Throughput Requirements Research Articles

Clustering Algorithm for a Spaceborne Lightning Imager: Design, Trade-Off, and FPGA Implementation

In this paper, the development and implementation of the clustering algorithm (CA) developed for an instrument to detect lightning phenomena (Lightning Imager) is presented. The aim of the Lightning Imager (LI) instrument is to provide information relevant to the localization and the radiance of lightning events with respect to terrestrial systems. A CMOS sensor with $1170 \times 1000$ resolution and 1000 fps frame-rate is used on the instrument, making the LI able to identify smaller lightning in terms of dimension (minimum diameter 10 km) and temporal pulse duration (minimum 0, 6ms). The amount of acquired data is very large, but only a small subset of pixels contains useful lightning information, thus information clustering is fundamental for downlink data-rate reduction. Raw sensor data are processed by application-specific integrated circuits (ASIC) in order to extract pixels coordinates belonging to lightning flashes (detected transients), which are then directly sent to the CA implemented in a field-programmable gate array (FPGA). Detected transients must be processed as they come to the CA due to high throughput requirement. The CA objective is to define rectangular windows that enclose more detected transients (DTs) in order to send more compact information to Earth. The design presented in this paper was carried out through the development of a high-level model in order to verify algorithm functionality and compliance with performance requirements. A lightning event generator was developed to simulate the DTs coming from the ASIC part.The hardware architecture was conceived and a bit-true model was developed to evaluate the implementation loss.

Energy Provision Minimization in Wireless Powered Communication Networks With Node Throughput Requirement

Radio-frequency (RF) energy harvesting is one of the promising technologies in wireless networks. This paper considers wireless powered communication networks (WPCNs) where one hybrid access point (H-AP) coordinates RF energy/information transmissions to/from a set of nodes. For the important type of WPCNs where the nodes have throughput requirement, we aim to minimize H-AP's energy provision (EP) via jointly optimizing the time allocation of energy/information transmissions and H-AP's transmission power while satisfying node throughput requirement. More specifically, the half-duplex WPCN is considered first. We prove that H-AP's energy transmission power must be the maximum allowed value to achieve the minimal EP, then show that the problem is convex under this condition and finally design an algorithm applying golden section search and bisection search jointly to efficiently obtain the optimal time allocation. Second, the full-duplex WPCN is considered. Its EP minimization problem is proved convex. However, due to the hardness of solving it directly, we turn to solve the feasibility problem (FP) for a given EP per unit time. We construct a convex optimization problem based on this FP and design a dual subgradient algorithm for finding its optimal solution, which indicates whether this FP is feasible. The minimal EP per unit time and its corresponding optimal solution are found by using the bisection search method. Simulation results show that, the EP-minimized schemes are able to greatly reduce H-AP's EP as compared to the baseline schemes.

Performance Analysis of V2V Communications: A Novel Scheduling Assignment and Data Transmission Scheme

Vehicle-to-vehicle (V2V) communications is a key enabler for traffic management and road safety, where vehicles exchange real-time information. Major challenges for practical V2V communications include strict requirements for latency, collision avoidance, throughput, and reliability. This paper proposes a novel scheduling assignment (SA) and data transmission scheme for mode 4-based non-adjacent V2V communications. The main objective of the proposed scheme is to decrease the contention of radio resources to achieve better performance gain. To achieve this, we propose the SA-embedded data message transmission scheme instead of transmitting the SA and data messages separately. To investigate the achievable performance, a mathematical model for the proposed scheme has been derived using the finite state Markov chain. Using this mathematical model, we have derived expressions (validated by simulations) to quantify the collision probability, throughput, transmission probability, and the load on the network. Furthermore, we explore several design parameters and their effects on system performance. Through system level simulation and analytical results, the significant improvement of overall network performance in terms of the throughput maximization and resource collision minimization for the proposed scheme has been shown.

Interview

interview

Completion Time Minimization With Path Planning for Fixed-Wing UAV Communications

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations.

Fast algorithm for HEVC intra‐coding implemented by preprocessing

Compared to previous video compression standards H.264/AVC, high-efficiency video coding (HEVC) introduces a recursive quad-tree structure in intra-coding and adds intra-mode from 9 to 35 to achieve higher coding efficiency. The update significantly improves the performance of intra-coding. However, it greatly increases the amount of computation, and this will increase the need for the fast algorithm and hardware design that can satisfy the real-time encoding of HEVC encoder. In this study, the authors propose a fast intra-coding algorithm based on analysis of original pixel gradient texture. The algorithm consists of two steps, fast coding unit size decision and reduction of candidate modes. The experimental results show that compared with the HM16.7 encoder, the optimisation algorithm can obtain 50.7% time reduction on average with 1.32% Bjontegaard distortion (BD)-rate increase and 0.07 dB BD-peak-signal-to-noise ratio loss. Meanwhile, this study designs a pre-processing hardware structure based on original pixels. With TSMC 90 nm complementary metal oxide semiconductor technology, the proposed structure can achieve 625 MHz working frequency at the cost of 2094 gates, which can fulfil the throughput requirement of 8K × 4K@46 fps real-time encoding.

Efficient QC-LDPC Encoder for 5G New Radio

This paper presents a novel efficient encoding method and a high-throughput low-complexity encoder architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes for the 5th-generation (5G) New Radio (NR) standard. By storing the quantized value of the permutation information for each submatrix instead of the whole parity check matrix, the required memory storage size is considerably reduced. In addition, sharing techniques are employed to reduce the hardware complexity. The encoding complexity of the proposed method was analyzed, and indicated a substantial reduction in the required area as well as memory storage when compared with existing state-of-the-art encoding approaches. The proposed method requires only 61% gate area, and 11% ROM storage when compared with a similar LDPC encoder using the Richardson–Urbanke method. Synthesis results on TSMC 65-nm complementary metal-oxide semiconductor (CMOS) technology with different submatrix sizes were carried out, which confirmed that the design methodology is flexible and can be adapted for multiple submatrix sizes. For all the considered submatrix sizes, the throughput ranged from 22.1–202.4 Gbps, which sufficiently meets the throughput requirement for the 5G NR standard.

Joint Energy and QoS-Aware Memetic-Based Scheduling for M2M Communications in LTE-M

As a result of the increasing number of machine-type devices connected through the Internet, several challenges remain to date to support machine-to-machine (M2M) communications over long term evolution (LTE) cellular networks. In this paper, we tackle one important challenge of scheduling M2M traffic in uplink over LTE-M mobile networks. We propose a novel cross-layer scheme that considers packet scheduling in time and frequency domain using a memetic-based algorithm and aims to optimize resource allocation of M2M devices by considering their quality of service (QoS) needs while minimizing at the same time their energy consumption. After integrating an energy module for LTE in NS3 simulator, we perform simulations in a realistic M2M scenario and we evaluate the results which show how our proposed scheduling scheme outperforms other existing scheduling methods from the literature such as round robin and proportional-fair algorithms in terms of throughput, energy consumption and the percentage of satisfied devices with regard to their delay and throughput requirements.

Fully Decentralized Approximate Zero-Forcing Precoding for Massive MIMO Systems

We analyze the downlink of a massive multiuser multiple input, multiple output (MIMO) system where antenna units at the base station are connected in a daisy chain without a central processing unit and only possess local channel knowledge. For this setup, we develop and analyze a linear precoding algorithm for suppressing interuser interference. It is demonstrated that the algorithm is close to zero-forcing precoding in terms of performance for a large number of antennas. Moreover, we show that with careful scheduling of processing across antennas, requirements for interconnection throughput are reduced compared with the fully centralized solution. Favorable tradeoff between performance and interconnection throughput makes the daisy chain a viable candidate topology for real-life implementations of base stations in MIMO systems where the number of antennas is very large.

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.

Licensed and Unlicensed Bands Allocation for Cellular Users: A Matching-Based Approach

Long term evolution-unlicensed (LTE-U) technologies allow cellular users (CUs) to be served in unlicensed bands. However, how a CU properly chooses between licensed and unlicensed bands and how a CU can utilize the unlicensed bands without jeopardizing other unlicensed-band users, e.g., Wi-Fi users (WUs), remain as open problems. In this letter, we aim to optimize the allocation of radio resources in both licensed and unlicensed bands in an LTE-U and Wi-Fi coexisting scenario, where a CU can be allocated either a licensed or unlicensed band. We formulate a multi-objective optimization problem to maximize every CU's utility, while guaranteeing the throughput requirements of both CUs and WUs. We propose an algorithm based on one-to-one and many-to-one matching algorithms to solve it. The stability, Pareto optimality, and convergence of the proposed matching-based algorithm are proven. Simulation results show that the proposed algorithm can achieve near-optimal performance but with a low computational complexity.

Femtosecond lasers: the ultimate tool for high-precision 3D manufacturing

Abstract The ever-growing trend of device multifunctionality and miniaturization puts enormous burden on existing manufacturing technologies. The requirements for precision, throughput, and cost become increasingly harder to achieve with minimal room for compromises. Femtosecond lasers, which saw immense development throughout the last few decades, have been proven time and time again to be a superb tool capable of standing up to the challenges posed by modern science and the industry for ultrahigh-precision material processing. Thus, this paper is dedicated to provide an outlook on how femtosecond pulses are revolutionizing modern manufacturing. We will show how they are exploited for various kinds of material processing, including subtractive (ablation, cutting, and etching), additive (lithography and laser-induced forward transfer), or hybrid subtractive-additive cases. The advantages of using femtosecond lasers in such applications, with main focus on how they enable the most precise kinds of material processing, will be highlighted. Future prospects concerning emerging industrial applications and the future of the technology itself will be discussed.

Survey and Performance Evaluation of the Upcoming Next Generation WLANs Standard - IEEE 802.11ax

With the ever-increasing demand for wireless traffic and quality of services (QoS), wireless local area networks (WLANs) have developed into one of the most dominant wireless networks that fully influence human life. As the most widely used WLANs standard, IEEE 802.11 will release the upcoming next generation WLANs standard amendment: IEEE 802.11ax. Thus, this article briefly surveys the key technologies of IEEE 802.11ax. Furthermore, performance requirements of IEEE 802.11ax are evaluated via a proposed systems and link-level integrated simulation platform (SLISP). Simulations results confirm that IEEE 802.11ax significantly improves the user experience in high-density deployment, while successfully achieves the average per user throughput requirement in project authorization request (PAR) of IEEE 802.11ax by four times compared to the legacy IEEE 802.11. To the best of our knowledge, this article is the first work to thoroughly and deeply evaluate the compliance of the performance requirements of IEEE 802.11ax.

EGFR point mutation detection of single circulating tumor cells for lung cancer using a micro-well array

In view of their critical function in metastasis, characterization of single circulating tumor cells (CTCs) can provide important clinical information to monitor tumor progression and guide personal therapy. Single-cell genetic analysis methods based on microfluidics have some inherent shortcomings such as complicated operation, low throughput, and expensive equipment requirements. To overcome these barriers, we developed a simple and open micro-well array containing 26,208 units for either nuclear acids or single-cell genetic analysis. Through modification of the polydimethylsiloxane surface and optimization of chip packaging, we addressed protein adsorption and solution evaporation for PCR amplification on a chip. In the detection of epidermal growth factor receptor (EGFR) exon gene 21, this micro-well array demonstrated good linear correlation at a DNA concentration from 1 × 101 to 1 × 105 copies/μL (R2 = 0.9877). We then successfully integrated cell capture, lysis, PCR amplification, and signal read-out on the micro-well array, enabling the rapid and simple genetic analysis of single cells. This device was used to detect duplex EGFR mutation genes of lung cancer cell lines (H1975 and A549 cells) and normal leukocytes, demonstrating the ability to perform high-throughput, massively parallel duplex gene analysis at the single-cell level. Different types of point mutations (EGFR-L858R mutation or EGFR-T790M mutation) were detected in single H1975 cells, further validating the significance of single-cell level gene detection. In addition, this method showed a good performance in the heterogeneity detection of individual CTCs from lung cancer patients, required for micro-invasive cancer monitoring and treatment selection.

Rendering differential performance preference through intelligent network edge in cloud data centers

SummarySharing the network infrastructure, the performance of emerging distributed applications and services in data centers is directly impacted by the network. As these applications are becoming more and more demanding, it is challenging to satisfy their requirements of low latency, high throughput, and low packet loss rate simultaneously. Prior approaches typically resort to flow control or scheduling mechanisms, prioritizing flows according to their demands. However, none of the methods can solely satisfy the various demands of data center applications. Addressing this challenge, we propose tasch, a preference aware flow scheduling mechanism equipped in the software network edge (ie, end‐host networking). This mechanism utilizes multiple separate queues for flows with different preferences, which guarantees low packet delay for latency‐sensitive flows and provides bandwidth guarantees for throughput‐sensitive flows. A coordinating algorithm is presented to share the network resource among multiple queues with pareto‐optimality. tasch is implemented as a thin and plugable kernel module in Linux based hypervisors, which lies between the complicated physical network and tenants VMs. Subsequently, based on the flow traces of real‐world applications, extensive experiments were conducted to verify the effectiveness of network management mechanism.

Diagnostic Agent Based Inter-Process Communication Aware Monitoring System for Wireless Sensor Networks

Process failures are instigated by underlying errors and faults in various layers of WSN (Wireless Sensor Network) communication protocol stack. Therefore, efficient and effective monitoring systems for fault detection and diagnosis are imperative for fault tolerance and robust operation of WSN to meet critical application requirements for reliability and throughput. Existing detection-diagnosis regimen are either centralized or distributed and network monitoring is performed passively or actively. This work presents a diagnostic agent based inter-process communication aware monitoring system for WSNs. Diagnostic agent actively performs probe-based process execution tracking and examines the effects of errors, omissions and channel misbehavior on process execution at node, link and network levels to implement failure detection and fault diagnosis. Such diagnosis is performed through the inference of inter-process communication of stacked and peer layer processes on sender and receiver side. The monitoring system has been implemented in Castalia simulator for WSN. Local diagnostic agent is implemented on sensor nodes for self-monitoring and network wide fault diagnosis is performed by global diagnostic agent on cluster head. Simulation results show that the system performs robust root cause analysis of critical process failures due to errors in stacked and peer layer processes. The decentralized distribution of diagnostic load on sensor nodes and cluster head produces lesser communication overhead and is energy efficient.

The mathematical models and program complex for synthesis of reciprocating extruders with adjustable configurations

The program complex which enables solving tasks of structural and parametrical synthesis of reciprocating extruders with adjustable configurations has been developed. The complex includes modules for extruder brand choice, assembly and visualization of 3D model of extruder screw, calculation of geometrical parameters of elements and sections of assembled screw configurations, synthesis of a mathematical model of extrusion, and calculation of its output parameters. The complex is configured to various types of polymeric films, as well as throughput and energy consumption requirements. The combined modeling method is implemented by the complex. It consists of synthesis of a static model for calculation of polymeric material state parameters, extruder throughput and energy consumption, indices of extrudate quality and a dynamic model for calculation of average residence time. Calculation results are visualized in the form of 3D graphs of criterion indices’ dependence on controlling influences. The complex is an effective tool for helping production personnel determine screw configurations and extruder regime parameters, ensuring required extrudate quality when performing restrictions for throughput and energy consumption for various types of films.

Datacentre TCP Protocol of Centralized Window Control

There is an important class of applications in data centre called online dataintensive (OLDI) application, such as search engine, electronic shopping, and advertising, which has low latency, high throughput, and high burstiness. For better user experience, the query response delay in the data centre is controlled under 300 milliseconds. DCTCP can better meet the requirements of OLDI, but all the switches in the data centre are required to support RED. For data centre that does not fully support RED switch, TCP protocol can only be used. TCP Incast will appear in the application, which is far from the requirements of OLDI application. This paper proposes the TCP protocol of data centre based on window control. It is in existing switches and network devices,and centralized control makes the send window match with the switch queue cache. In the case of ensuring low latency, it keeps the switch queue in a small range of fluctuation, enabling the data centre to meet low latency, high throughput and high burstiness application requirements. Simulation experiments verify that WCTCP congestion control method can effectively solve the above problems. It is 5 times lower than TCPNewReno packet loss rate, 6 times higher than the network utilization rate, and the network performance is close to the DCTCP protocol.

Energy Minimization for Wireless Communication With Rotary-Wing UAV

This paper studies unmanned aerial vehicle (UAV) enabled wireless communication, where a rotarywing UAV is dispatched to send/collect data to/from multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive an analytical propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN when hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem (TSP) and convex optimization techniques. Next, we consider the general case where the UAV communicates also when flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a locally optimal solution by applying the successive convex approximation (SCA) technique. Numerical results show the significant performance gains of the proposed designs over benchmark schemes, in achieving energy-efficient communication with rotary-wing UAVs.

Area–Delay–Energy Efficient VLSI Architecture for Scalable In-Place Computation of FFT on Real Data

Efficient computation of real-valued fast Fourier transform (RFFT) has received significant attention in recent years due to its several applications in conventional digital signal processing and other emerging areas. In-place RFFT architectures are gaining popularity due to their lower hardware complexity compared with pipeline architectures. But the scaling of in-place RFFT architecture for higher lengths and higher throughput is a challenging issue due to increasing memory access conflict and higher memory bandwidth requirement. In this paper, a design approach is presented to develop an area-delay and energy-efficient architecture for in-place RFFT. Generally, an in-place fast Fourier transform (FFT) structure consists of a butterfly block which performs a set of butterfly operations in every clock cycle. From complexity analysis we find that in-place FFT structures with larger butterfly blocks are more efficient in terms of area-time complexity and energy consumption. The resolution of memory access conflict is however more challenging for higher butterfly block sizes. Therefore, we have analyzed the data-flow and memory footprint of in-place RFFT architectures for different throughput requirements, and based on that, we have proposed here a strategy to partition the storage unit into several banks of smaller sizes (without increasing the overall memory size) to resolve the memory access conflicts by concurrent data-swapping between the banks. Synthesis result shows that the proposed structure with butterfly block of size 4 and 8 involves (~44% and ~57%) less area-delay product and (~54% and ~57%) less energy per sample than those of existing similar structure on average for different FFT lengths, respectively.