Recent research has proposed media-based modulation (MBM) as a method to reduce the hardware complexity of wireless communications systems and therefore also achieve a reduction of the associated cost. In this work, we propose an MBM system based on a novel asymmetric signal constellation consisting of scaled and shifted Eisenstein integers. The constellation is generated by phase shifts induced by a reconfigurable intelligent antenna, where the magnitudes are modulated by turning on or off certain numbers of reflecting elements. At the receiver, a uniform linear antenna array is used to capture the incident electromagnetic planar wave. Robust estimation techniques, such as the median, the Weiszfeld algorithm, and the Sq-estimator are employed to recover the constellation points. A novel gain control scheme is proposed together with a phase offset detection method based on circular cross-correlation. Furthermore, complex-valued convolutional neural networks are used as decoders. We consider the performance of our system under impulse noise caused by voltage transients in addition to additive white Gaussian noise and show superior performance vis-a-vie a generic 64-QAM ` modulation scheme and a brute-force arithmetic method based on the four-quadrant arctan function and the median. Furthermore, we compare our system performance with hexagonal QAM-MBM and QAM-MBM.

A document by Anders Buvarp . Click on the document to view its contents.

The concept of Digital Twin has recently emerged, which requires the transmission of a massive amount of sensor data with low latency and high reliability. Analog error correction is an attractive method for low-latency communications; hence, in this paper, we propose the use of complex-valued neural networks and Quaternionic Neural Networks (QNNs) to decode analog codes. Furthermore, we propose mapping our codes to the baseband of the frequency domain to enable easy time and frequency synchronization as well as to mitigate frequency-selective fading using robust estimation theory. This is accomplished by applying inverse Discrete Fourier Transform (DFT) modulation, which achieves a significant reduction in hardware complexity, power, and cost as compared to our previously proposed analog coding scheme. Additionally, we introduce a scaled version of our previous analog codes that enables statistical signal processing, something we have not been able to achieve until now. This achieves significant noise immunity with drastic performance improvements at low Signal-to-Noise Ratios (SNR) and a small loss at high SNR.