22 nd September 2008 | Tariciso F. Maciel Suboptimal Resource Allocation for Multi-User MIMO-OFDMA...

22nd September 2008 | Tariciso F. Maciel

Suboptimal Resource Allocation forMulti-User MIMO-OFDMA Systems

Tarcisio F. Maciel

Darmstadt, 22nd September 2008

22nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems 2

Outline

1. Resource Allocation Problem Overview

2. Suboptimal Resource Allocation Strategies: Single Resource

3. Suboptimal Resource Allocation Strategies: Multiple Resources

4. Conclusions and Outlook


Outline






Resource Allocation Problem OverviewScenario and Optimization Objective

Frequency Division Multiple Access (FDMA)

Fre

quen

cyTime

…



Time Division Multiple Access (TDMA)

Fre

quen

cyTime

…



Current systems FDMA/TDMA (e.g., GSM)

Fre

quen

cyTime

…



Fre

quen

cy

Time

Space

Future mobile radio systems High flexibility and high capacity

Orthogonal Frequency Division Multiple Access (OFDMA)

Multiple Input Multiple Output (MIMO) Space Division Multiple Access (SDMA)

Objective Maximize the capacityof the system



Fre

quen

cy

Time

Space

Future systems High flexibility and high capacity

Orthogonal Frequency Division Multiple Access (OFDMA)

Multiple Input Multiple Output (MIMO) Space Division Multiple Access (SDMA)



Resource Allocation Problem OverviewDescription of Subproblems

Users must be separable in space Many possible groups of users Finding the group with highest

capacity requires an Exhaustive Search

SDMA groupingproblem




Users must be separable in space Many possible groups of users Finding the group with highest

capacity requires an Exhaustive Search





Beamforming done at the base station Linear precoding used to compute

precoding vectors and form beams


Precoding problem




How should power be allocated to the different users served by the system


Precoding problem

Power allocation problem




How should power be allocated to the different users served by the system


Precoding problem





Fre

quen

cy

Time

Space

Resource 1

Resource 2


Resource assignmentproblem

Precoding problem





Resource assignment problem

Precoding problem

SDMA grouping problem


Joint solution of the subproblems

Separated solutionsto the subproblems

RA strategy Optimal Suboptimal Complexity Too high Low



Resource assignment problem

Resource assignment algorithm

Precoding problem Precoding algorithm

SDMA grouping problem

SDMA algorithm

Power allocation problem Power allocation algorithm

Joint solution of the subproblems

Separated solutionsto the subproblems

RA strategy Optimal Suboptimal Complexity Too high Low


Resource Allocation Strategies


Precoding algorithm

SDMA algorithm

Power allocation algorithm

Single-resource case Multiple-resource case


Outline






Resource Allocation Strategies: Single Resource


Precoding algorithm

SDMA algorithm



•Grouping metric

•Grouping

algorithm


SDMA algorithmGrouping metric

Group capacity Suitable to maximize sum rate, but quite complex

Convex combination of spatial correlation and channel gains Less complex than group capacity

Spatially uncorrelated

h2

h2 h1





Spatially uncorrelated

h2

h2

h1

h1





Spatially uncorrelated Spatially correlated

h2

h2

h1

h2

h2

h1

h1





h2

h2

h1

h1

h2

h2

h1

h1

Spatially uncorrelated Spatially correlated


SDMA algorithmGrouping algorithm

1. Exhaustive Search + Group capacity metric Upper bound2. Random Grouping Lower bound

3. Best Fit + Group capacity metric Benchmark

4. Convex Grouping + Convex comb. spatial correlation & channel gains

5. Best Fit + Convex comb. spatial correlation & channel gains

Channel gains

Spatial correlation




Precoding algorithm

SDMA algorithm



•Grouping metric

•Grouping algorithm


Precoding and power allocation algorithms

Precoding algorithm Linear Zero-Forcing

Power allocation algorithm Water-filling

Other proposed/investigated algorithms can be found in the written work


Simulation Parameters

Parameter Value

System frequency 5 GHz

# of used subcarriers 48 subcarriers organized in 8 resources

Subcarrier spacing 9.77 kHz

Channel modelWINNER channel model, macro-cell urban scenario C2, Non-Line of Sight

# of antennas at the BS 4 omnidirectional antennas in a uniform linear array

# of users 16 single-antenna users

Average user’s speed 10 km/h

Target SDMA group size Initially set to 4 users


Performance of the RA Strategies: Single Resource

1. Upper bound: Cap.-basedExhaustive Search

2. Lower bound: Random GroupingSingle User

3. Benchmark:Cap.-based Best Fit

4. Proposed ConvexComb.-basedConvex Grouping5. Proposed Convex

Comb.-based Best Fit


Sequential Removal Algorithm

Removes users from the SDMA group increase the group capacity

User are removed, e.g., according to their effective channel gain Users with the lowest channel gain

removed first

Computes group capacity of the resulting groups

Keeps the group with the highest capacity


Performance of the RA Strategies: Single ResourceImprovement due to the Sequential Removal Algorithm

1. Upper bound: Cap.-basedExhaustive Search

2. Lower bound: Random GroupingSingle User

4. Proposed ConvexComb.-basedConvex Grouping

5. Proposed Convex Comb.-based Best Fit



Performance of the RA Strategies: Single ResourceComputational complexity

Benchmark and proposed strategies Sum rates close to those achieved through the Exhaustive Search But considerably different complexity

1. Upper bound:Cap.-basedExhaustive Search


2. Lower bound:Random Grouping

4. Proposed Convex Comb.-basedConvex Grouping

5. Proposed Convex Comb.-based Best Fit


Outline








Precoding algorithm

SDMA algorithm



•Grouping metric

•Grouping algorithm

•Priority

•Assignment algorithm


Resource Allocation Strategies: Multiple ResourcesAssignment Algorithms

1. Assign resourcesone-by-one

2. Assign resourcesto initial users

3. Assign resourcesto SDMA groups

For capacity maximization almost same sum rate of the Exhaustive SearchFor proportional fairness better degree of throughput fairness

small reduction of the sum rate

Assign a resourceto an initial user based

on user priorities

Build an SDMA group

Apply precodingand power allocation

Assign resourcesto initial users based

on user priorities

Build an SDMA groupon each resource


Build severalcandidate SDMA groups


Assign resources toSDMA groups basedon the user priorities


Resource Allocation Strategies: Multiple ResourcesProportional Fair Priorities

Benchmark: Cap.-based Best Fit

Proposed Convex Comb.-based Best Fit

Proportional Fair

Capacity Maximization



Resource Allocation Strategies: Multiple ResourcesThroughput Fairness

ProportionalFair

CapacityMaximization

1. Assign resourcesone-by-one


Proposed Strategy 2: Convex Comb.-based Best Fit

SNR = 10 dB


Outline






Conclusions and Outlook

Performance and complexity Proposed some new RA strategies Sum rate close to that achieved by the Exhaustive Search Lower complexity compared to the benchmark strategy and Exhaustive Search Provide a good trade-off between performance and complexity

Throughput fairness Higher throughput fairness at the expense of small reduction of the sum rates Provide a good trade-off between performance and fairness

Outlook Extension to multi-antenna users Extension to ensure minimum QoS levels Extension to multiple cells, including relay networks and cooperation among

base stations


…

Thank you !

22 nd September 2008 | Tariciso F. Maciel Suboptimal Resource Allocation for Multi-User MIMO-OFDMA...

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