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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.

This work shows that massive multiple-input multiple-output (MIMO) with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM), where the massive number of receive antennas act as hidden nodes of the ELM, and the low-resolution ADCs serve as the activation function of the ELM. It is demonstrated that by adding biases to received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments, e.g., the power amplifier nonlinearity at transmitter side. It is interesting that the low-resolution ADCs can bring benefit to the receiver in handling nonlinear impairments, and the most computation-intensive part of the ELM is naturally accomplished by signal transmission and reception.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The chemotactic response regulator CheY, when phosphorylated by the phosphoryl group from phosphorylated CheA, can bind to the motor switch complex to control the flagellar motor rotation. Agrobacterium fabrum (previous name: Agrobacterium tumefaciens), a phytopathogen, carries two paralogous cheY genes, cheY1 and cheY2. The functional difference of two paralogous CheYs remains unclear. Three cheY-deletion mutants were constructed to test the effects of two CheYs on the chemotaxis of A. fabrum. Phenotypes of three cheY-deletion mutants show that deletion of each cheY significantly affects the chemotactic response, but cheY2-deletion possesses more prominent effects on the chemotactic migration and swimming pattern of A. fabrum than does cheY1-deletion. CheA-dependent cellular localization of two CheY paralogs and in vitro pull-down of two CheY paralogs by FliM demonstrate that the distinct roles of two CheY paralogs arise mainly from the differentiation of their binding affinities for the motor switch component FliM, agreeing with the divergence of the key residues on the motor-binding surface involved in the interaction with FliM. The single respective replacements of key residues R93 and A109 on the motor-binding surface of CheY2 by alanine (A) and valine (V), the corresponding residues of CheY1, significantly enhanced the function of CheY2 in regulating the chemotactic response of A. fabrum CheY-deficient mutant Δy to nutrient substances and host attractants. These results conclude that the divergence of the key residues in the functional subdomain is the decisive factor of functional differentiation of these two CheY homologs and protein function may be improved by the substitution of the divergent key residues in the functional domain for the corresponding residues of its paralogs. This finding will help us to better understand how paralogous proteins sub-functionalize. In addition, the acquirement of two CheY2 variants, whose chemotactic response functions are significantly improved, will be very useful for us to further explore the mechanism of CheY to bind and regulate the flagellar motor and the role of chemotaxis in the pathogenicity of A. fabrum.

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers.

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