A deep learning driven hybrid beamforming method for millimeter wave MIMO system

The hybrid beamforming is a promising technology for the millimeter wave MIMO system,which provides high spectrum efficiency,high data rate transmission,and a good balance between transmission performance and hardware complexity.The most existing beamforming systems transmit multiple streams by formulating multiple orthogonal beams.However,the Neural network Hybrid Beamforming(NHB)adopts a totally different strategy,which combines multiple streams into one and transmits by employing a high-order non-orthogonal modulation strategy.Driven by the Deep Learning(DL)hybrid beamforming,in this work,we propose a DL-driven nonorthogonal hybrid beamforming for the single-user multiple streams scenario.We first analyze the beamforming strategy of NHB and prove it with better Bit Error Rate(BER)performance than the orthogonal hybrid beamforming even with the optimal power allocation.Inspired by the NHB,we propose a new DL-driven beamforming scheme to simulate the NHB behavior,which avoids time-consuming neural network training and achieves better BERs than traditional hybrid beamforming.Moreover,our simulation results demonstrate that the DL-driven nonorthogonal beamforming outperforms its traditional orthogonal beamforming counterpart in the presence of subconnected schemes and imperfect Channel State Information(CSI).

An intelligent task offloading algorithm(iTOA)for UAV edge computing network

Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively.

iPAS:A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output(MIMO)is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system.However,due to the massive connectivity of mobile devices,the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system.We propose a deep Monte Carlo Tree Search(MCTS)-based intelligent Pilot-power Allocation Scheme(iPAS)to address this issue.The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training.To accelerate the searching convergence,we introduce a Deep Neural Network(DNN)to predict the pilot sequence and power allocation actions.The DNN is trained in a self-supervised learning manner,where the training data is generated from the searching process of the MCTS algorithm.Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function(CDF)of the ergodic spectral efficiency compared with the previous suboptimal algorithms.

Nitrogen and boron co-doped graphene nanoribbons as peroxidase-mimicking nanozymes for enhanced biosensing

The rational design of nanozymes with superior activities is essential for improving bioassay performances.Herein,nitrogen and boron co-doped graphene nanoribbons(NB-GNRs)are prepared by a hydrothermal method using urea as the nitrogen source and boric acid as the boron source,respectively.The introduction of co-doped and edge structures provides high defects and active sites.The resultant NB-GNRs nanozymes show superior peroxidase-like activities to nitrogen-doped and boron-doped counterparts due to the synergistic effects.By taking advantage of their peroxidase-like activities,NB-GNRs are used for the first time to develop enzyme-linked immunosorbent assay for the detection of interleukin-6.The biosensors exhibit a high performance with a linear range from 0.001 ng/mL to 1000 ng/mL and a detection limit of 0.3 pg/mL.Due to their low cost and high stability,the proposed nanomaterials show great promise in biocatalysis,immunoassay development and environmental monitoring.

Elastic rescattering photoelectron distributions entering the relativistic regime

We report photoelectron energy spectra and angular distributions for ionization with elastic scattering in ultra- strong laser fields. Noble gas species with Hartree-Fock scattering potentials show a reduction in elastic rescat- tering with the increasing energy of ultrastrong fields and when the Lorentz deflection of the photoelectron exceeds its wave function spread. The relativistic extension of a three-step recollision model is well-suited to the ultrastrong intensity regime （〉10^17 W/cm2） that lies between traditional strong fields and extreme relativistic interactions.