Download
1 / 28
280 likes | 393 Vues
This paper discusses a hybrid optimization approach for the frequency assignment problem in satellite communication systems. Given the limited frequencies available, the study aims to maximize user assignments while minimizing co-channel and intra-system interference. It integrates discrete optimization techniques, such as Integer Linear Programming and greedy algorithms, with continuous optimization methods, including beam movement strategies, to enhance performance. The findings suggest that the hybrid method achieves better results than traditional approaches, paving the way for future research in frequency assignment strategies.
E N D
Hybrid Discrete-Continuous Optimization for the Frequency Assignment Problem in Satellite Communications System Kata KIATMANAROJ, Christian ARTIGUES, Laurent HOUSSIN (LAAS), Frédéric MESSINE (IRIT)
Contents • Problem definition • Discrete optimization • Continuous optimization • Hybrid method • Conclusions and perspectives
Problem definition • To assign a limited number of frequencies to as many users as possible within the service area
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
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
Problem definition • Interferenceconstraints Binary interference Cumulative interference i i j j k
Problem definition • Satellite beam & antenna gain
Discrete optimization • Integer Linear Programming • Greedy algorithms
Discrete optimization • Integer Linear Programming (ILP)
Discrete optimization • Greedy algorithms • User selection rules • Frequency selection rules
Discrete optimization • Greedy algorithms • User selection rules • Frequency selection rules
Discrete optimization • Performance comparison: ILP vs. Greedy
Discrete optimization • ILP performances
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
Continuous optimization • Matlab’s solver fmincon i j x k
Continuous optimization • Matlab’s solver fmincon i j x k
Continuous optimization • Matlab’s solver fmincon i j x k
Continuous optimization • Matlab’s solver fmincon i j x k
Continuous optimization • Matlab’s solver fmincon i j x k
Continuous optimization • Matlab’s solver fmincon • Parameters: k, MAXINEG, UTVAR
Hybrid method • Beam moving results with k-MAXINEG-UTVAR = 7-2-0
Hybrid method • Beam moving results with k-MAXINEG-UTVAR = 7-2-0
Hybrid method • Closed-loop implementation
Conclusions and further study • Greedy algorithm vs. ILP • Beam Moving algorithm benefit • Closed-loop implementation benefit vs. time • Further improvements
