co-channel interference aware resource allocation in cellular

Technische Universität Berlin, Germany

Co-channel Interference AwareResource Allocation in

Cellular OFDMA Systems

Mathias Bohge

Workshop der ITG Fachgruppe 5.2.1Stuttgart, November 2009

Mathias Bohge, “Co-channel Interference Aware Resource Allocation in Cellular OFDMA Systems”

The Problem

Co-Channel Interference (CCI)


The Solution: Dynamic OFDMA

SC

Wireless TerminalQuality of SC 1

W.Rhee and J.M.Cioffi, 2000: „Increase the capacity of multiuser OFDM system using dynamic subchannel allocation”

SC

Wireless Terminal

Dynamic OFDMA sub-carrier assignments

Static assignments can’treact on spatial diversity


p passignments

- optimization approaches sufferfrom outdated CSI

- local knowledge only still showssignificant improvement vs. random

System Throughput User Throughput CDF

- use dynamic power masks to boost local performance

- compare to static power masksapproaches

25% of all users


AgendaProblem: CCI*, Solution: dynamic OFDMA

Optimal dynamic resource allocation (non-linear)

Soft-frequency reuse enabled dynamic SCA* (linear)

*CCI: co-channel interference, *SCA: sub-carrier allocation

Dynamic power mask creation (heuristic)

Simulative evaluation


CCI in the CELLULAR contextProblem- frequency reuse of 1- power/sc usage is decided per cell

- single-cell dynamic approach leads to1. higher power for cell-edge users2. increased CCI probabilityCell 1 Cell 2

1 2 3 4

Solution approach: CoMP- apply CCI aware resource scheduling

1. optimal sub-carrier assignments 2. optimal power distribution

Formulate resource schedulingproblem including CCI

LTE resource usage


Downlink scheduling problemScheduler is central entity.System Model

Scheduling DecisionsIn each frame, two kinds ofassignments need to be made:

- Resource Block &- Power Assignments.

Optimization ModelXj,m,r : sc assignment variableyj,r : power variable

Optimization Goal: Max. System Throughput


Multi Cell Resource SchedulingOptimization Model

Challenges

Co-channel interferenceNon – linear optimization problem

Distribution of information data

Complexity (non-linear IP)

Shannon Capacity

Benefits- Delivers optimal sub-carrier &

power assignments- Fair approach: implements simple

utility function (users do not get more than max Rate).

Signal-to-Noise & Interference

Optimization Problem


gAlternative

- SFR: soft frequency reuse

- FFR: fractional frequency reuse

- HFR: hard frequency reuse

Power Masking

Advantages- linear approach

- local info needed per BS only

- simplification easier

Optimization Model

A

BC

Signal-to-Noise & Interference

Optimization Problem


Research Question

- SFR: soft frequency reuse

- FFR: fractional frequency reuse

- HFR: hard frequency reuse

How does the local power masking approachperform compared to the non-linear optimum?

Simulations based on C++ library OMNeT++- non-linear optimization problem instances solved with LINGO

- linear optimization problem instances solved with CPLEX

Initial Investigation- Formulation of the non-linear optimization problem. - Formulation of the linear power mask enabled optimization problem.- Solving 10.000 instances of both approaches in a SIMPLE scenario.- Comparison of the performance of both to standard approaches.


Frequency Reuse Results

- Simple two cell scenario- Hard Frequency Reuse (HFR) vs.

Soft Frequency Reuse (SFR) vs.Frequency Reuse 1 (FR1)

System Throughput Weakest Throughput

- Optimum solution achieves max.Rate for all users at all rates

- SFR outperforms FR1- HFR bad for high max. Rates

OPTIMUM OPTIMUM


Intermediate ConclusionsDynamic OFDMA resource scheduling

approaches help to mitigate CCI.

- Get closer to the real world:- Consider larger scenarios.- Consider less channel state information per base station.- Consider non-ideal channel state information.

- Construct dynamic power masks to improve local performance.

Next steps

- Non-linear optimal problem highly complex, but delivers promising results.

- The performance of linear locally optimal scheduling is worse, but can be improved for the multi-cell case by applying soft-frequency reuse power masks. Is the gain still significant?


AgendaProblem: CCI*, Solution: dynamic OFDMA

Optimal dynamic resource allocation (non-linear)

Soft-frequency reuse enabled dynamic SCA* (linear)

*CCI: co-channel interference, *SCA: sub-carrier allocation

Dynamic power mask mechanism (heuristic)

Simulative evaluation


gAlternativeSimulation scenario

Tasks - compare random assignment to global and local knowledge based optimized assignment approaches

- consider CSI actuality

- 3GPP UTRA and EUTRA simulation case 1

- 7 cells, 70 users, 25 RBs- user speeds 0m/s, 10m/s, 30m/s- Manhattan grid mobility- random user positions- max THR: 1Mbit/s- central entity (CE)


p passignments

- optimization approaches sufferfrom outdated CSI

- local knowledge only still showssignificant improvement vs. random

System Throughput User Throughput CDF

- use dynamic power masks to boost local performance

- compare to static power masksapproaches

25% of all users


Adapt power masks in outer loop

- take successful UMTS approach

- calculate power masks at CE

- re-distribute every 100 frames

Power mask updates Power mask calculation

Modify to suit LTE- create coupling matrix with

respect to user tp needs per rb

UMTS interference coupling model


Coupling model for LTE

Coupling equations

- one coupling equation per rb- dimension: cells x cells- input: coupling matrix- output: vector of cell powers

needed to provide throughput encoded in coupling matrix

Linearization

- derive linear model byapproximating Shannon

- main difference: infinite tp isimpossible, even with infinite power

UMTS is power controlled / LTE uses AMC instead

Idea from [Majewski,Türke et al., PIMRC’07]


Heuristic

Linearization

Power Mask OptimizationOptimization Problem

- simple, but sub-optimal- not yet fast enough

Heuristic facts


The impact of local optimization

- Improvement for worst thirdof users

- check in non-symmetric scenarios- Check with real-word data

Empirical CDF, 0 m/sSystem Throughput


ConclusionsDynamic OFDMA resource scheduling

approaches help to mitigate CCI.

- Use real-word data from the momentum project to validate results.- Use low-complexity algorithm instead of optimization problem to

get closer to real world system capabilities.- Improve dynamic power mask configuration algorithm.

Next steps

- Local optimization of resource assignments has a great potential to improve system performance even with delay CSI.

- The performance of linear locally optimal scheduling can significantly be improved by using dynamic power masks.


UEFA Cup Winner 2010

Hertha BSC Berlin


The End

THANK YOU !

co-channel interference aware resource allocation in cellular

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