1. 22 nd September 2008 | Tariciso F. Maciel Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Tarcisio F. Maciel Darmstadt, 22 nd September 2008

2. 2 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Outline 1.Resource Allocation Problem Overview 2.Suboptimal Resource Allocation Strategies: Single Resource 3.Suboptimal Resource Allocation Strategies: Multiple Resources 4.Conclusions and Outlook

3. 3 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Outline 1.Resource Allocation Problem Overview 2.Suboptimal Resource Allocation Strategies: Single Resource 3.Suboptimal Resource Allocation Strategies: Multiple Resources 4.Conclusions and Outlook

4. 4 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Scenario and Optimization Objective  Frequency Division Multiple Access (FDMA) Frequency Time …

5. 5 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Scenario and Optimization Objective  Time Division Multiple Access (TDMA) Frequency Time …

6. 6 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Scenario and Optimization Objective  Current systems  FDMA/TDMA (e.g., GSM) Frequency Time …

7. 7 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Scenario and Optimization Objective Frequency 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 capacity of the system

8. 8 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Scenario and Optimization Objective Frequency 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) Objective  Maximize the capacity of the system

9. 9 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems  Users must be separable in space  Many possible groups of users  Finding the group with highest capacity requires an Exhaustive Search SDMA grouping problem Objective  Maximize the capacity of the system

10. 10 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems  Users must be separable in space  Many possible groups of users  Finding the group with highest capacity requires an Exhaustive Search SDMA grouping problem Objective  Maximize the capacity of the system

11. 11 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems  Beamforming done at the base station  Linear precoding used to compute precoding vectors and form beams SDMA grouping problem Precoding problem Objective  Maximize the capacity of the system

12. 12 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems  How should power be allocated to the different users served by the system SDMA grouping problem Precoding problem Power allocation problem Objective  Maximize the capacity of the system

13. 13 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems  How should power be allocated to the different users served by the system SDMA grouping problem Precoding problem Power allocation problem Objective  Maximize the capacity of the system

14. 14 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems Frequency Time Space Resource 1 Resource 2 SDMA grouping problem Resource assignment problem Precoding problem Power allocation problem Objective  Maximize the capacity of the system

15. 15 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems Resource assignment problem Precoding problem SDMA grouping problem Power allocation problem Joint solution of the subproblems Separated solutions to the subproblems RA strategy Optimal Suboptimal  Complexity Too high  Low

16. 16 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Problem Overview Description of Subproblems 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 solutions to the subproblems RA strategy Optimal Suboptimal  Complexity Too high  Low

17. 17 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies Resource assignment algorithm Precoding algorithm SDMA algorithm Power allocation algorithm Single-resource caseMultiple-resource case 

18. 18 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Outline 1.Resource Allocation Problem Overview 2.Suboptimal Resource Allocation Strategies: Single Resource 3.Suboptimal Resource Allocation Strategies: Multiple Resources 4.Conclusions and Outlook

19. 19 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Single Resource Resource assignment algorithm Precoding algorithm SDMA algorithm Power allocation algorithm Single-resource caseMultiple-resource case Grouping metric Grouping algorithm

20. 20 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems SDMA algorithm Grouping 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 h2h2 h2h2 h1h1

21. 21 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems SDMA algorithm Grouping 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 h2h2 h2h2 h1h1 h1h1

22. 22 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems SDMA algorithm Grouping 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 uncorrelatedSpatially correlated h2h2 h2h2 h1h1 h2h2 h2h2 h1h1 h1h1

23. 23 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems SDMA algorithm Grouping metric  Group capacity  Suitable to maximize sum rate, but quite complex  Convex combination of spatial correlation and channel gains  Less complex than group capacity h2h2 h2h2 h1h1 h1h1 h2h2 h2h2 h1h1 h1h1 Spatially uncorrelatedSpatially correlated 

24. 24 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems SDMA algorithm Grouping algorithm 1. Exhaustive Search+ Group capacity metric  Upper bound 2. 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 

25. 25 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Single Resource Resource assignment algorithm Precoding algorithm SDMA algorithm Power allocation algorithm Single-resource caseMultiple-resource case Grouping metric Grouping algorithm

26. 26 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems 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 

27. 27 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Simulation Parameters ParameterValue System frequency5 GHz # of used subcarriers48 subcarriers organized in 8 resources Subcarrier spacing9.77 kHz Channel model WINNER channel model, macro-cell urban scenario C2, Non-Line of Sight # of antennas at the BS4 omnidirectional antennas in a uniform linear array # of users16 single-antenna users Average user’s speed10 km/h Target SDMA group sizeInitially set to 4 users

28. 28 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Performance of the RA Strategies: Single Resource 1. Upper bound: Cap.-based Exhaustive Search 2. Lower bound: Random Grouping Single User 3. Benchmark: Cap.-based Best Fit 4. Proposed Convex Comb.-based Convex Grouping 5. Proposed Convex Comb.-based Best Fit

29. 29 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems 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 

30. 30 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Performance of the RA Strategies: Single Resource Improvement due to the Sequential Removal Algorithm 1. Upper bound: Cap.-based Exhaustive Search 2. Lower bound: Random Grouping Single User 4. Proposed Convex Comb.-based Convex Grouping 5. Proposed Convex Comb.-based Best Fit 3. Benchmark: Cap.-based Best Fit

31. 31 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Performance of the RA Strategies: Single Resource Computational complexity  Benchmark and proposed strategies  Sum rates close to those achieved through the Exhaustive Search  But considerably different complexity 1. Upper bound: Cap.-based Exhaustive Search 3. Benchmark: Cap.-based Best Fit 2. Lower bound: Random Grouping 4. Proposed Convex Comb.-based Convex Grouping 5. Proposed Convex Comb.-based Best Fit 

32. 32 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Outline 1.Resource Allocation Problem Overview 2.Suboptimal Resource Allocation Strategies: Single Resource 3.Suboptimal Resource Allocation Strategies: Multiple Resources 4.Conclusions and Outlook

33. 33 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Single Resource Resource assignment algorithm Precoding algorithm SDMA algorithm Power allocation algorithm Single-resource caseMultiple-resource case Grouping metric Grouping algorithm Priority Assignment algorithm

34. 34 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Multiple Resources Assignment Algorithms 1. Assign resources one-by-one 2. Assign resources to initial users 3. Assign resources to SDMA groups For capacity maximization  almost same sum rate of the Exhaustive Search For proportional fairness  better degree of throughput fairness  small reduction of the sum rate Assign a resource to an initial user based on user priorities  Build an SDMA group Apply precoding and power allocation Assign resources to initial users based on user priorities Build an SDMA group on each resource Apply precoding and power allocation Build several candidate SDMA groups Apply precoding and power allocation Assign resources to SDMA groups based on the user priorities

35. 35 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Multiple Resources Proportional Fair Priorities Benchmark: Cap.-based Best Fit Proposed Convex Comb.-based Best Fit Proportional Fair Capacity Maximization 3. Assign resources to SDMA groups

36. 36 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Resource Allocation Strategies: Multiple Resources Throughput Fairness Proportional Fair Capacity Maximization 1. Assign resources one-by-one 3. Assign resources to SDMA groups Proposed Strategy 2: Convex Comb.-based Best Fit SNR = 10 dB

37. 37 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems Outline 1.Resource Allocation Problem Overview 2.Suboptimal Resource Allocation Strategies: Single Resource 3.Suboptimal Resource Allocation Strategies: Multiple Resources 4.Conclusions and Outlook

38. 38 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems 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

39. 39 22 nd September 2008 | Tariciso F. Maciel | Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems … Thank you !