Specific Absorption Rate-Aware Beamforming in MISO Downlink SWIPT Systems

This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides the received signal into two parts: one for information decoding and the other for energy harvesting with a practical non-linear rectification model. The problem of interest is to maximize as much as possible the received signal-to-interference-plus-noise ratio (SINR) and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints by optimizing the transmit beamforming at the transmitter and the power splitting ratios at different receivers. The optimal beamforming and power splitting solutions are obtained with the aid of semidefinite programming and bisection search. Low-complexity fixed beamforming and hybrid beamforming techniques are also studied. Furthermore, we study the effect of imperfect channel information and radiation matrices, and design robust beamforming to guarantee the worst-case performance. Simulation results demonstrate that our proposed algorithms can effectively deal with the radio exposure constraints and significantly outperform the conventional transmission scheme with power backoff.Comment: to appear in TCO

Deep learning-based edge caching for multi-cluster heterogeneous networks

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability

Deep learning based predictive beamforming design

This paper investigates deep learning techniques to predict transmit beamforming based on only historical channel data without current channel information in the multiuser multiple-input-single-output downlink. This will significantly reduce the channel estimation overhead and improve the spectrum efficiency especially in high-mobility vehicular communications. Specifically, we propose a joint learning framework that incorporates channel prediction and power optimization, and produces prediction for transmit beamforming directly. In addition, we propose to use the attention mechanism in the Long Short-Term Memory Recurrent Neural Networks to improve the accuracy of channel prediction. Simulation results using both a simple autoregressive process model and the more realistic 3GPP spatial channel model verify that our proposed predictive beamforming scheme can significantly improve the effective spectrum efficiency compared to traditional channel estimation and the method that separately predicts channel and then optimizes beamforming

dsRNA Virus Model Molecule and the Mechanism of PRRs and its Research Progress in Female Reproductive Tract Infections

Female animal genital tract opening on the body surface, prone to bacterial, viral, parasitic, and other pathogenic microorganism infections, leading to genital tract infectious diseases, such as endometritis, cervicitis, vaginitis, etc. Severe infection can lead to infertility, abortion, and even fetal death. Double-stranded RNA (dsRNA) is an important model molecule, which is widely present in the genome of viruses and generated in the process of virus replication. In mammals, dsRNA is considered to be an innate immune response signal for viral infection, which binds to the corresponding pattern-recognition receptors (PRRs) In vivo and then exerts biological functions. This review summarizes the signal transduction pathway induced by the binding of dsRNA model molecules to PRRs, research status of female genital tract infections and research progress of dsRNA in simulating viral infection in the female genital tract

Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate

Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes

Progressive collapse resistance mechanism of RC frame structure considering reinforcement corrosion

Corrosion causes reduction in cross-sectional area of reinforcement, deterioration of mechanical properties, and degradation of bonding properties between reinforced concrete, which are the most important factors leading to the degradation of structural service performance. In order to investigate the progressive collapse mechanism of a corroded reinforced concrete frame structure, the failure modes, characteristics of the vertical displacement, and load capacity are studied using the finite element method. Based on existing experimental research, the established model is verified, and the influence of different influencing factors on the progressive collapse mechanism is analyzed. The results show that the corrosion of the reinforcement affects the yield load, peak load, and ultimate load of the reinforced concrete substructure. As the corrosion rate increases, the tensile arch action shows a particularly severe deterioration. The variation of concrete strength and the height–span ratio affects the substructure’s load-bearing capacity much more significantly than the stirrup spacing

Influence of a groove-structured vortex generator on the drag reduction characteristics of a multiphase pump

The oil–gas mixture pump significantly contributes to marginal oil field extraction and remote transportation of deep-sea oil. Nevertheless, during the operation of the mixture pump, it is inevitable to encounter problems like the separation of the mixed media from the hydraulic components as well as the gas phase from the liquid phase, which leads to enhancing the flow resistance of the mixed media. Therefore, this study investigates the influence of a groove-structure vortex generator on the drag reduction characteristics of a helical axial-flow gas–liquid multiphase pump under the design flow rate condition and various inlet gas content rates. The findings show that the vortex generator with diverse groove depths can prevent the separation of the mixed media from the blade suction surface effectively and minimize the flow resistance of the media in the 1/10 of the blade inlet. In particular, excellent drag reduction results were gained with a maximum drag reduction rate of 36.7% when the relative depth was 3/40. In addition, the efficiency of the mixture pump increased by a maximum of 2.1%, and the head increased by a maximum of 4.3%. The significance of this study lies in its potential to further optimize the design and performance of gas–liquid multiphase pumps. It provides new insights into the design and application of vortex generators. It offers robust support for the optimization and enhancement of gas–liquid multiphase pumps