Transformer-based Joint Source Channel Coding for Textual Semantic Communication

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission.Comment: 6 pages, 5 figures. Accepted by IEEE/CIC ICCC 202

A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity

Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels

Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems

A Novel Multitask Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This letter aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity

Sparse Code Multiple Access with Enhanced K-Repetition Scheme: Analysis and Design

This work presents a novel K-Repetition based Hybrid Automatic Repeat reQuest (HARQ) scheme for uplink sparse code multiple access (SCMA) systems. Our core idea is to apply network coding (NC) principle to re-encode different packets (after channel coding and interleaving) or their fragments, where K-Repetition is an emerging HARQ technique (recommended in 3GPP Release 15) for enhanced reception in future massive machine-type communications. Such a proposed scheme is referred to as the NC aided K-repetition SCMA (NCKSCMA) in this paper. We aim to understand the optimal NCKSCMA design criteria for maximizing the channel diversity as well as the efficient receiver processing for superior error rate performances. It is found that NC can enable a larger diversity order for NCK-SCMA with fewer resources (i.e., higher spectrum efficiency). Toward this objective, some novel design criteria are developed for the efficient configuration of NCK-SCMA. Moreover, we propose an iterative network decoding and SCMA detection (INDSD) algorithm for robust and low-complexity recovery of the transmit data from a low-density parity-check (LDPC) coded uplink NCK-SCMA system. Simulation results demonstrate that the proposed NCK-SCMA lead to higher throughput and improved reliability over the conventional KSCMA

Effect of Salt Stress on Growth, Physiological Parameters, and Ionic Concentration of Water Dropwort (Oenanthe javanica) Cultivars

Salt stress is an important environmental limiting factor. Water dropwort (Oenanthe javanica) is an important vegetable in East Asia; however, its phenotypic and physiological response is poorly explored. For this purpose, 48 cultivars of water dropwort were grown hydroponically and treated with 0, 50, 100, and 200 mm NaCl for 14 days. Than their phenotypic responses were evaluated, afterward, physiological studies were carried out in selected sensitive and tolerant cultivars. In the present study, the potential tolerant (V11E0022) and sensitive (V11E0135) cultivars were selected by screening 48 cultivars based on their phenotype under four different levels of salt concentrations (0, 50, 100, and 200 mm). The results depicted that plant height, number of branches and leaves were less effected in V11E0022, and most severe reduction was observed in V11E0135 in comparison with others. Than the changes in biomass, ion contents, accumulation of reactive oxygen species, and activities of antioxidant enzymes and non-enzymatic antioxidants were determined in the leaves and roots of the selected cultivars. The potential tolerant cultivar (V11E0022) showed less reduction of water content and demonstrated low levels of Na+ uptake, malondialdehyde, and hydrogen peroxide (H2O2) in both leaves and roots. Moreover, the tolerant cultivar (V11E0022) showed high antioxidant activities of ascorbate peroxidase (APX), superoxide dismutase, peroxidase, catalase (CAT), reduced glutathione (GSH), and high accumulation of proline and soluble sugars compared to the sensitive cultivar (V11E0135). These results suggest the potential tolerance of V11E0022 cultivar against salt stress with low detrimental effects and a good antioxidant defense system. The observations also suggest good antioxidant capacity of water dropwort against salt stress. The findings of the present study also suggest that the number of branches and leaves, GSH, proline, soluble sugars, APX, and CAT could serve as the efficient markers for understanding the defense mechanisms of water dropwort under the conditions of salt stress

A Novel Non-Coherent SCMA With Massive MIMO

The synergistic amalgamation of sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) technologies can be exploited for improving spectral efficiency and providing enhanced wireless services to massive users. In this case, however, channel estimation is a burning issue with the increasing number of users and/or antennas. To tackle this problem, we propose a novel non-coherent transmission scheme for SCMA, referred to as NC-SCMA. In the proposed NC-SCMA, each user first maps its binary data to sparse codewords, and then perform differential modulation on the non-zero dimensions. Upon receiving all users’ signals, we leverage the channel hardening effect to carry out differential demodulation and multi-user detection without any instantaneous channel state information. In addition, the design of the sparse codebooks in the NC-SCMA system is investigated with the aid of the pair-wise probability. Numerical results demonstrate the superiority of the proposed technique over the benchmark scheme in terms of bit error rate performance

Gut microbiota alterations are associated with functional outcomes in patients of acute ischemic stroke with non-alcoholic fatty liver disease

IntroductionPatients with acute ischemic stroke (AIS) with non-alcoholic fatty liver disease (NAFLD) frequently have poor prognosis. Many evidences suggested that the changes in gut microbiota may play an important role in the occurrence and development of AIS patients with NAFLD. The purpose of this study was to explore microbial characteristics in patients of AIS with NAFLD, and the correlation between gut microbiota and functional outcomes.MethodsThe patients of AIS were recruited and divided into NAFLD group and non-NAFLD group. The stool samples and clinical information were collected. 16 s rRNA sequencing was used to analyze the characteristics of gut microbiota. The patients of AIS with NAFLD were followed-up to evaluate the functional outcomes of disease. The adverse outcomes were determined by modified Rankin scale (mRS) scores at 3 months after stroke. The diagnostic performance of microbial marker in predicting adverse outcomes was assessed by recipient operating characteristic (ROC) curves.ResultsOur results showed that the composition of gut microbiota between non-NAFLD group and NAFLD group were different. The characteristic bacteria in the patients of AIS with NAFLD was that the relative abundance of Dorea, Dialister, Intestinibacter and Flavonifractor were decreased, while the relative abundance of Enorma was increased. Moreover, the characteristic microbiota was correlated with many clinical parameters, such as mRS scores, mean arterial pressure and fasting blood glucose level. In addition, ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish functional dependence patients and functional independence patients in AIS with NAFLD (area under curve is 0.765 and 0.882 respectively).ConclusionThese findings revealed the microbial characteristics in patients of AIS with NAFLD, and further demonstrated the predictive capability of characteristic microbiota for adverse outcomes in patients of AIS with NAFLD

Spatial-temporal Characteristics and Source Apportionment of Ambient VOCs in Southeast Mountain Area of China

Seasonal variations and sources of ambient volatile organic compounds (VOCs) were conducted at the county and rural sites in a mountain area of southeastern China. The results showed that the pattern of VOC concentrations was dominated by oxygenated VOCs (37.6%) and alkanes (25.8%), followed by halocarbons (16.8%), alkenes (11.9%), aromatics (6.87%), and alkynes (1.04%). Based on the OH radical loss rate (LOH) and ozone formation potential (OFP) analysis, alkenes had the highest chemical activity, especially the contribution of isoprene in rural areas. Aromatics contributed the most to secondary organic aerosols (SOA) formation in both county and rural areas. Source apportionment of VOCs were quantified by the positive matrix factorization (PMF) model, including industrial emissions and combustion burning (30.1% and 43.3% in the county and rural areas, respectively) and vehicle exhausts (30.3% and 10.8%), followed by solvent usage (17.1% and 5.2%), liquid petroleum gas (LPG) usage and fuel evaporation (14.2% and 10.0%), and biogenic source (8.3% and 30.6%). The backward air trajectories showed that air mass in spring was mainly originated from the intercity transmission, while the air clusters in autumn came from the northern areas through long-range transport. The study was helpful to understand the pollution characteristics in the mountainous area and provides a scientific basis for local O3 and PM2.5 pollution control