Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

Hybrid architecture design enhances the areal capacity and cycling life of low-overpotential nanoarray oxygen electrode for lithium–oxygen batteries

Transition metal oxide(TMO)nanoarrays are promising architecture designs for self-supporting oxygen electrodes to achieve high catalytic activities in lithium-oxygen(Li-O2)batteries.However,the poor conductive nature of TMOs and the confined growth of nanostructures on the limited surfaces of electrode substrates result in the low areal capacities of TMO nanoarray electrodes,which seriously deteriorates the intrinsically high energy densities of Li-O2 batteries.Herein,we propose a hybrid nanoarray architecture design that integrates the high electronic conductivity of carbon nanoflakes(CNFs)and the high catalytic activity of Co3 O4 nanosheets on carbon cloth(CC).Due to the synergistic effect of two differently featured components,the hybrid nanoarrays(Co3 O4-CNF@CC)achieve a high reversible capacity of3.14 mA h cm-2 that cannot be achieved by only single components.Further,CNFs grown on CC induce the three-dimensionally distributed growth of ultrafine Co3 O4 nanosheets to enable the efficient utilization of catalysts.Thus,with the high catalytic efficiency,hybrid Co3 O4-CNF@CC also achieves a more prolonged cycling life than pristine TMO nanoarrays.The present work provides a new strategy for improving the performance of nanoarray oxygen electrodes via the hybrid architecture design that integrates the intrinsic properties of each component and induces the three-dimensional distribution of catalysts.

The Methodology of Application Development for Hybrid Architectures

This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.

Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

Log Anomaly Detection Through GPT-2 for Large Scale Systems

As the scale of software systems expands,maintaining their stable operation has become an extraordinary challenge.System logs are semi-structured text generated by the recording function in the source code and have important research significance in software service anomaly detection.Existing log anomaly detection methods mainly focus on the statistical characteristics of logs,making it difficult to distinguish the semantic differences between normal and abnormal logs,and performing poorly on real-world industrial log data.In this paper,we propose an unsupervised framework for log anomaly detection based on generative pre-training-2(GPT-2).We apply our approach to two industrial systems.The experimental results on two datasets show that our approach outperforms state-of-the-art approaches for log anomaly detection.

Improving Playback Quality of Peer-to-Peer Live Streaming Systems by Joint Scheduling and Distributed Hash Table Based Compensation

In Peer-to-Peer(P2P) streaming systems,video data may be lost since peers can join and leave the overlay network randomly,thereby deteriorating the video playback quality.In this paper we propose a new hybrid mesh and Distributed Hash Table(DHT) based P2P streaming system,called HQMedia,to provide high playback quality to users by maintaining high data dissemination resilience with a low overhead.In HQMedia,peers are classified into Super Peers(SP) and Common Peers(CP) according to their online time.SPs and CPs form a mesh structure,while SPs alone form a new Streaming DHT(SDHT) structure.In this hybrid architecture,we propose a joint scheduling and compensation mechanism.If any frames cannot be obtained during the scheduling phase,an SDHT-based compensation mechanism is invoked for retrieving the missing frames near the playback point.We evaluate the performance of HQMedia by both theoretical analysis and intensive simulation experiments on large-scale networks to demonstrate the effectiveness and scalability of the proposed system.Numerical results show that HQMedia significantly outperforms existing mesh-based and treebased P2P live streaming systems by improving playback quality with only less than 1% extra maintenance overhead.

A cost-e ective SCTP extension for hybrid vehicular networks

Connected vehicles are promoted with the use of di erent communication technologies for diverse applications and services.There is an ongoing debate in the research and industry communities whether short range communications based on IEEE 802.11p or cellular based on 3GPP LTE should be used for vehicular communications.We propose a mechanism to utilise both short range and cellular communications simultaneously in a cost effcient way while providing the required quality of service to the users.A host connected to multiple networks is referred to as a multi-homed node and SCTP(Stream Control Transmission Protocol)is an IETF standard which supports multi-homing.We propose an extension to SCTP that takes into account not only path quality but also the cost of using each network.It is shown that the combination of QoS and cost information increases economic bene ts for provider and end-users,while providing increased packet throughput.

The TianHe-1A Supercomputer: Its Hardware and Software

This paper presents an overview of TianHe-lA （TH-1A） supercomputer, which is built by National University of Defense Technology of China （NUDT）. TH-1A adopts a hybrid architecture by integrating CPUs and GPUs, and its interconnect network is a proprietary high-speed communication network. The theoretical peak performance of TH-1A is 4700TFlops, and its LINPACK test result is 2566TFlops. It was ranked the No. 1 on the TOP500 List released in November, 2010. TH-1A is now deployed in National Supercomputer Center in Tianjin and provides high performance computing services. TH-1A has played an important role in many applications, such as oil exploration, weather forecast, bio-medical research.

Simulation of dynamic fluid-solid interactions with an improved direct-forcing immersed boundary method

Dynamic fluid-solid interactions are widely found in chemical engineering, such as in particle-laden flows, which usually contain complex moving boundaries. The immersed boundary method （IBM） is a convenient approach to handle fluid-solid interactions with complex geometries. In this work, Uhlmann＇s direct-forcing IBM is improved and implemented on a supercomputer with CPU-GPU hybrid architec- ture. The direct-forcing IBM is modified as follows： the Poisson＇s equation for pressure is solved before evaluation of the body force, and the force is only distributed to the Cartesian grids inside the immersed boundary. A multidirect forcing scheme is used to evaluate the body force. These modifications result in a divergence-free flow field in the fluid domain and the no-slip boundary condition at the immersed boundary simultaneously. This method is implemented in an explicit finite-difference fractional-step scheme, and validated by 2D simulations of lid-driven cavity flow, Couette flow between two concentric cylinders and flow over a circular cylinder. Finally, the method is used to simulate the sedimentation of two circular particles in a channel. The results agree very well with previous experimental and numerical data, and are more accurate than the conventional direct-forcing method, especially in the vicinity of a moving boundary.