Ant-colony optimization-based multi-input multi-output (ACO-MIMO) equalization for low-complexity mode-division multiplexing (MDM) processing

In mode-division multiplexing (MDM) systems, the computational complexity of the multi-input multi-output (MIMO) equalization module is a critical obstacle to practical development. The step size μ and the number of taps K are key parameters in the equalization algorithm, influencing the performance of finite impulse response (FIR) equalizers, including convergence speed and output signal quality. To alleviate the computational burden of locating the optimal μ-K combination, we propose two ant colony optimization (ACO) -based MIMO equalization schemes: the fixed ACO-MIMO and the random ACO-MIMO, corresponding to two optimization strategies. These schemes expedite the initialization process of both parameters. Subsequently, we conduct experiments to evaluate their performance in a 3-mode recirculating-loop transmission system. Our findings demonstrate that, compared to conventional schemes, such as genetic algorithm (GA) and steepest descent algorithm (SDA), the proposed ACO-MIMO schemes significantly reduce the number of calls to the equalization algorithm for locating optimal μ-K combination by up to 42.74% and 80.63%, reducing the complexity of the whole MIMO equalization for MDM systems. And the resulting hit-rate P