A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback

Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FDD) massive MIMO systems, the base station (BS) relies on sustained and accurate CSI feedback from the users. However, due to the large number of antennas and users being served in massive MIMO systems, feedback overhead can become a bottleneck. In this paper, we propose a model-driven deep learning method for CSI feedback, called learnable optimization and regularization algorithm (LORA). Instead of using l1-norm as the regularization term, a learnable regularization module is introduced in LORA to automatically adapt to the characteristics of CSI. We unfold the conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural network and learn both the optimization process and regularization term by end-toend training. We show that LORA improves the CSI feedback accuracy and speed. Besides, a novel learnable quantization method and the corresponding training scheme are proposed, and it is shown that LORA can operate successfully at different bit rates, providing flexibility in terms of the CSI feedback overhead. Various realistic scenarios are considered to demonstrate the effectiveness and robustness of LORA through numerical simulations

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

International audienceIn massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatiotemporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencingattention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity

Further results on maximal ratio combining under correlated noise for multi-carrier underwater acoustic communication using vector sensors

In the isotropic marine ambient noise field, the pressure and particle velocity signal processing based on the maximal ratio combining (MRC) algorithm can effectively improve the signal-to-noise ratio (SNR) for the underwater acoustic (UWA) communication using acoustic vector sensors (VS). However, the assumption of isotropic ambient noise is not always valid, especially when combined with VS manufacturing errors, which increase the correlation between pressure and velocity branches for the noise and reduce MRC performance. Therefore, a correlation coefficient weighted MRC (CCW-MRC) method is proposed to achieve the optimal combination under correlated noise. And we derive the optimal weighting factors and apply the CCW-MRC method to the multi-carrier M-ary Frequency Shift Keying (MFSK) based UWA communication system with the single acoustic VS. Simulation and the field trial results demonstrate that the proposed CCW-MRC method can improve the received SNR by 2-3 dB compared with the traditional MRC in the multicarrier MFSK based UWA communication system using the single acoustic VS

MEGF10, a Glioma Survival-Associated Molecular Signature, Predicts IDH Mutation Status

Glioma is the most common primary brain tumor with various genetic alterations; among which, IDH mutation is the most common mutation and plays an important role in glioma early development, especially in lower grade glioma (LGG, WHO II-III). Previous studies have found that IDH mutation is tightly associated with extensive methylation across whole genome in glioma. To further investigate the role of IDH, we obtained methylation data of 777 samples from CGGA (Chinese Glioma Genome Atlas) and TCGA (The Cancer Genome Atlas) with IDH mutation status available. A package compiled under R language called Tspair was used as the main analytic tool to find potential probes that were significantly affected by IDH mutation. As a result, we found one pair of probes, cg06940792 and cg26025891, which was capable of predicting IDH mutation status precisely. The hypermethylated probe was cg06940792, designed in the promoter region of MEGF10, while the hypomethylated probe was cg26025891, designed in the promoter region of PSTPIP1. Survival analysis proved that hypermethylation or low expression of MEGF10 indicated a favorable prognosis in 983 glioma samples. Moreover, gene ontology analysis demonstrated that MEGF10 was associated with cell migration, cell proliferation, and regulation of apoptosis in glioma. All findings above can be validated in three other independent cohorts. In a word, our results suggested that methylation level and mRNA expression of MEGF10 in glioma were not only correlated with IDH mutation but also associated with clinical outcome of patients, providing potential guide for future dissection of IDH role in glioma

MOESM1 of Expression profile analysis of antisense long non-coding RNA identifies WDFY3-AS2 as a prognostic biomarker in diffuse glioma

Additional file 1. Additional figures and table