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This paper presents a neural-like network-based signal detection method for orbital angular momentum multiplexing systems with uniform circular array antennas. The signal detection network is derived by unfolding the alternating direction method of multipliers (ADMM), and in addition, a parallel interference cancellation (PIC) function is integrated, which enhances the tolerance to inter-mode interference while keeping the complexity feasible. The number of parameters to be learned in each layer of the network is a linear order of the number of antenna elements. Simulation results show that the ADMM-PIC detector exhibits excellent error performance, which cannot be achieved by a conventional minimum mean square error-based detector.

Learning-Based Signal Detection for Wireless OAM-MIMO Systems With Uniform Circular Array Antennas