Homogeneous Network Optimization

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


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


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


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


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


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


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


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)


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


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.

Data & Analytics

Homogeneous Network Optimization