Extreme Learning Machine-Based Receiver for MIMO LED Communications
This addresses performance degradation in MIMO LED communication systems, but appears incremental as it applies an existing method (ELM) with modifications to a specific domain.
The paper tackled the problem of LED nonlinearity and cross-LED interference degrading performance in MIMO LED communications by proposing an extreme learning machine-based receiver with a circulant input weight matrix, demonstrating efficient handling of these issues.
This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED interference.