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Frequency assignment for satellite communication systems Kata KIATMANAROJ Supervisors: Christian ARTIGUES, Laurent HOUSSIN. Contents. Problem definition Current state of the art Contributions Conclusions and perspectives. Problem definition. Problem definition.

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  1. Frequency assignment for satellite communication systemsKata KIATMANAROJSupervisors: Christian ARTIGUES, Laurent HOUSSIN

  2. Contents • Problem definition • Current state of the art • Contributions • Conclusions and perspectives

  3. Problem definition

  4. Problem definition • To assign a limited number of frequencies to as many users as possible within a service area

  5. Problem definition • To assign a limited number of frequencies to as many users as possible within a service area • Frequency is a limited resource! • Frequency reuse -> co-channel interference • Intra-system interference

  6. Problem definition • Simplified beam • SDMA: Spatial Division Multiple Access j i k

  7. Problem definition • To assign a limited number of frequencies to as many users as possible within a service area • Frequency is a limited resource! • Frequency reuse -> co-channel interference • Intra-system interference • Graph coloring problem • NP-hard

  8. Problem definition • Interferenceconstraints Binary interference Cumulative interference i i j j k Acceptable interference threshold Interference coefficients

  9. Problem definition • Assignment • Logical boxes (superframes) • Demand = |F|x|T| • No overlapping within the superframe • Overlapping between superframes (simultaneous)  may create interference 1 2 0 ≤ oij ≤ 1

  10. Problem definition • Superframe structure

  11. Problem definition • Frames and satellite beams

  12. Problem definition

  13. Current state of the art

  14. Current state of the art - FAP • Distance FAPs • Maximum Service FAP • Minimum Order FAP • Minimum Span FAP • Minimum Interference FAP • Solving methods • Exact method • Heuristics and metaheuristics

  15. Current state of the art – satellite FAP • Two branches • Inter-system interference • Intra-system interference • Inter-system interference • Two or more adjacent satellites • Minimize co-channel interference (multiple carriers) • Intra-system interference • Multi-spot beams • Geographical zones assuming the same propagation condition

  16. Contributions

  17. Contributions • Part 1: Single carrier models • Part 2: Multiple carrier models • Part 3: Industrial application

  18. Single carrier models • K. Kiatmanaroj, C. Artigues, L. Houssin, and F. Messine, Frequency assignment in a SDMA satellite communication system with beam decentring feature, submitted to Computational Optimization and Applications (COA) • K. Kiatmanaroj, C. Artigues, L. Houssin, and F. Messine, Frequency allocation in a SDMA satellite communication system with beam moving, IEEE International Conference on Communications (ICC), 2012 • K. Kiatmanaroj, C. Artigues, L. Houssin, and F. Messine, Hybrid discrete-continuous optimization for the frequency assignment problem in satellite communication system, IFAC symposium on Information Control in Manufacturing (INCOM), 2012

  19. Single carrier models • 1 frequency over the total duration • Same frequency + located too close -> Interference • 3 models (supplied by Thales Alenia Space)

  20. Single carrier models • Model 1 (fixed-beam binary interference) • 40 fixed-beams • 2 frequencies / beam even no user • Interference matrix (binary interference) • Graph coloring: DSAT algorithm -> 4 colors 8 frequencies in total

  21. Single carrier models • Model 2 (fixed-beam varying frequency) • 40 fixed-beams • 8 frequencies (different within the same beam) • Cumulative interference • Greedy vs. ILP

  22. Single carrier models • Model 3 (SDMA-beam varying frequency) • SDMA (beam-centered) • 8 frequencies (different within the same beam) • Cumulative interference • Greedy vs. ILP

  23. Single carrier models • Greedy algorithms • User selection rules • Frequency selection rules

  24. Single carrier models • Greedy algorithms • User selection rules • Frequency selection rules

  25. Single carrier models • Integer Linear Programming (ILP)

  26. Single carrier models • Performance comparison ILP 60 sec

  27. Single carrier models • ILP performances

  28. Continuous optimization * Collaboration with FrédéricMezzine, IRIT, Toulouse

  29. Continuous optimization • Beam moving algorithm • For each unassigned user • Continuously move the interferers’ beams from their center positions • Non-linear antenna gain • Minimize the move • Not violating interference constraints

  30. Continuous optimization • Matlab’s solver fmincon i j x k

  31. Continuous optimization • Matlab’s solver fmincon i j x k

  32. Continuous optimization • Matlab’s solver fmincon i j x k

  33. Continuous optimization • Matlab’s solver fmincon i j x k

  34. Continuous optimization • Matlab’s solver fmincon i j x k

  35. Continuous optimization • Matlab’s solver fmincon • k: number of beams to be moved • MAXINEG: margin from the interference threshold • UTVAR: whether to include user x to the move

  36. Continuous optimization • Matlab’s solver fmincon • Parameters: k, MAXINEG, UTVAR

  37. Continuous optimization • Beam moving results with k-MAXINEG-UTVAR = 7-2-0

  38. Continuous optimization • Beam moving results with k-MAXINEG-UTVAR = 7-2-0

  39. Continuous optimization • Closed-loop implementation

  40. Conclusions and further study – Part 1 • Greedy algorithm: efficient and fast • ILP: optimal but long calculation time • Beam moving: performance improvement • Column generation for ILP • Fast heuristics for continuous problem • Non-linear integer programming

  41. Multiple carrier models

  42. Multiple carrier models • Binary interference • Cumulative interference

  43. Multiple carrier models • Binary interference • LF: loading factor

  44. Multiple carrier models • Binary interference • A user as a task or an operation • User demand (frequencies) as processing time • Interference pairs as non-overlapping constraints • Disjunctive scheduling problem without precedence constraints • Max. number of scheduled tasks with a common deadline

  45. Multiple carrier models • Binary interference • Disjunctive graph and clique • {1,2}, {2,3}, {2,4}, {3,5}, {4,5,6} vs. 7 interference pairs • CP optimizer

  46. Multiple carrier models • Binary interference

  47. Multiple carrier models • Binary interference

  48. Multiple carrier models • Binary interference

  49. Multiple carrier models • Cumulative interference • Overlapping duration should be considered

  50. Multiple carrier models • Cumulative interference: ILP1

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