Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization

This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters; while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices

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

Dynamical analysis of a network-based SIR model with saturated incidence rate and nonlinear recovery rate: an edge-compartmental approach

A new network-based SIR epidemic model with saturated incidence rate and nonlinear recovery rate is proposed. We adopt an edge-compartmental approach to rewrite the system as a degree-edge-mixed model. The explicit formula of the basic reproduction number $\mathit{\boldsymbol{R_{0}}}$ is obtained by renewal equation and Laplace transformation. We find that \mathit{\boldsymbol{R_{0}}} < 1 is not enough to ensure global asymptotic stability of the disease-free equilibrium, and when \mathit{\boldsymbol{R_{0}}} > 1 , the system can exist multiple endemic equilibria. When the number of hospital beds is small enough, the system will undergo backward bifurcation at $\mathit{\boldsymbol{R_{0}}} = 1$. Moreover, it is proved that the stability of feasible endemic equilibrium is determined by signs of tangent slopes of the epidemic curve. Finally, the theoretical results are verified by numerical simulations. This study suggests that maintaining sufficient hospital beds is crucial for the control of infectious diseases

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

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

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

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

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