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

High-dose Radiation Associated with Improved Survival in IDH-wildtype Low-grade Glioma

Abstract PurposeAs molecular advances have deepened the knowledge on low-grade glioma (LGG), we investigated the effect of higher radiation dose on the survival of IDH-wildtype (IDHwt) LGG.MethodsIn the current study, 52 IDHwt LGG patients who received radiotherapy were enrolled from the Chinese Glioma Genome Atlas dataset. Radiation doses &gt; 54 Gy were defined as high-dose, whereas doses ≤ 54 Gy were defined as low-dose. We performed univariate and multivariate survival analyses to examine the prognostic role of high-dose radiotherapy.ResultsIn total, the radiation dose ranged from 48.6 Gy to 61.2 Gy, with a median of 55.8 Gy, and 31 patients were grouped into high-dose radiation. Univariate survival analysis indicated that high-dose radiotherapy (p = 0.015), tumors located in the frontal lobe (p = 0.009), and pathology of astrocytoma (p = 0.037) were significantly prognostic factors for overall survival. In multivariate survival analysis, high-dose radiotherapy (p = 0.028) and tumors located in the frontal lobe (p = 0.016) were independently associated with better overall survival.ConclusionIn conclusion, high-dose radiotherapy independently improved the survival of IDHwt LGG. This can guide treatments for glioma with known molecular characteristics.</jats:p

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

High-dose radiation associated with improved survival in IDH-wildtype low-grade glioma

Abstract Background As molecular advances have deepened the knowledge on low-grade glioma (LGG), we investigated the effect of higher radiation dose on the survival of IDH-wildtype (IDHwt) LGG. Methods In the current study, 52 IDHwt LGG patients who received radiotherapy were enrolled from the Chinese Glioma Genome Atlas dataset. Radiation doses &gt; 54 Gy were defined as high-dose, whereas doses ≤ 54 Gy were defined as low-dose. We performed univariate and multivariate survival analyses to examine the prognostic role of high-dose radiotherapy. Results In total, the radiation dose ranged from 48.6 Gy to 61.2 Gy, with a median of 55.8 Gy, and 31 patients were grouped into high-dose radiation. Univariate survival analysis indicated that high-dose radiotherapy (p = 0.015), tumors located in the frontal lobe (p = 0.009), and pathology of astrocytoma (p = 0.037) were significantly prognostic factors for overall survival. In multivariate survival analysis, high-dose radiotherapy (p = 0.028) and tumors located in the frontal lobe (p = 0.016) were independently associated with better overall survival. Conclusions In conclusion, high-dose radiotherapy independently improved the survival of IDHwt LGG. This can guide treatments for glioma with known molecular characteristics. </jats:sec

Transcriptomic Profiling Identifies a DNA Repair–Related Signature as a Novel Prognostic Marker in Lower Grade Gliomas

Abstract Background: Gliomas are the most common and malignant intracranial tumors. The standard therapy is surgical resection combined with radiotherapy and chemotherapy. However, the emergence of radioresistance and chemoresistance, which is largely due to DNA damage repair, limits the therapeutic efficacy. Therefore, we identified a high-efficiency DNA damage repair–related risk signature as a predictor for prognosis in lower grade glioma. Methods: The signature was developed and validated in two independent datasets of the Chinese Glioma Genome Atlas (172 samples) and The Cancer Genome Atlas (451 samples). The time-dependent ROC curve, Cox regression, Nomogram, and Kaplan–Meier analyses were performed to evaluate the prognostic performance of the risk signature. The Metascape and IHC staining were performed to reveal the potential biological mechanism. GraphPad prism, SPSS, and R language were used for statistical analysis and graphical work. Results: This signature could distinguish the prognosis of patients, and patients with high-risk scores exhibited short survival time. The time-dependent ROC curve, Cox regression, and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with the IDH mutation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell cycle and DNA repair. These mechanisms were confirmed in patient samples. Conclusions: The DNA damage repair–related signature was an independent and powerful prognostic biomarker in lower grade glioma. Impact: The signature may potentially improve risk stratification of patients and provide a more accurate assessment of personalized treatment in clinic. </jats:sec