Throughput Requirements Research Articles

RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points

Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a flying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconfigurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a fixed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs offers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.

Intelligent Reconfigurable Surface enhanced Ambient Backscatter Communication enabled Wireless Body Area Network

This paper considers an Intelligent Reconfigurable Surface (IRS)-enhanced Ambient Backscatter Communication (AmBC)-enabled Wireless Body Area Network (WBAN) system. We study the Base Station (BS) transmit power minimization problem, where the BS’s transmit beamforming vector and IRS’s reflecting coefficients are jointly optimized, subjected to the Signal-to-Noise Ratio (SNR) requirements of intra-WABN communication and throughput requirement of primary communication. To solve the non-convex problem, a near-optimal algorithm based on Semi-Definite Relaxation (SDR) and Successive Convex Approximation (SCA), and a low complexity algorithm based on IRS elements partition are proposed. Numerical results show that the two proposed algorithms considerably outperform the baseline schemes.

Quality of Service Management for Fast Data Transmission in Industrial Mobile Communications Using PSO-Based Computer Vision Technique

Mobile ad hoc networks (MANETs) have rapidly expanded in recent years, mostly as a result of the mobile devices’ obvious low cost, heterogeneity, and flexibility. When communication networks are down or inaccessible, sensors can quickly create a reliable network that can be used as a rescue data system. Industrial mobile communication has developed into a significant study area for both industry and academia in recent years. The need for data interchange between various smart devices with various latency flows is enormous. Nevertheless, there has not been as much research done in this area. The suggested work suggests a fuzzy-based improved PSO optimized in MANET to alleviate the drawbacks of the conventional routing approach. The suggested study offers a numerical modelling that can be used to carry out adaptive transmission optimization with a variety of programmable module structures and guarantee cost-effective route establishment with greater throughput, goodput, and lowest delay requirements. To find the best route, the proposed approach combines energy-optimized route construction with data-driven cluster head (CH) selection based on swarm intelligence. Particle swarm optimization- (PSO-) based clustering achieves improved delay, goodput, throughput, and path difference degree as compared to other conventional approaches, according to the extensive simulation results. The energy efficiency of a network that is decentralized is more important. The MANET device’s energy efficiency helps to extend battery life and improve network performance. This research demonstrates how the fuzzy-based improved PSO optimized in MANET helps to raise the network’s energy efficiency. As a result, network energy conservation improves network performance and battery life. Additionally, this method enhances the quality of service methodology. End-to-end delay, energy consumption, packet delivery ratio, and normalized routing overhead are measured and compared between the simulation and conventional routing protocols.

Optimum: Runtime optimization for multiple mixed model deployment deep learning inference

GPUs used in data centers to perform deep learning inference tasks are underutilized. Previous systems tended to deploy a single model on a GPU to ensure that inference tasks met throughput and latency requirements. The rapid increase in one GPU’s resources, as well as the emergence of scenarios such as small models and small batches, have exacerbated the issue of low GPU utilization. In this case, a mixed model deployment-based solution can significantly improve GPU utilization while also providing greater flexibility to the inference system’s upper layer. The selection of model combinations and optimization strategies in mixed model deployment, however, remain unresolved issues. This paper proposes Optimum, the first model-combination planning and runtime optimization framework for mixed model deployment. Facing enormous search spaces, Optimum uses performance prediction for model combination selection with low search overhead. The predictor is based on a multilayer perceptron. Its input features are the profiling results of the model engine, and the output is the performance degradation. The runtime optimization strategies allow Optimum to perform performance optimization and fine-grained tradeoff. The Optimum prototype is based on CUDA multi-stream and TensorRT. The test results show that we have a flat 10.3% performance improvement over mainstream single-model deployments. We have a performance improvement of up to 7.09% over the state-of-the-art with an order of magnitude reduction in search overhead.

Energy efficiency in ARQ-based multi-hop systems and the tradeoff with throughput

The minimum energy per bit (EPB) as the energy efficiency (EE) metric in an automatic retransmission request (ARQ) based multi-hop system is analyzed under power and throughput constraints. Two ARQ protocols including type-I (ARQ-I) and repetition redundancy (ARQ-RR) are considered and expressions for the optimal power allocation (PA) are obtained. Using the obtained optimal powers, the EE-throughput tradeoff (EETT) is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained. It is shown that how different throughput requirements, especially the high levels, affect the EE performance. Additionally, asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol. To evaluate the EE a distributed PA scenario, as a benchmark, is presented and the energy saving-gain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.

Forecasting Framework for Mobile Networks Based on Automatic Feature Selection

Traditionally, mobile network management has been based on reactive systems, where corrective actions are taken when a failure or a suboptimal network performance is detected. However, the requirements of low latency and high throughput associated with 5G new services, make these reactive systems insufficient to manage the new needs of users. Thus, the focus of mobile network management has changed to a proactive approach, where preventive actions are taken to avoid failures and suboptimal network performance. Moreover, in 5G, management algorithms are expected to make use of large amounts of information sources. The use of this large amount of information and the need to use historical data to generate forecasting models, may cause a loss of accuracy. To address these challenges, a forecasting framework is proposed in this work. The framework is based on the use of automatic feature selection techniques in both spatial and temporal dimensions as a pre-prediction stage. In this way, the proposed framework can improve the accuracy of other prediction schemes when using large amounts of data as input. The proposed scheme has been tested using different forecasting techniques. In addition, the impact of the proposed framework on predictions of different future lags has been analyzed.

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

This paper explores the use of multi-agent deep learning as well as learning to cooperate principles to meet strict service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network algorithms and evaluate their performances in different network, traffic, and routing scenarios, highlighting both the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across different scenarios, decreasing silver and bronze flow median waiting delays by more than 50 % and reducing the SLA violations of the latter by nearly 60 %, with respect to a classic priority queuing approach.

Adaptive Repetition Strategies in IEEE 802.11bd V2X Networks

Vehicle-to-everything (V2X) communications have very strict throughput and latency requirements, even in scenarios with high mobility. IEEE 802.11bd is being developed as a WiFi amendment to improve V2X performance, allowing up to three repetitions per packet, along with other features. Message repetitions increase time diversity and enable maximum ratio combining at the receiver, thus improving the probability of correct decoding. This paper investigates the IEEE 802.11bd packet repetition feature. First, we analyze how the increased channel load due to repetitions may in some cases result in a higher collision rate leading to lower network performance. Then, we propose two strategies for exploiting the IEEE 802.11bd repetition feature. The proposed strategies use the channel busy ratio to adapt the number of transmissions to the channel load and are validated through network-level simulations, which account for both the acquisition and decoding processes. Results show that the proposed strategies improve network performance under variable traffic conditions and enable fair access to the channel.

Data-Driven Security Measurements to Improve Safety in NYC and NJ Mass Transit

Public transit in America in recent years is potentially vulnerable to terrorist or mass casualty attacks. These vulnerabilities are in part due to the lack of strict screening and content policing, unlike security at airports, but also their attractiveness as a potentially high-value target. Although current public transit systems are designed to efficiently allow passengers to quickly travel, screening of individual riders for weapons remains limited due to current technology limitations and high peak throughput requirements. This paper aims to develop an understanding of the current state of security check systems as applicable to high-traffic subway stations. We also worked towards creating a proof-of-concept risk analysis model using crime and other types of publicly available data for the New York City and New Jersey transit regions.

Beyond wax printing: The future of paper analytical device fabrication

Many microfluidic “lab on paper” devices have been demonstrated for point-of-use applications, but scalable fabrication methods are necessary for these devices to make it past the proof-of-concept stage and become viable commercial products. Commercially available wax printers such as the Xerox ColorQube presented a near-ideal compromise between cost, ease of prototyping, and throughput, but in 2016, these printers were discontinued, and now, alternative methods are needed. In this review, we survey current paper analytical device (PAD) fabrication methods through the lens of scalability, focusing on the tradeoffs between resolution, ease of prototyping, cost, and throughput. Categories discussed include handmade, semi-automated, batch, laboratory-based, printer-based, and roll-to-roll fabrication methods. Hand-, batch-, semi-automated, and laboratory-based fabrication methods suffer from low throughput and high hands-on labor requirements. Roll-to-roll methods are high throughput but costly, while printer-based methods offer a compromise between cost and throughput. By highlighting the comparative merits of existing methods, we hope to offer insight into what makes a method “scalable” or “manufacturable.”

Evaluating Latency in Fifth-Generation Vehicle-to-Everything Communications using Adaptive Neuro-Fuzzy Inference System Model

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

Joint Optimization of Relay Selection and Transmission Scheduling for UAV-Aided mmWave Vehicular Networks

To deal with the explosive growth of mobile traffic, millimeter-wave (mmWave) communications with abundant bandwidth resources have been applied to vehicular networks. As mmWave signal is sensitive to blockage, we introduce the unmanned aerial vehicle (UAV)-aided two-way relaying system for vehicular connection enhancement and coverage expansion. How to improve transmission efficiency and to reduce latency time in such a dynamic scenario is a challenging problem. In this paper, we formulate the joint optimization problem of relay selection and transmission scheduling, aiming to reduce transmission time while meeting the throughput requirements. To solve this problem, two schemes are proposed. The first one is the random relay selection with concurrent scheduling (RCS), a low-complexity algorithm implemented in two steps. The second one is the joint relay selection with dynamic scheduling (JRDS), which fully avoids relay contentions and exploits potential concurrent ability, to obtain further performance enhancement over RCS. Through extensive simulations under different environments with various flow numbers and vehicle speeds, we demonstrate that both RCS and JRDS schemes outperform the existing schemes significantly in terms of transmission time and network throughput. We also analyze the impact of threshold selection on achievable performance.

Field Evaluation of Adaptive Path Selection for Platoon-Based V2N Communications

Vehicles on the road are expected to connect continuously to the Internet at sufficiently high speeds, e.g., several Mbps or higher, to support multimedia applications. However, even when passing through a well-facilitated city area, Internet access can be unreliable and even disconnected if the travel speed is high. We therefore propose a network path selection technique to meet network throughput requirements. The proposed technique is based on the attractor selection model and enables vehicles to switch the path from a route connecting directly to a cellular network to a relay type through neighboring vehicles for Internet access. We also develop a mechanism that prevents frequent path switching when the performance of all available paths does not meet the requirements. We conduct field evaluations by platooning two vehicles in a real-world driving environment and confirm that the proposed technique maintains the required throughput of up to 7 Mbps on average. We also evaluated our proposed technique by extensive computer simulations of up to 6 vehicles in a platoon. The results show that increasing platoon length yields a greater improvement in throughput, and the mechanism we developed decreases the rate of path switching by up to 25%.

Self-consistent integrated modeling of combined hybrid discharge-laser produced plasma devices for extreme ultraviolet metrology

Discharge- and laser-produced plasma (DLPP) devices are being used as light sources for extreme ultraviolet (EUV) generation. A key challenge for both, DPP and LPP, is achieving sufficient brightness to support the throughput requirements of nanometrology tools. To simulate the environment of a hybrid DLPP device and optimize the EUV output, we have developed an integrated HEIGHTS-DLPP computer simulation package. The package integrates simulation of two evolving plasmas (DPP and LPP) and includes modeling of a set of integrated self-consistent processes: external power source and plasma energy balance, plasma resistive magnetohydrodynamics, plasma heat conduction, detailed radiation transport (RT), and laser absorption and refraction. We simulated and optimized DLPP devices using Xe gas as a target material. We synchronized the external circuit parameters, chamber gas parameters, and laser beam temporal and spatial profiles to achieve maximum EUV output. The full 3D Monte Carlo scheme was integrated for detailed RT and EUV output calculations in Xe using more than 3600 spectral groups. The modeling results are in good agreement with Julich Forschungszentrum experimental data. Theoretical models, developed and integrated into the HEIGHTS package, showed wide capabilities and flexibility. The models and package can be used for optimization of the experimental parameters and settings, investigation of DLPP devices with complex design, analyzing the impact of integrated spatial effects and working timeline arrangement on the final EUV output, and EUV source size, shape, and angular distribution.

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A High-Throughput Full-Dataflow MobileNetv2 Accelerator on Edge FPGA

FPGA accelerators for lightweight neural networks such as MobileNetv2 are of great need in edge computing applications with high throughput requirements. Dataflow architecture has been considered a promising approach to optimize throughput since the intermediate feature map transfers can be significantly saved. However, previous MobileNetv2 accelerators only achieved a partial-dataflow architecture, and just one-third of the feature map transfers can be saved. To solve this issue, we propose a scheme to achieve a full-dataflow MobileNetv2 accelerator on FPGA. The scheme contains four techniques. First, we improve the full-integer quantization for easier deployment on hardware. Second, we propose tunable activation weight imbalance transfer for less quantization accuracy loss. Third, we present several highly optimized accelerator components whose parallelism can be flexibly adjusted, and implement residual connection with deeper FIFO so that the requirements of the full-dataflow architecture can be fully met. Finally, we present a computing resource allocation strategy to balance the latency of each layer, and a memory resource allocation strategy to effectively use the on-chip memory. Compared to the state-ofthe-art, experimental results show that the accelerator achieves 1910 FPS with 1.8× speedup when implemented on the Xilinx ZCU102 FPGA. In addition, it reaches 72.98% Top-1 accuracy with 8-bit integer quantization that outperforms all the other MobileNetv2 accelerators.

Joint Access Point Placement and Power-Channel-Resource-Unit Assignment for IEEE 802.11ax-Based Dense WiFi Network With QoS Requirements

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 x 50 m2), our algorithm gives the optimal solutions; when the area is larger (80 x 60 m2), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers.

In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if "no-data-lost" requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, we present a hardware/software platform built using commercial off-the-shelf elements, designed to acquire and store digitized signals captured from imaging spectrometers capable of supporting real-time data acquisition with stringent throughput requirements (sustained rates in the boundaries of 100 MBytes/s) and simultaneous information storage in a lossless fashion. The correct combination of commercial hardware components with a properly configured and optimized multithreaded software application has satisfied the requirements in determinism and capacity for processing and storing large amounts of information in real time, keeping the economic cost of the system low. This real-time data acquisition and storage system has been tested in different conditions and scenarios, being able to successfully capture 100,000 1 Mpx-sized images generated at a nominal speed of 23.5 MHz (input throughput of 94 Mbytes/s, 4 bytes acquired per pixel) and store the corresponding data (300 GBytes of data, 3 bytes stored per pixel) concurrently without any single byte of information lost or altered. The results indicate that, in terms of throughput and storage capacity, the proposed system delivers similar performance to data acquisition systems based on specialized hardware, but at a lower cost, and provides more flexibility and adaptation to changing requirements.

A parallel decision-making design for highly speedy packet classification

Packet classification is crucial in computer networks to increase network security because of developments in high-speed data communication. To support different network services including Quality of Service (QoS), security, and resource reservation, network packet classification is a crucial network kernel function. It became extremely challenging to classify arriving packets using the traditional packet classification algorithms at a decent pace due to the rapidly increasing size of rulesets and rule fields in current networks. In addition to hardware-based solutions, numerous contemporary software-based classification techniques have been put out to speed up packet classification. In general, it's critical to manage low latency, fast throughput, and higher energy efficiency with minimal memory needs while designing a packet classification method. In this paper, proposed the architecture makes use of the parallelism provided by current hardware technologies to classify numerous packets at once to improve throughput, reduce energy consumption, and significantly decrease the latency. The suggested high-speed parallel classification procedure uses two DPRAMs (DPRAM1 and DPRAM2) to support multiple read of the data. DPRAMs(Dual Port Random Access Memories) are preferred for stage memory because of their low latency performance, which enables quick rate packet lookup. The packet scheduling module receives the data from the DPRAM. The four successive packet splitter modules receive the packets in parallel. As a result, the packet classification throughput increased. To optimize throughput, and latency while maintaining low energy consumption, facilitating parallel packet classification architecture on FPGA is discussed in this work. Several parameters, including throughput, latency, energy efficiency, and memory requirements, will be evaluated and compared to traditional methods to assess performance. The classifier offers less latency of 51 ns and the lowest energy efficiency of 5.2 nJ, and its throughput may approach 802 million packets per second at the rate of 420 MHz clock frequency.