an energy efficient approach in coverage and capacity optimization …€¦ · ·...

Telecommunication Networks

and integrated Services

An energy efficient approach in coverage and capacity

optimization of heterogeneous networksLaboratory

Department of Digital

Systems

University of Piraeus

Research Center (UPRC)

tns.ds.unipi.gr

optimization of heterogeneous networks

Presenter: Dimitrios Karvounas

Email: [email protected]

● Motivation

● Research Objectives

● Extended Self-Organizing Networks Functional Architecture

● System Model

► Hard Constraint

● Formulation

► Cases

► Constraints and Policies

Outline

TNS LaboratoryETSI Energy Efficiency Workshop, 8/10/2013, Athens, Greece Slide 2

► Constraints and Policies

● Evaluation

► Parameters

► Results

● Conclusion

● Future work

Motivation

● Demand for wireless access to Internet-based services increasing exponentially

► Benefits for Network Operators (NOs) through cost efficient deployment of Radio Access Networks (RANs)

● 3GPP LTE offered smart operation, administration and maintenance (OAM)

► LTE will co-exist with legacy networks →Complex and heterogeneous infrastructure

● A HetNet may consist of different types of infrastructure elements (macro-BS, micro-


infrastructure elements (macro-BS, micro-, pico-, femto-cells, etc.)

► Small cells can enhance the coverage and the capacity of smaller areas than the areas covered by the macro-BSs

► HetNet elements can share same resources (e.g. frequency bands)

■ Joint radio resource and interference

management is needed

► In general, all these elements need to be properly managed

● The proposal of a Coverage and Capacity Optimization (CCO) mechanism that will also take into account green aspects

► Transmission energy of utilized elements

■ Necessary elements transmit to optimal power levels

■ Redundant elements transit to sleep mode

● Extension of the SON Functional Architecture

► Support the management of small cells

Research Objectives


● Formulation of the CCO problem

► Consideration of an area that comprises a macro-BS and small cells

► Mathematical formulation of an optimization problem for

■ The assignment of resources to the BSs (e.g. users, transmission power levels, etc.)

■ The assignment of resources to the users (e.g. resource blocks (RBs))

● Solution of the CCO problem

► Utilization of IBM ILOG CPLEX Optimization Studio

Extended SON Functional Architecture


● Profile management → device capabilities and user preferences required for the estimation of the solution (e.g. set of potential configurations, applications that can be used and potential QoS levels)

● Suitability determination → Detects whether the offloading to small cells is beneficial (e.g. in terms of performance, reconfiguration cost, energy, etc.)

● Startup and management → Initiates and manages (reconfiguration or termination) the offloading procedure

● Macro-BS and small cells operate at LTE technology

► Frequency reuse factor = 1 for maximum spectral efficiency

► Macro-BS and small cells cannot use the same RBs simultaneously (hard constraint) → reduced interference

● Transmission power of BSs

► Macro-BS transmits to a constant power level so as not to create coverage “holes”

System Model


coverage “holes”

► Small cells can operate at discrete power levels (e.g. a proportion of their maximum transmission power level)

● Users

► Can be served by only one BS (macro or small)

► Assigned with a set of RBs → specific bitrate

► Have a demand (in terms of bits/sec)

► Suffer interference from other cells that use the same RBs

● Consideration of 3 cases

► A network with only a macro BS (MC case)

► A network with a macro BS and deployed small cells (MC+SC)

■ RBs can be used simultaneously by the macro and the small cells

► A network with a macro BS and deployed small cells and the hard constraint (MC+SC+HC)

■ The macro BS and the small cells cannot use the same RBs simultaneously

System Model: Hard Constraint

● MC case

► All users served by the macro BS

► Throughput of 73Mbps


● MC+SC case

► Part of the traffic offloaded to the small cells

► High interfence


■ Increase by 8%

● MC+SC+HC case

► Part of the traffic offloaded to the small cells


■ Increase by 13% and 4%

D. Karvounas, P. Vlacheas, A. Georgakopoulos, V. Stavroulaki, P.Demestichas, "Enriching Self-Organizing Networks Use Caseswith Opportunistic Features: A Capacity and CoverageOptimization Paradigm", International Journal of NetworkManagement, Special Issue on Managing Self-Organizing RadioAccess Networks, vol.23, no.4, pp.272-286, 2013, Wiley

● Consideration of two cases

► Energy Efficiency (EE) is taken into account

► Only performance (PRF) is taken into account

● Maximization of an objective function (OF)

Formulation: Cases

(

m

,

ax

)

c c

c c

uc u u

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uEE

c

c k

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● Weighted sum of users’ throughput

► Weights are coefficients that adjust fairness among users

● Sum of the transmission powers assigned to the BSs


( )PRF uc u u

Uu

OF w T∈

⋅=∑

the BSs

● Only the throughput is taken into consideration

● Decision on

► Allocation of users to cells

► Assignment of RBs to users

► Power allocation to small cells

● Each user can acquire RBs by one BS at most

► Each user can be served by only one BS at most

● Each RB of a cell can be assigned to one user at most

► The same RB within a BS cannot be assigned to two users

● Each BS can be configured to transmit at only one power level

● The achieved throughput of each user must be greater or equal than his demand

● The sum of the weights must be equal to 1

Formulation: Constraints and Policies

Constraints


● The macro-BS and the small cells cannot use the same RBs simultaneously

► Reduce interference from high-power elements

● The network’s energy consumption must be less or equal than a proportion of the network’s maximum energy consumption

► Applies only for the EE case

● The weight given to a user is proportional to his demand

► Higher demand → More expensive contract

Policies

Evaluation: Parameters

Parameter Value

Number of macro-cells 1

Number of small cells 4

LTE frequency 2 GHz

LTE bandwidth 5 MHz

Reuse factor 1


Number of RBs 25

Bandwidth of each RB 180 KHz

Thermal noise density -204 dB/Hz

Macro BS max. transmission power 3.98e-6 W/Hz

Small cell max. transmission power 1.26e-6 W/Hz

Small cell power levels 100%, 80%, 60%

Number of users 24

● Trade-off between the energy consumption and the throughput

► When EE is considered, the energy consumption is reduced by 33% with respect to the PRF case

► In EE case the average user throughput decreases by 18% compared to the PRF case

●

Evaluation: Results


● Macro-BS users suffer no interference

● Small cell users

► EE case → Low transmission power →Low interference

► Small cells 2 and 4 switched off at EE case → no interference

D. Karvounas, P. Vlacheas, A. Georgakopoulos, M. Logothetis, V.

Stavroulaki, K. Tsagkaris, P. Demestichas, "Coverage and Capacity

Optimization in Heterogeneous Networks (HetNets): A Green

Approach", in Proc. International Symposium on Wireless

Communication Systems (ISWCS) 2013, Ilmenau, Germany, Aug.

2013

● A CCO use case was considered where small cells are deployed within the area of a macro-BS

● Small cells were configured to optimal power levels

► Energy efficiency by turning to sleep mode redundant small cells

► Low interference to other users

► Adequate throughput

Conclusion


● Formulation of the CCO problem

► EE and PRF cases

► Complex binary integer optimization problem

► Solution through IBM ILOG CPLEX Optimization Studio

● Trade-off when EE is considered

► 33% reduction of energy consumption → 18% decrease in throughput

● Enhancement of the CCO mechanism

► Support ultra-dense networks

■ Thousands of cells

■ Traffic requirements of 2020

● Development of a heuristic algorithm

► Solution of the CCO problem within reasonable time

► Sub-optimal solution close to the optimal estimated by the ILOG

Future work


● This work is partially supported by the ARTEMIS project (A cognitive ecosystem for smART Energy Management of wIreless technologieS and mobile applications) funded by the General Secretariat of Research and Technology (GSRT) of the Greek Ministry of Development. The views expressed in this presentation do not necessarily represent the views of the complete consortium. The Community is not liable for any use that may be made of the information contained herein.

Acknowledgement


● This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) -Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.

Thank you


Thank you

TNS LaboratoryETSI Energy Efficiency Workshop, 8/10/2013, Athens, Greece

Backup slides

Formulation

Formula Description

Binary variable that depicts whether RB r∈R of cell c∈C is assigned.

Received signal power of user u∈U for RB r∈R. It is assumed that the transmission power of a cell is equally distributed at its RBs.

Suffered interference of user u∈U for RB r∈R.

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Formulation: Constraints and Policies

Constraint/Policy Description

Each user u∈U can acquire a RB r∈R by one cell c∈C at most, thus can only be assigned to one cell.

Each RB r∈R of a cell c∈C can be assigned to only one user u∈U at most.

Each cell c∈C can be configured to operate at only power level kc∈Pc.

The throughput of each user u∈U must be greater or equal than his demand.

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The sum of the weights must be equal to 1.

A macro-cell cm∈C and a small cell cs∈C cannot use the same RB r∈R simultaneously (Policy)

The system’s energy consumption must be less or equal than a specific ratio (pthres) of the maximum system’s energy consumption (Policy – considered only in EE case)

Priority is given to the users that demand higher bitrates. It is assumed that these users have more expensive contracts with the operator (Policy)

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