Achieving Higher HSDPA Performance and Preserving R99 Soft Handover Control
by Large Scale Optimization in CPICH Coverage Planning
Lei Chen∗ and Di Yuan∗
∗Department of Science and Technology, Link ¨oping University, SE-601 74 Norrk ¨oping, Sweden.
Emails: [email protected], [email protected]
Abstract
Common Pilot Channel (CPICH) power is a crucial parameter in coverage planning of today’s UMTS networks that implement both HSDPA and R99 services. Adopting a non-uniform allocation of cell CPICH power and minimizing its amount necessary for coverage, the resulting power saving significantly improves HSDPA performance. At the same time, it is vital to have the desired level R99 soft handover, which is heavily influenced by CPICH. In this paper, we demonstrate how large scale optimization can deal with both tasks. Our approach focuses on enhancing cell-edge HSDPA data rate, subject to requirements of CPICH coverage and the level of R99 soft(-er) handover. We present a solution algorithm that optimizes CPICH allocation for HSDPA performance, and, in parallel, offers accurate con-trol of R99 soft handover. Experimental results for large and realistic network planning scenarios demonstrate the benefit of our optimization approach.
1. Introduction
First introduced in 3GPP Release 5 [1], High Speed Downlink Packet Access (HSDPA) is under rapid deploy-ment worldwide. HSDPA impledeploy-ments adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ) and fast scheduling. These enable lower link latency and significantly higher data rates than earlier UMTS releases, making HSDPA a key technology for the evolution of mo-bile broadband. Deploying UMTS/HSDPA network requires planning and optimization – a topic that is attracting research attention [2], [3], [4], [5].
For HSDPA, transmit power is an important parameter for network performance. Note that, for power-controlled services in R99, transmit power is adjusted so that it does not go over the amount needed for reaching service-specific signal to interference and noise ratio (SINR) target. In contrast, allocating additional power to HSDPA raises SINR and leads to higher data rate. Thus in networks having co-existing HSDPA and R99 that share power at base stations, HSDPA directly benefits from power saving made on other channels.
In this paper we rigorously investigate the effect of com-mon pilot channel (CPICH) power on HSDPA performance enhancement by means of large-scale optimization. CPICH is used by a cell to broadcast its pilot signal. Since user equipments (UEs) use CPICH for channel estimation and cell selection, CPICH defines the coverage patterns of cells, and hence has large influence on network performance. A rather common setting is to uniformly allocate a con-stant proportion (about 10% - 15%) of the total power to CPICH [6]. From a resource consumption standpoint, however, uniform CPICH power performs poorly. Indeed, it has been concluded in earlier research that, by non-uniform CPICH power allocation, power consumption can be reduced significantly without sacrificing service coverage [7]. More-over, non-uniform CPICH can improve load balancing [8]. For HSDPA, adopting non-uniform CPICH and minimizing its amount necessary for coverage make additional power available to the HSDPA service, and thereby yield higher performance. What’s more, additional savings are gained on some other common channels (CCHs), of which the power consumed is in proportion to CPICH power.
For R99, a potential pitfall of non-uniform CPICH is that the performance of soft(-er) handover (SHO) may degrade and drop below the desired level. SHO allows a UE to be connected with multiple cells simultaneously, resulting in smoother handover as well as higher signal detection and antenna diversity gains. Under uniform CPICH power, it is relatively simple to predict SHO regions, which are solely determined by signal propagation. With non-uniform CPICH power allocation, one has to consider the CPICH power levels of multiple cells in order to find out the strongest and second strongest received CPICH, and thereby whether or not a UE can be in SHO.
To summarize, for networks offering both HSDPA and R99 services, optimizing non-uniform CPICH for higher HSDPA performance must, at the same time, preserve a desired level of R99 SHO.We show how large scale optimization can deal with both tasks in network planning. Our approach focuses on enhancing HSDPA performance at cell-edges – locations that typically have lowest data rates. The side constraints are CPICH coverage and adequate level of R99 SHO. It should be remarked that, in addition to providing a framework of CPICH optimization targeting at both HSDPA performance
and R99 soft handover, a key contribution of the paper is the design of a solution algorithm, based on Tabu Search, that can deal with large-scale planning effectively and
time-efficiently. Large scale network planning is challenging and there is no model can finish the planning time efficiently. The author in [9] propose an optimisation model from which solutions can be obtained for small networks only. Our approach in this paper overcomes the limitation of the model and can deal with large scale networks time efficiently. In Section 4, we demonstrate the significant benefit of our approach by experimental results for large and realistic network planning scenarios.
2. System Model
2.1. Preliminaries
Consider a UMTS network implementing both R99 and HSDPA, and denote the set of cells byC. The total power available in cell i is denoted by ptot
i . The service area is represented by a grid of a large number of test points. This is a common practice in UMTS network planning[10]. We use J to denote them and gij the total gain between the base station antenna of celli and test point j. This parameter is obtained by measurements or propagation prediction. In our planning framework, possible levels of CPICH power is modeled by a discrete set{p1
i, p 2
i, . . . , pli, . . . , pLi, i ∈ C}. R99 soft handover requirement is specified by parameterµ. In this paper, µ denotes the minimum percentage of test points that must be in SHO state, summed over the entire service area. Note that whether or not a test point is expected to be in SHO depends on cell coverage patterns, which, in their turn, are determined by CPICH power allocation.
2.2. Coverage
In order to provide coverage, each test point should receive at least one CPICH with sufficient strength. Denote byppiloti the CPICH power of celli, test point j is covered by i if the carrier to interference ratio Ec/Io meets a thresholdγc [11]. Ec/Io= ppiloti gij k∈C ptot k gkj+ ν0 ≥ γc. (1)
To be consistent with the power-sharing assumption in Section 1, we assume that cells operate at full power in (1), thus interferences from all the cells (own cell interference also included since Ec/Io is detected before signal decod-ing) at pointj isk∈Cptot
k gkj. Parameterν0is the thermal noise. Typically, the value of threshold γc varies between -20 dB and -18 dB, depending on UE and network. Point j might be covered by more than one cell, in that case it chooses the cell giving the strongest signal as the best server.
From (1), one can easily calculate, among the possible CPICH power levels, the minimum level that celli has to use in order to coverj. Denote this power by plij
i . To ease the presentation, we will henceforth use indexlijas a short-hand notation for plij
i . For later use, we denote by Cj the possible covering cells at pointj. Since the CPICH power level is limited by L, a cell can only potentially cover a limited number of test points. We useJi to denote this set for celli. We sort the elements of Ji andCj in ascending order of the CPICH power levelslij.
Note that sometimes a test point can be covered by one cell only. (Typically, such a point is very near to the base station antenna of the cell but far away from other antennas.) Suppose celli is the only possible covering cell at test point j, then the CPICH level of i must be at least lij, otherwise full coverage can not be achieved. By considering test points having one potential covering cell, we can derive a parameter lbi– all power levels below it can be discarded for celli in planning CPICH power.
2.3. Cell-Edge HSDPA Power Requirement
For the High Speed Downlink Shared Channel (HS-DSCH), its SINR for a single-antenna Rake receiver can be expressed as [11]: SIN Rij= SFhs× phs i (1 − aj)Ior+ Ioc+ ν0 . (2) Where phs
i is the power allocated to HSDPA in cell i, SFhsis the spreading factor (equals 16),aj is the orthogo-nality factor at pointj. IorandIocare the interference from the own cell and other cells respectively. The latter amounts to calculatingk∈C:k=iptot
k gkj. Provided that cell i is the best server at pointj and rearranging (2), we can derive the HSDPA power requirement for pointj with its best server i by the formula below. Note that for a specific point, Ior andIoc only depend on the point itself since we assume the cell is running with full power.
phs ij =
SIN Rij× ptoti (1 − aj+IocIor+ν0) SFhs
. (3)
We model HSDPA service availability by a minimum SINR value. For example, a throughput of 100 Kbps cor-responds to a SINR of 2.5 dB, if the UE supports 5 channelization codes [11]. By choosing a SINR target, we can calculate from (3) parameterphs
ij – the (minimum) power necessary to make HSDPA service available to test pointj with celli as the best server. We refer to this parameter as the power requirement. Among all the test points that are served by a cell, the one with the highest HSDPA power requirement defines the cell-edge. We denote this cell-edge HSDPA power requirement byphs
i . Note thatphsi depends on CPICH power allocation.
2.4. SHO
UEs measure CPICH signals from cells. If the signal levels satisfy the condition of SHO, the UE can connect to other cells in addition to the serving cell. Typical two-way soft handover is demonstrated in Figure 1.
Suppose celli is the best server and cell k is the second best server at point j, j can be in SHO (two-way) if the received SINR difference from these two cells stays within the SHO threshold (3dB in our simulation).
(F,R (F,R
G% G%
6+2 ]RQH
8(
Figure 1: An illustration of the SHO condition.
SHO may occur only if two cells overlap with each other. We refer to potentially overlapping cells as neighbors. For cell i, the neighbor cells are derived and denoted by N eibi(i ∈ C).
3. SOLUTION ALGORITHMS
3.1. Problem Statement
Based on the previous discussion, we define an objective function, taking into account two aspects. First, the function targets at reducing the power required to provide satisfactory HSDPA service at cell-edges. The second part of the function minimizes the total CPICH power necessary for coverage. These two elements are put together to reflect the fact that power saving from CPICH is re-allocated to HSDPA.
obj : i∈C
(ppiloti + phs
i ). (4)
Thus the optimization problem is to allocate the CPICH
power used by cells to minimize (4) subject to CPICH coverage and R99 SHO constraints.
Given a CPICH power allocation, the cell in Cj having the highest power at point j serves as the best server of j, and the point in Ji that needs the highest HSDPA power defines the cell-edge for celli.
Since the coverage problem in UMTS network planning is NP-hard, we develop heuristic algorithms to find high quality solutions in reasonable computing time. We first develop a randomized greedy algorithm to find an initial solution. Next, a Tabu Search algorithm is developed by iteratively increasing and decreasing cell CPICH power. Because a high value of µ turns out to be too stringent if the search is conducted for feasible solutions only, we adopt the notion of relaxation.
3.2. Greedy Search
Although uniform power allocation can be used as an initial solution, we develop a randomized greedy search al-gorithm to find the initial solution in order to save computing time.
Starting at the uniform power allocation and an empty cell setB = Φ, we randomly select a cell i (i ∈ C\B) to check if the CPICH power can be decreased. If the coverage and soft handover constraints are both kept when we decreaseppiloti by one level, the algorithm will proceed by decreasing the power level of this cell and start from a new cell. Otherwise, if either coverage or soft handover constraint is violated, celli will be put into set B, thus B = B∪{i}. The same procedure is repeated until all the cells are in set B. The output solution will be used as an initial solution for the TS algorithm.
3.3. Tabu Search
As a meta-heuristic algorithm, TS utilizes both intensifi-cation and diversifiintensifi-cation procedure. Intensifiintensifi-cation utilizes short-term memory by means of local search and tabu list to find the best solution in a restricted region. Tabu list is used to memorize the recently visited solutions and forbids the search to visit them within a certain time. Aspiration criteria is used together with tabu list for the search to visit solutions still in tabu, provided that the solution is better than the best we have got. Diversification utilizes long-term memory to redirect the search to a new unvisited region if intensification procedure can not find better solution in a pre-defined number of steps.
3.3.1. Neighborhood Design.Neighborhood design is im-portant in TS algorithm. For the problem we discussed before, if we increase one of the cell CPICH power by one level, the cell coverage expands and it may cover more test points. The coverage pattern changes and this may allow some neighbor cells to lower their power levels. Algorithm 1 illustrates the neighborhood generation procedure. We denote a solution by p which includes the power allocation pi(i ∈ C) with level li and the resulting network condition including covering cellsCj(Cj⊆ Cj, j ∈ J ), soft handover patternSj(j ∈ J ), best server pattern Bj(j ∈ J ) and cell-edge HSDPA power requirementphs
i (i ∈ C). We start with an empty setN (p) which denotes the neighbor solutions of p. For all the cells, first we increase the cell CPICH power by one level. Then, we check all the neighbor cells of this cell, and try to decrease the power levels of the neighbor cells as much as possible, provided that the coverage is kept. Coverage and SHO will be updated after each change of power. After this, we get one neighbor solution p and add it toN (p). Note that when we generate the neighborhood,
only coverage is considered, therefore, some neighbor solu-tions might be infeasible because of insufficient SHO. We will discuss this shortly. Since in practice, there will be a constant number of neighbor cells for each cell, the size of the neighborhood grows linearly with the number of cells.
Algorithm 1 Neighborhood Generation 1: N (p) ⇐ Φ 2: fori ∈ C do 3: p ← p 4: pi⇐ pli+1 i 5: forj ∈ Ji do 6: update Cj, Sj, Bj, phsB j 7: end for 8: forh ∈ N eibi do 9: forj ∈ Jh do 10: while|Cj| > 1 do 11: ph⇐ pli−1 h 12: update Cj, Sj, Bj, phsB j 13: end while 14: end for 15: end for 16: N (p) ⇐ N (p) ∪ {p} 17: end for
3.3.2. Local Search. Algorithm 2 illustrates one step of local search. The relation between the changing of HS-DPA power requirement, SHO pattern and the cell CPICH power is not straightforward. Increasing the power may not necessarily increase the number of test points in SHO, the same for HSDPA power requirement. During step 4 to 15 in Algorithm 1, SHO and cell-edge HSDPA power requirement will be recalculated according to the power change. During local search, objective value will lead the algorithm to move to the neighbor solution. We maintain two tabu lists namely T Ii(i ∈ C) and T Di(i ∈ C). Cells in T I are not allowed to increase their power levels and cells inT D can’t decrease their power levels. We choose tabu length 30 for T I and 15 for T D. A move will change the tabu lists by entering cells from the previous solution into the lists. As when SHO requirement is high, it turns out to be a very stringent constraint and TS can hardly move to good solutions. We apply the notion of relaxation and penalty to deal with this issue. For a solution with SHO requirement violated, a penalty is added to the objective function.
obj : i∈C
(phsi + p pilot
i ) + λ(µ|J | − |S|). (5) |S| is the current number of points in SHO. The penalty depends on the the number of additional test points needed to satisfy the SHO requirement and coefficientλ (0.09 in our simulation). Even if the best solution in the neighborhood is with SHO requirement violated, TS will still move to
Algorithm 2Tabu Search
Input: pini,T I length,T D length,λini,α,β,µ Output: popt 1: popt⇐ pini 2: pstart⇐ pini 3: objlocal⇐ ∞ 4: λ ⇐ λini 5: for p ∈ N (pstart) do 6: if|S| < µ|J | then 7: obj =i∈C(ppiloti + p
hs
i ) + λ(µ|J | - |S local|) 8: else
9: obj =i∈C(ppiloti + p hs i ) 10: end if
11: ifobj < objlocal then
12: objlocal ⇐ obj 13: plocal ⇐ p 14: end if
15: end for
16: if|objlocal| < µ|J | then
17: λ ⇐ αλ 18: else 19: λ ⇐ βλ 20: end if 21: fori ∈ C do 22: ifpstart i > pi then 23: T Ii⇐ T I length 24: ifT Di> 0 then 25: T Di⇐ T Di− 1 26: end if 27: else ifpstart i < pi then 28: T Di⇐ T D length 29: ifT Ii> 0 then 30: T Ii⇐ T Ii− 1 31: end if 32: end if 33: end for
34: ifobjlocal< objopt then
35: if|Slocal| ≥ µ|J | then 36: popt⇐ plocal 37: pstart⇐ plocal 38: else 39: pstart⇐ plocal 40: end if 41: end if
the solution but scale up λ with coefficient α (1.03 in the simulation). For solutions satisfying SHO, no penalty is imposed, and λ will be scaled down with coefficient β (0.97 in the simulation). So, when infeasible solutions continuously appear,λ will keep increasing and penalty will dominate more in the objective function. This will eventually lead TS to a feasible solution. Relaxation will help TS find a
better feasible solution by going through infeasible regions. Even with relaxation, it may still take too long for a better solution to appear. Diversification is utilized in the algorithm to direct the algorithm to another region. We implement diversification by increasing randomly half of all the cells’ CPICH power levels by one step.
4. NUMERICAL RESULTS
We conduct simulation of the algorithm on five networks from medium to very large ones including both synthesized and real planning scenarios. The algorithm is implemented in C++ and runs on HP Compaq 8510p laptop with 2.4GHz Intel Core2 Duo processor and 2G RAM.
4.1. Test Networks and Scenarios
Table 1 summaries the test network statistics and Table 2 shows the parameters we use for the simulation.
Table 1: Network Statistics.
Net Cells
Grid Uniform Average Maximal point point grid CPICH HSDPA SHO
size (m2 ) size (points) (W) (W) % N1 140 9409 40*40 100*100 1.4 5.33 29.99 N2 203 19088 40*40 140*140 1.7 6.60 41.51 N3 255 21678 40*40 150*150 1.5 6.13 38.29 N4 148 22500 50*50 150*150 2.5 10.66 37.28 N5 140 62500 20*20 250*250 2.1 10.57 31.61
Table 2: Simulation Parameters.
Net
Therm CPICH Orthogonality Total noise threshold factor Tx Power
(dB) (W) N1 1e-13 -18 0.6 15 N2 1e-13 -18 0.6 15 N3 1e-13 -18 0.6 15 N4 1.5488e-14 -20 {0.327,0.633,0.938} 19.9526 N5 1.5488e-14 -20 {0.327,0.633,0.938} 19.9526 N1 to N3 are synthesized networks by placing a number of sites randomly over an area and generating isotropic path-loss predictions by the modified Hata model. The other two are real networks provided by EU project MOMENTUM [12]. N4 is a planning scenario for city Berlin and N5 is for Lisbon. Uniform power takes a constant portion of the total power. We use 2.5dB for the cell-edge HSDPA SINR threshold. Average cell-edge HSDPA power requirement and maximal SHO in Table 1 are derived under uniform power allocation.
4.2. Simulation Results
We enforce a total search limit of 2000 steps. Every 300 consecutive non-improving moves will trigger diver-sification. We study the SHO rates (µ) of 20%, 25% and 30% (29.99% for N1 since it is the maximal value) of the test points and the results are presented in Table 3. In the table, average cell-edge HSDPA power requirementphs i has already taken into account CPICH power saving. The
next column (Diver) shows the number of diversification. Final SHO rate is the soft handover rate with optimized CPICH power allocation, it might not be exactly the same as the SHO requirement. It is quite obvious from the results
Table 3: Simulation Results with different SHO requirements
Net SHO Average Average Diver Final µ ppiloti phs i SHO (%) N1 0.2 0.83 2.27 0 20.06 0.25 0.85 2.33 5 25.00 0.3 1.02 4.36 5 30.00 N2 0.2 0.92 4.10 3 21.79 0.25 0.92 4.13 3 25.08 0.3 0.97 4.30 0 30.00 N3 0.2 0.89 4.21 2 20.77 0.25 0.92 4.31 4 26.03 0.3 0.98 4.52 2 30.00 N4 0.2 0.94 6.02 1 22.00 0.25 0.99 6.25 3 25.00 0.3 1.23 7.07 6 30.00 N5 0.2 0.92 6.72 0 22.71 0.25 0.94 6.86 2 25.01 0.3 1.34 8.46 4 30.01
that with higher SHO requirement, both CPICH power and HSDPA transmit power grow. So, there is a trade off between the power consumption and SHO requirement. Figure 2 shows the comparison of CPICH power consumption and cell-edge HSDPA power requirement for both uniform and optimized CPICH power allocation.
1 1 1 1 1 8QLIRUP 7DEX 7DEX 7DEX A ve ra g e C PI C H p o w e r(W ) 1 1 1 1 1 A ve ra g e c e ll-e d g e H S D P A p o w e r re q u ir e m e n t (W )
Figure 2: Comparison of CPICH power and HSDPA Power Re-quirement
As can be seen from the results, CPICH achieves sig-nificant decrease. Up to 50% of power can be saved from control channel in most of the cases even with quite high soft handover requirement. Moreover, by reallocating these power saving to HSDPA transmit power, cell-edge HSDPA power requirement achieves significant drop, up to 30% in most of the cases. For city Berlin, average cell- edge HSDPA power requirement drops by 35% under 25% SHO requirement.
For the network performance, data throughput can be denoted by the SINR at the test points. After we reallocate the CPICH power saving to HSDPA transmit power, SINR is improved all across the planning scenario. Figure 3 shows the SINR improvement at test points. Lines in the graph represent antenna locations and directions. As can be seen from the graph, up to 2.5dB improvement of SINR can be gained. In average, we obtain about 1.3dB improvement of
Figure 3: HSDPA performance improvement (dB)
SINR for this planning scenario which is quite beneficial for the data throughput.
5. CONCLUSIONS AND FUTURE WORKS
We have presented a solution algorithm based on Tabu Search to improve the HSDPA performance of UMTS/HSDPA networks while maintaining the R99 SHO service. By allocating the CPICH power non-uniformly, sig-nificant power saving can be achieved from control channel. By reallocating power saving to traffic channel, network performance achieves improvement. The algorithm targets at large scale network planning and optimization. Simulations on both synthesized networks and realistic networks show significant power saving and performance improvement.
One possible extension is to include additional opti-mization parameters like antenna configurations to further enhance the HSDPA performance. Another very interesting extension is to include HSUPA which supports soft handover into the planning framework.
Acknowledgment
The authors wish to thank Dr. Iana Siomina, Ericsson Research, Sweden, for her discussion and help. The work of the first author has been financed by CENIIT, Link¨oping Institute of Technology, Sweden, and the Swedish Research Council. The work has been carried out within European COST action 2100 and FP7 project IAPP@Ranplan.
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