Frequency Allocation in a SDMA Satellite Communications System

Frequency Allocation in a SDMA Satellite Communications System with Beam Moving Kata KIATMANAROJ, Christian ARTIGUES, Laurent HOUSSIN (LAAS), Frédéric MESSINE (IRIT) ICC-2012 1

Contents • • Problem definition Discrete optimization Continuous optimization (Beam Moving) Conclusions and perspectives ICC-2012 2

Problem definition • To assign a limited number of frequencies to as many users as possible within the service area ICC-2012 3

Problem definition • To assign a limited number of frequencies to as many users as possible within the service area • Frequency is a limited resource! – Frequency reuse -> co-channel interference – Intra-system interference ICC-2012 4

Problem definition • To assign a limited number of frequencies to as many users as possible within the service area • Frequency is a limited resource! – Frequency reuse -> co-channel interference – Intra-system interference • Graph coloring problem – NP-hard ICC-2012 5

Problem definition • Interference constraints Binary interference Cumulative interference i i j j k ICC-2012 6

Problem definition • Satellite beam & antenna gain • SDMA: Spatial Division Multiple Access ICC-2012 i j k 7

Discrete optimization ICC-2012 8

Discrete optimization • Integer Linear Programming (ILP) • Greedy algorithms ICC-2012 9

Discrete optimization • Integer Linear Programming (ILP) ICC-2012 10

Discrete optimization • Greedy algorithms – User selection rules – Frequency selection rules ICC-2012 11

Discrete optimization • Greedy algorithms – User selection rules – Frequency selection rules ICC-2012 12

Discrete optimization • Performance comparison: ILP vs. Greedy 180 160 Number of accepted users 140 120 100 Greedy 80 ILP (60 s) ILP (180 s) 60 40 20 ICC-2012 40 60 80 100 120 140 Number of users 160 180 200 13

Discrete optimization • ILP performances ICC-2012 14

Continuous optimization ICC-2012 15

Continuous optimization • Beam moving algorithm – For each unassigned user • Continuously move the interferers’ beams from their center positions-> reduce interference • Non-linear antenna gain • Minimize the move • Not violating interference constraints ICC-2012 16

Continuous optimization • Matlab’s solver fmincon User i i x k ICC-2012 j Gain αi Δix + j Δjx + k Δkx + x 0 - 17

Continuous optimization • Matlab’s solver fmincon User i i x j i Gain αi Δix ↓ ↓+ j k k ICC-2012 x - 18

Continuous optimization • Matlab’s solver fmincon User i i x j i Gain αi Δix ↓ ↓ j k k ICC-2012 x - 19

Continuous optimization • Matlab’s solver fmincon User i i x j i Gain αi Δix ↓ ↓- j k k ICC-2012 x - 20

Continuous optimization • Matlab’s solver fmincon User i i x k ICC-2012 j Gain αi Δix i ↓ ↓ j ↓ ↓ k ↓ ↓ x + 21

Continuous optimization • Matlab’s solver fmincon • Parameters: k, MAXINEG, UTVAR 180 6, 0 160 140 5, 0 120 4, 0 100 3, 0 80 60 2, 0 40 1, 0 20 0, 0 0 3 ICC-2012 4 5 6 7 8 k (Number of Interferers) 9 10 7, 0 160 6, 0 140 120 5, 0 100 4, 0 80 3, 0 60 2, 0 40 1, 0 20 0, 0 0 3 4 5 6 7 8 k (Number of Interferers) 9 10 Users (MAXINEG = 1) Users (MAGINEG = 2) Time (MAXINEG = 1) Time (MAXINEG = 2) 22 Cal. Time / Resggined User (s) 7, 0 Number of Reassigned Users Beam Decentring with UTVAR = 1 Cal. Time / Resggined User (s) Number of Reassigned Users Beam Decentring with UTVAR = 0

Continuous optimization • Beam moving results with k-MAXINEG-UTVAR = 7 -2 -0 180 160 140 120 100 80 Greedy 60 ILP (60 s) 60 ILP (180 s) 40 Greedy + Beam Decentring ILP + Beam Decentring 20 0 20 ICC-2012 40 60 80 100 120 140 160 180 20 40 60 80 100 120 140 160 180 200 23

Continuous optimization • Beam moving results with k-MAXINEG-UTVAR = 7 -2 -0 ICC-2012 24

Conclusions and further study • Greedy algorithm vs. ILP • Beam Moving algorithm benefit • Closed-loop implementation benefit vs. time • Further improvements ICC-2012 26

Thank you ICC-2012 27