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HUAWEI TECHNOLOGIES CO., LTD.
www.huawei.com
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152. DOI: 10.15514/ISPRAS-2016-28(6)-10
Automatic Analysis, Decomposition and Parallel
O p t i m i z a t i o n o f L a r g e
H o m o g e n e o u sN e t w o r k s
ISPRAS Open 2016
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X.
S1
A0
Signals of sector antennas A0 – A7
A1
A2
A3
A4
A5
A6
A7
Homogeneous Networks
Element Crossroad Switch Antenna
Optimized integral index Average speed of traffic Average power of
prevalent radio signalCorrelation formula for 2 elements
1 / (1 + [elements quantity on the shortest path]) 1 / (distance between antennas)
WirelessWired
Communication Networks
Road network
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Background: Sector Planning with full optimization
Graph-based representationHomogeneous network is represented as a weighted complete graph, where
•each vertex corresponds to network element •each edge has weight equals to correlation between correspondent elements
Optimization loop:1. Decomposition of network into
subnets by the rule of minimal sum of crossing edges weights
2. Parallel optimization of subnets
3. Update of network parameters
Main drawback:Crossing edges are ignored => poor accuracy
Optimization ofall parameters
DECOMPOSITION
While stopping criterion isn’t met
Thread
Thread
Full network
2
- SubnetsElement
Agenda-
1 2
1
11
1
1
1 2
2
22
2
2
UPD
ATE
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Idea 1: Alternative splitting
OptimizationWhile stopping criterion is not met
Thread
Thread
Thread
Thread
Split by split
Advantage: All crossing edges are taken into account
Discard light edges under threshold
Alternative splits
Full network
Reduced network
1 2 3 4
1
2
3
4
1
2
34
1
2
3
4
- Subnets
ElementAgenda-
1 23 4
UPDATE NETWORK
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
COMBINE NON-OVERLAPPING SUBNETS
Idea 2: Independent optimization
Advantage: Parallel optimization of the fully independent elements
Reduced network
FOR EVERY OPTIMIZED UNIT
FIND SUBNET
- Subnets
- Border element- Optimized unit
Agenda
2
2
2
2
1
1
1
1
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Splitting with threshold
Idea 3: Regulation of thresholdThreshold increasing
O p t i m
i z a t i o n1 subnet
2 subnets
3 subnets
Optimized unit = 2 elements
Advantage: Optimization process is regulated by threshold
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Optimization ofindependent elements
UPDATE NETWORK
If stop-ping criterion is not met
Thread
Thread
Alternative splits of network into non-overlapping subnets for optimized units:
Split by split
DISCARD EDGES UNDER THRESHOLD
DECOMPOSITION
Full cycle of alternative splitting with regulated threshold
If threshold under limit increase its value and continue
Reduced network
…
11
1
22
2
11
2
Full network
- Subnets- Border element
- Optimized unit
Agenda
1
1
1
1
1
2
2
2
2
2
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Optimization process regulation
Start
Quantity of alternative
calculations
Com-plexity
Rough search of global optimum in small number of complex subnets (avoiding of stuck in local optimum)
Precise search of optimums in big number of simple subnets (maximal precision at the end)
Quantity
Strength of distant
interactionsPrecision of optimizing procedure
Subnets
Quantity of processes = available cores
Precision of optimization
End
Usage of all computational resources on computer / cluster
Colored arrows ( ) – increase (up) or decrease (down) of parameter valueEmpty arrows – impact on optimization process
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Optimization of mobile network coverage and quality
High optimization complexity:300 antennas * 4 parameters, n states of parameter => n1200 states
Regulated parameters of sector antenna: power, height, tilt, azimuth
Initial subnet Optimized subnet
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Alternative splitting vs. Sector planning
n – number of optimized areas in network Pi – the power of the prevalent signal in i-th areaIi – the power of the other (interfering) signals in i-th areaNi – other noise in i-th area
Optimized integral index – average Signal to Interference plus Noise Ratio:
)N+I
Pn1
(log•10=SINR ∑n
1=i ii
i10
Optimizing procedure – modified Simulated Annealing: procedure optimize(S0, precision) { Snew := S0
step := maxStep ∙ (1 – precision) Sgen := random neighbor of S0 within step t := T(1 – precision) if A(E(S0), E(Sgen), t) ≥ random(0, 1) then Snew := Sgen Output: state Snew
}
S0, Sgen, Snew – current, generated, new states of subnetmaxStep – maximal value of stepT, E, A – temperature, energy, acceptance functions
9 times faster1 hour – SINR 15 % higher (p < 0.01)
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
Benefits of Independent optimization of alternatives
• Faster optimization due to reduction of optimization complexity and efficient usage of all computational resources
• Better quality of optimization due to progressive shift of optimization strategy from rough search of global optimum at the beginning of optimization process to precise search of optimum at the end of optimization
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152.
HUAWEI TECHNOLOGIES CO., LTD.
www.huawei.com
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks // Proc. ISP RAS, 2016, vol. 28, issue 6, pp. 141-152. DOI: 10.15514/ISPRAS-2016-28(6)-10
Automatic Analysis, Decomposition and Parallel
O p t i m i z a t i o n o f L a r g e
H o m o g e n e o u sN e t w o r k s
ISPRAS Open 2016
Ignatov D.Yu., Filippov A.N., Ignatov A.D., Zhang X.
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