FDD system. The proposed optimization procedure is not based in an isolated cell by cell analysis, but in a global scenario context by means of Simulated Annealing. Results show the convergence of the algorithm towards configurations that imply optimal system performance. Interference levels are minimized, effective cell load is reduced and therefore capacity can be increased.
I. INTRODUCTION With the deployment of UMTS networks, coverage, traffic
and QoS have become tightly coupled and must be treated as a whole in the radio planning process. Moreover, the use of new and more complex radio resource management (RRM) algorithms and the WCDMA system nature imply that a great deal of variables will have to be considered. These parameters influence the network performance simultaneously and a joint adjustment will not be trivial. Since efficient planning appears of outmost importance, different optimization techniques are being studied in order to increase system performance [1][2][3].
Two of the parameters with more influence on network capacity and RRM are the Common Pilot Channel (CPICH or pilot) and antenna downtilt angle. The CPICH is the channel that mobiles use to estimate channel conditions. It is also one of the inputs to cell selection and handover procedures. In fact, UTRA soft handover is based on a mobile assisted policy, the network orders the user equipment (UE) to add a base station (BS) to its active set (AS) according to quality measurements made by the UE on the CPICH [4]. Given this, it may be inferred that traffic can be balanced among the cells by means of CPICH powers variations. Modifications in the antennas downtilt also allow a controlled way of changing the cell shape. Initially, antennas were just simple radiant elements which could be mechanically tilted in order to reduce interference towards (or from) other cells but the introduction of remotely controlled electrical downtilt offers much more flexibility and introduces new possibilities in UMTS radio
planning. Moreover, mechanical tilt shows several drawbacks since it is only effective in the forward direction and has no effect on side radiation, it also moves rear lobe upwards.
Both parameters have a direct influence on the effective cell shape, but nevertheless their effect is noticeable different. Whereas pilot variations just imply a change in the power assigned to a control signal, downtilt variations have a major impact upon the power UEs have to transmit, the radiated interference and also interference levels listened from other cells.
The use of equal pilot powers and a fixed antenna downtilt angle can be predicted intuitively as the best solution in terms of interference in an ideal scenario with perfectly uniform user distribution, equal path loss conditions and only one service. This situation would imply a homogeneous distribution of traffic among all the cells. However, unbalanced traffic distributions in realistic environments would lead some groups of cells towards blocking situations whereas other ones could remain with a much lower load. Note that unbalanced traffic situations are produced by both a non-uniform user distribution and a non-uniform service distribution. Also, it must be noted that UEs always transmit to the BS that requires less power from them. However, the AS is finite and the included base stations (the best servers ones) are not always the best options since in UTRA-FDD environments, interference level has a great impact in the power a UE has to transmit. It can be predicted the existence of a group of CPICH powers and downtilt angles so that traffic is effectively equalized among the cells, the load is balanced and a higher capacity can be achieved. Thus, it is important to obtain a way to adapt the CPICH signals and antennas downtilt to the contour conditions. These modifications should lead the system to an optimal or quasi-optimal configuration in which the load is minimized (mobiles will be connected to the BSs that require less power from them).
The organisation of the paper is as follows: in the next section the problem is defined and an optimization strategy based on Simulated Annealing is proposed. Next, the simulation conditions are described. In the fourth section, results will be shown and analysed. Finally, the last section is devoted to conclusions and further possible works.
This work has been founded by the Spanish Research Council CICYT-
FEDER through the projects TIC2001-2222 and TIC2003-08609.
A. Problem Definition In order to tackle the problem, it has been defined as a
resource allocation one. That is a set of possible CPICH powers and downtilt angles has to be assigned to BSs so that a cost function is minimized. Actually, the election of the cost function will play an important role on final results.
Capacity is aimed to be maximized but without loss of coverage, so both should be considered jointly. Moreover capacity could be uplink (UL) or downlink (DL) limited depending on the scenario. Nevertheless, maximizing capacity according to UL requirements (in an UL limited system) could jeopardize DL performance unless it is also taken into account in some way. In this work a single objective has been defined with a set of constraints to be respected by the final solution. After statistical analysis by means of Monte Carlo simulations the system showed to be UL limited. Consequently the aim will be to reduce the UL load factor (1) of all BSs jointly. Note that NT(m) is the total received power (signal, interference and noise) and N(m) is the thermal noise, both measured at BS m.
)()()()(
mNmNmNm
T
TUL
−=η (1)
Given this, equation (2) was used as cost function. Note that M is the number of BSs in the system and S is the number of snapshots considered during the optimization process.
∑∑= =
=S
s
M
1mUL(m)
Scost
1
1 η (2)
As stated before, different constraints were also imposed:
1) The percentage of users transmitting in degraded mode in a certain cell (i.e. not achieving the Eb/N0 target) is not allowed to be greater than 5%.
2) Within the system, the percentage of pixels out of coverage should be under the 5%. A connection is considered out of coverage when the best CPICH Ec/I0 is less than 12 dB.
3) The number of BS under maximum DL capacity conditions cannot be greater than one. Maximum DL capacity is found when one BS has to transmit at its maximum power and therefore congestion control algorithms are working on.
Moreover, the ranges of possible CPICH powers and donwtilt angles have to be established according to different restrictions that should be defined in a previous phase of the planning process:
1) The maximum eligible power must avoid illogical situations, in which DL capacity could be jeopardized.
2) The minimum eligible power must guarantee a desirable coverage when all pilot powers are set to this value. Note that this restriction does not guarantee the second constraint since heterogeneous traffic distribution and pilot pollution could generate dead spots as well.
3) Minimum downtilt angles will be set to zero and maximum values will be adjusted according to typical electrically controlled commercial antennas.
Finally, steps between consecutive pilot powers and downtilt angles have to be chosen in order to define the number of available options within each set. An excessive low step implies a wide range of solutions with the same cost and an increase in the dimension of the problem and, thus, longer simulations. On the other hand, a big step could produce a loss of solutions that might have been quasi-optimal ones (interesting minimums in the solution space). So a first trade-off between the quality of solutions and computing time arises.
B. Simulated Annealing adaptation Mathematically speaking the problem has been defined as a
combinatorial NP-complete one. That means the resolution time will be exponential in the problem size and a heuristic approach will have to be used unless the problem is trivial. Simulated Annealing (SA) is one of the techniques that has gained more popularity during the past years because it has been successfully applied to different 2G problems.
SA consists of a random search algorithm that efficiently explores the solutions space (Table 1). The generation of a new solution (point 3) consists of a slight perturbation over the current one. This modification is done according to two random selections: one pilot power accomplishing the exposed restrictions and one BS. The new cost value is recalculated and constraints are evaluated. Whenever a constraint is not accomplished the solution is rejected. If constraints are accomplished the so-called Metropolis Criterion is evaluated. This is one of the key points because, unlike simple local search, the algorithm explores the solution space without being trapped in local minima. Movements towards worse solutions are allowed with a certain probability controlled by a parameter called temperature. The higher the temperature is, the higher the probability of accepting a worse solution is. Thus, previous to the normal execution an initial appropriate value must be found. This has been done by means of a heating process, the algorithm is executed and the temperature is gradually increased until the ratio of acceptance r (number of accepted solutions/number of proposed solutions) reaches 90%. This also guarantees independence between the initial solution and the final one. The algorithm finishes when r is less than 1%.
The quality of the final solution depends on the cooling strategy (point 7), the termination criterion (point 8) and the equilibrium condition (point 6). Note that the equilibrium condition states the number of iterations that must be carried out before the temperature value is updated, this will be called a cycle. Once more time, a trade-off between the speed for the convergence and the quality of the solution arises. In this work the temperature has been updated according to (3) because, unlike typical geometric cooling, it has been shown to preserve the convergence theory towards the global minimum as much as possible [5].
1
3)1(
1−
+⋅+⋅=
σδlnT
TT oldoldnew
(3)
Being δ an adjustable parameter that allows controlling the cooling speed (typically 0.1) and σ is the standard deviation of the cost of accepted solutions during the period of time in which the temperature was Told. This dependence on σ implies
1. Obtain initial solution S and temperature T. 2. C ← cost of S. 3. Generate new solution S’. 4. C’ ← cost of S’. 5. Accept S’ as the current solution S with probability p:
p=exp[(C-C’)/T] if C’≥C p=1 if C’<C
6. If equilibrium condition has not been reached, go to 3. 7. Update temperature T. 8. If termination criterion has not been reached, go to 3.
that if certain equilibrium was not reached with Told the decrease in temperature will be low and vice versa. That means the algorithm could react to some extent if the equilibrium condition had not been accurately chosen. Actually, in this work this condition was empirically set and according to the number of eligible solutions. The larger the number of BS and the number of eligible powers, the longer the loop with the same temperature.
Regarding downtilt optimization, it must be pointed out that an uncoordinated increase of downtilt angles and a reduction of pilot powers could lead to a reduction of soft handover regions, and in the limit to no overlapped cells or the generation of dead spots. That is why downtilt angles are not randomly chosen, which produces a lot of invalid solutions not accomplishing the constraints and, therefore, an excessive waste of computational time. The initial algorithm has been modified so that intelligent downtilt optimizations are run several times per cycle. These consist in choosing one random base station and evaluating all possible angles. The solution that implies a minimum cost is directly accepted without evaluating the Metropolis criterion. The number of downtilt optimizations within a cycle must be adjusted taking into account the cycle’s length. We set this value so that there were at least ten downtilt optimizations per cycle. Thanks to this approach it will be shown that the search procedure is intelligently guided around the solutions space and configurations with a better performance are found.
III. SIMULATION APPROACH A static system level simulator has been developed in order
to cope with the analysis. The simulated scenario is a 3GPP based, urban and macrocellular one [6], with an area of 5.5x5 km and 42 cells. The deployment scheme is done according to a quasi-regular cell layout. Since BS locations are very dependant on the availability of sites and ideal positions cannot be usually used, in the simulator, ideal sites are shifted by a bidimensional uniform random variable (square of 100x100 m). Antenna height was also randomly chosen between 25 and 35 m.
Users will be scattered according to a heterogeneous density distribution (Figure 1) so there will be certain limited areas in which more mobiles units will be simultaneously operating. Table 2 shows the model of multimedia traffic that has been considered. It includes typical conversational services as well as data services with their percentage of UEs in the system, required Eb/N0 for both links and the maximum transmission power that can be assigned in the DL.
Classical COST231-Hata propagation model for suburban areas has been used [7] considering a 2GHz carrier. Regarding shadowing model, as it depends on position and several UEs will be operating close, the two dimensional model proposed in [8] has been used. Values have been calculated by means of a two dimensional filtering process. A correlation distance of 20m and a standard deviation of 10 dB have been assumed. Correlation among shadowing from different base stations has also been considered with a correlation coefficient 0.5 [9].
The simulator assumes that electrical downtilting can be used by all base stations in the system. Moreover, in order to obtain realistic results, a diagram pattern from a commercial antenna has been used. Its main features are a gain of 15.85dBi and 65 and 6.7 degrees of horizontal and vertical beam width respectively. Too theoretical diagram patterns would prevent the simulator to consider first nulls and second main lobes effects [10]. Table 3 shows other simulation parameters.
Without optimizationPilot optimizationPilot & Tilt optimization
Figure 2. Mean Uplink Load Factor before and after optimization, with and without considering downtilt adjustment.
IV. RESULTS In order to test the technique, different configurations
where run. Different cycles length were tested: 75, 750 and 4000 samples, being the last one the default value in those figures in which none is indicated. The algorithm was also executed with downtilt optimization module (Option P&T) and without downtilt optimization module (Option P).
From Figure 2 it can be seen the mean uplink load factor for all the cells in the system before and after optimization. Results show that the proposed technique achieves noticeable reductions. The number of cells with a load factor higher than 0.8 is reduced from 18 to 4 when both downtilt and pilot powers are optimized and to 7 when only pilot powers are considered. The algorithm is able to find a configuration of parameters so that interference is minimized and, consequently, capacity can be increased. It also can be seen that running the downtilt module does not guarantee a reduction in the 100% of cells when compared to Option P. However, as it will be seen later, the achieved cost value was better.
Figure 2 also outlines the heterogeneity of the scenario since differences in load factors among the cells are quite sharp. Note also that those cells with a particularly low load are the border ones which cover a smaller region because the specific dimensions of the simulated area. Border effects are not important because the interest is not on the absolute values but on the relative ones. Moreover, wrapping around the scenario could lead the algorithm to instability.
Figure 3 shows a comparison of cost evolution for both optimization options. Different numbers of samples per cycle have been considered as well with Option P&T. A continuous line indicates the original cost value (equal pilot powers and downtilt angles equal to zero). It can be seen the fast increase of cost during the search of the initial temperature, this reveals the existence of configurations which would lead the system to very poor performance. Once the temperature has been found, a downward trend appears until the algorithm finishes. According to the curves, Option P optimizes the system but the solution is improvable if downtilt optimization is added. Figure 4 reveals how it can help the Annealing process. Once the temperature is low enough the algorithm starts to converge towards a certain solution. However, thanks to local downtilt
Figure 3. Cost evolution for different optimization options.
20.7
20.8
20.9
21
21.1
450 500 550 600 650 700 750Samples in a cycle
Cos
t
Downtilt optimization
Trapped in local minimum
Figure 4. Benefits of intelligent local downtilt optimization
optimizations the algorithm is guided outside possible relative minimums and it is positioned in “better areas” of the solutions space. As a result, better final configurations are found. Paying attention to Option P&T curves, it can be observed that there is a gain when the cycle is longer, which is consistent with the fact that SA tends to the absolute optimum when
Figure 5. Mean DL transmission power before and after optimization
0
10
20
30
40
50
60
70
80
90
1 2 3Active Set Size
% o
f use
rs
Without optimizationPilot & Tilt optimization
Pilot optimization
Figure 6. Changes in soft handover areas because of optimization
computational time tends to infinite. Shorter simulations (75 samples/cycle) were also run and they gave results similar to Option P, though in a time around 50% shorter.
Although the cost function was defined according to UL load, UL and DL are not independent and users redistribution will have an impact on the BSs transmitted powers. Moreover, DL performance was controlled by means of constraints. Figure 5 shows the gain that is achieved in the DL transmitted power. Note that this is because of load balancing (mean DL powers assigned to traffic channels are reduced) but also because several CPICH powers have been reduced (especially in highly loaded cells) consequently less power is devoted to signaling.
Finally, the impact upon soft handover areas has to be evaluated too, because of the reasons that were previously mentioned. Figure 6, shows the percentage of users with a certain Active Set size. When only pilot powers are optimized, soft handover areas are increased, this is logical since in order to balance the load the UEs just need to include new BSs and they will adjust their transmission powers according to the one
that requires a lower level. On the other hand, when downtilt is introduced an expected decrease is obtained. Although the decrease does not jeopardize soft handover performance at all, new constraints concerning this topic could be also introduced.
V. CONCLUSIONS AND WORK ING PROGRESS Along this paper an optimization technique based on SA
has been introduced in order to find optimal configurations of pilot powers and downtilt angles. The algorithm is able to find solutions so that interference levels are minimized, the load is reduced and a higher capacity can be achieved. Different results have been presented and different optimization options have been compared.
As SA is rather time consuming it cannot be used in real time RRM by any means. However our analysis is focused in the optimization of defined patterns reflecting real situations. For example if a BTS fails, the system could automatically change the pilot power and downtilt of the surrounding cells to cope with the extra traffic. Also it is well known from 2G systems that there is some kind of periodicity in the traffic in a given scenario, as hot spots in weekends in the cells covering commercial areas, traffic increase from 6:00 to 8:00 pm also in commercial areas, a given temporal sequence of saturated cells due to concerts or football matches, more traffic in cells covering highways and main city roads on starting/ending weekends, etc. For each pattern the optimum BSs configuration can be calculated by using the proposed technique. By means of look-up-table based methods, neural network pattern recognition systems or just simply activated manually from the O&M subsystem the optimal configuration parameters can be applied an improve the network performance on a real time basis.
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