Unified bit-based probabilistic data association aided MIMO detection for high-order QAM constellations

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order rectangular Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear binary reflected Gray bit-to-symbol mappings are considered. With the aid of simulation results, we demonstrate that the linear natural mapping based B-PDA approach typically attained an improved detection performance (measured in terms of both Bit Error Ratio (BER) and Symbol Error Ratio (SER)) in comparison to the conventional symbol-based PDA aided MIMO detector, despite its dramatically reduced computational complexity. The only exception is that at low SNRs, the linear natural mapping based B-PDA is slightly inferior in terms of its BER to the conventional symbol-based PDA using binary reflected Gray mapping. Furthermore, the simulation results show that the linear natural mapping based B-PDA MIMO detector may approach the best-case performance provided by the nonlinear binary reflected Gray mapping based B-PDA MIMO detector under ideal conditions. Additionally, the implementation of the B-PDA MIMO detector is shown to be much simpler in the case of the linear natural mapping. Based on these two points, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping

Feeling the Heat: Investigating the dual assault of Zymoseptoria tritici and Heat Stress on Wheat (Triticum aestivum)

As a result of climate change, field conditions are increasingly challenging for crops. Research has shown how elevated temperatures affect crop performance, yet the impact of temperature on host-pathogen relationships remains unknown. Understanding the effects of combined abiotic and biotic stresses on crop plants and the plant-microbial interaction is crucial in developing strategies to improve crop stress tolerance and manage diseases effectively. Lipids sense, signal, and mitigate temperature elevation effects, and lipid remodelling plays a key role in the plant and fungal response to heat stress. Our study uses a systems approach to examine the Z. tritici wheat model system, combining transcriptomics, lipidomics, and phenotyping to decipher the impact of high-temperature stress on the plant-pathogen interaction. Microscopy in vivo and RNA-Seq analyses confirmed that Z. tritici responds to high-temperature treatments with morphological and transcriptomic changes. Temperature-related configuration of the transcriptome was associated with the accessory chromosomes and expression of ‘accessory’ pan-genome-derived genes. Metabolism-related gene expression predominated, indicated by GO enrichment and analysis of KOG classes, and large-scale lipid remodelling was likely given the proportion of lipid transport and metabolism-related expression changes in response to temperature. Changes in lipid content and composition were then validated by LC-MS analysis. Heat-responsive fungal genes and pathways, including scramblase family genes, are being tested by reverse genetics to ascertain their importance for fungal adaption to elevated temperatures. Elevated temperature schemes were applied to wheat to study the impact of combined stress on the plant-pathogen interaction, based on long-term climate data from Rothamsted Research, using transcriptomic, lipidomic and phenotypic analyses. Comparing non-infected and infected wheat plants under typical and elevated temperatures. Our initial analysis of the transcriptomic data indicates a delay in the development of Z. tritici, followed by its adaptation to the warmer environment. Once the infection was established, the fungus exhibited resilience to the impact of higher external temperatures. Our results indicate that temperature elevations associated with climate change directly impact plant-pathogen interactions. Furthermore, the study demonstrates a need for further detailed understanding to sustain crop resilience