Towards an Optimized Channel Estimation in Optical Spatial Multiplexing Systems via Swarm Intelligence Algorithms

Abstract

Optical spatial multiplexing systems are advantageous communication systems since they allow large amounts of data to be transferred securely. Nonetheless, the performance of the system is decreased due to modal coupling caused by environmental factors and fibre imperfections. Hence, modal demodulation techniques, which aim at estimating the exact channel sent by retrieving the amplitude and phase of the modes present in the signal, are crucial. Numerical demodulation techniques have been found to be more efficient than experimental ones. The conventional numerical demodulation techniques consist of linear optimization algorithms. However, these algorithms do not provide high-quality solutions when applied for channel estimation in optical spatial multiplexing systems as this is a non-linear problem. Some non-linear optimization algorithms such as artificial neural networks have been used for channel estimation and have been found to have a better performance than the linear optimization algorithms. However, other non-linear optimization algorithms such as swarm intelligence algorithms, which have been proven to be effective in solving non-linear optimization problems, have been scarcely used for channel estimation. Moreover, recent swarm intelligence algorithms have not been applied for channel estimation. Hence, in this paper, the idea of using a swarm intelligence algorithm for channel estimation in an optical spatial multiplexing system is proposed. A recent and high-performing swarm intelligence algorithm will be used for channel estimation by optimization an objective function. The results will be recorded and the performance of the algorithm will be evaluated by calculating the correlation of the reconstructed beam. It is expected that the algorithm will provide solutions with a high accuracy since swarm intelligence algorithms are non-linear optimization algorithms which have been proven to effective for non-linear optimization problems.