Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

Metabolomics and network analysis uncovered profound inflammation-associated alterations in hepatitis B virus-related cirrhosis patients with early hepatocellular carcinoma.

Patients with hepatitis B virus (HBV)-related liver cirrhosis (LC) are at high risk for hepatocellular carcinoma (HCC). Limitations in the early detection of HCC give rise to poor survival in this high-risk population. Here, we performed comprehensive metabolomics on health individuals and HBV-related LC patients with and without early HCC. Compared to non-HCC patients (N = 108) and health controls (N = 80), we found that patients with early HCC (N = 224) exhibited a specific plasma metabolome map dominated by lipid alterations, including lysophosphatidylcholines, lysophosphatidic acids and bile acids. Pathway and function network analyses indicated that these metabolite alterations were closely associated with inflammation responses. Using multivariate regression and machine learning approaches, we identified a five-metabolite combination that showed significant performances in differentiating early-HCC from non-HCC than α-fetoprotein (area under the curve values, 0.981 versus 0.613). At metabolomic levels, this work provides additional insights of metabolic dysfunction related to HCC progressions and demonstrates the plasma metabolites might be measured to identify early HCC in patients with HBV-related LC.

A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems.

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the "beam squint" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Pharmacokinetics/pharmacometabolomics-pharmacodynamics reveals the synergistic mechanism of a multicomponent herbal formula, Baoyuan decoction against cardiac hypertrophy.

Multicomponent herbal formulas (MCHFs) have earned a wide reputation for their definite efficacy in preventing or treating chronic complex diseases. However, holistic elucidation of the causal relationship between the bioavailable ingredients of MCHFs and their multitarget interactions is very challenging. To solve this problem, pharmacokinetics/pharmacometabolomics-pharmacodynamics (PK/PM-PD) combined with a multivariate biological correlation-network strategy was developed and applied to a classic MCHF, Baoyuan decoction (BYD), to clarify its active components and synergistic mechanism against cardiac hypertrophy (CH). First, multiple plasma metabolic biomarkers for ß-adrenergic agonist-induced CH rats were identified by using untargeted metabolomic profiling, and then, these CH-associated endogenous metabolites and the absorbed BYD-compounds in plasma at different treatment stages after oral administration of BYD were analyzed by using targeted PK and PM. Second, the dynamic relationship of BYD-related compounds and CH-associated endogenous metabolites and signaling pathways was built by using multivariate and bioinformatic correlation analysis. Finally, metabolic-related PD indicators were predicted and further verified by biological tests. The results demonstrated that the bioavailable BYD-compounds, such as saponins and flavonoids, presented differentiated and distinctive metabolic features and showed positive or negative correlations with various CH-altered metabolites and PD-indicators related to gut microbiota metabolism, amino acid metabolism, lipid metabolism, energy homeostasis, and oxidative stress at different treatment stages. This study provides a novel strategy for investigating the dynamic interaction between BYD and the biosystem, providing unique insight for disclosing the active components and synergistic mechanisms of BYD against CH, which also supplies a reference for other MCHF related research.