Optimal Throughput Research Articles

Energy Efficiency and Throughput Optimization in 5G Heterogeneous Networks

Device-to-device communication offers a promising technology for the 5G network that aims to enhance the data rate, reduce latency and cost, improve energy efficiency, and provide other desired features. The 5G heterogeneous network (5GHN) with a decoupled association strategy of downlink (DL) and uplink (UL) is a promising solution for the challenges faced in the 4G heterogeneous network (4GHN). The research presented in this paper evaluates the performance of the 4GHN as well as a DL-and-UL-coupled (DU-CP) access scheme in comparison with the 5GHN with a DL-and-UL-decoupled (DU-DCP) access scheme in terms of the energy efficiency and network throughput in four-tier heterogeneous networks. The energy and throughput are optimized for both scenarios, i.e., DU-CP and DU-DCP, and the results are compared. Detailed performance analyses of the DU-CP and DU-DCP access schemes were conducted with the help of comparisons of the results achieved by implementing a genetic algorithm (GA) and particle swarm optimization (PSO). Both of these algorithms are suited for the non-linear problem under investigation in which the search space is large. The simulation results have shown that the DU-DCP access scheme gives a better performance than the DU-CP scheme in a four-tier heterogeneous network in terms of network throughput and energy efficiency. The PSO achieves an energy efficiency of 12 Mbits/joule for the DU-CP and 42 Mbits/joule for the DU-DCP, whereas the GA yields an energy efficiency of 28 Mbits/joule for the DU-CP and 55 Mbits/joule for the DU-DCP. The performance of the proposed method is compared with those of three other schemes. The results show that the DU-DCP scheme using the GA outperforms the compared methods.

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.

Throughput optimization for IRS-assisted multi-user NOMA URLLC systems

Throughput optimization for IRS-assisted multi-user NOMA URLLC systems

An MCDM optimization based dynamic workflow scheduling used to handle priority tasks for fault tolerance in IIOT

Cloud computing's prominence can be mainly attributed to its convenient on-demand service delivery via the internet. The difficulty in managing these requests and allocating them optimally is exacerbated by the growing number of users making them and the variety of criteria used to apply cloud resources. Some of these difficulties include scheduling work while considering the user's Quality of Service (QoS) preferences, ensuring adequate load balancing, and adapting to the ever-changing conditions of the cloud environment. As server capabilities evolve, static load balancers, which distribute requests using fixed ratios of available resources, fail to provide optimum throughput. It has been shown that no other scheduling algorithms offer an adaptive method that considers both load balancing and maximizing the QoS criteria. The authors of this study present a novel adaptive strategy for dealing with this issue by fusing the best-worst multi-criteria decision-making method (BWM) with the compromise ranking method (VIKOR). Task priorities are determined by using the VIKOR method as a decision-maker. Numerical experiments are used to verify the validity of the suggested method, and the methodology is compared to other scheduling methods in terms of a variety of performance criteria. The simulation findings show that the presented method outperforms its rivals regarding throughput, makespan, waiting time, virtual machine (VM) utilization, and VM usage cost across the board of simulated experimental settings.

Continuous wave multi-pass imaging flow cytometry

Biological cells have weak optical properties that limit the sensitivity of measurements and imaging when the total illumination is limited by dynamics or throughput. Multi-pass microscopy enhances the sensitivity of classical light fields to these weak properties. We present a wide-field multi-pass microscope for label-free imaging flow cytometry. By passing the optical field through cells in a high-speed flow up to 4 times, we realize a ×4 enhancement in contrast and signal-to-noise ratio. We show that this technique approaches close to the quantum limit of measurement sensitivity, extending the range of optimal imaging to samples in the weakly absorbing regime. This allows for near optimal imaging sensitivity and throughput in practical scenarios involving dynamic biological samples and surpasses the sensitivity achieved with currently available quantum light sources.

Intelligent Access to Unlicensed Spectrum: A Mean Field Based Deep Reinforcement Learning Approach

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed spectrum is a promising approach that can relieve the pressure on cellular systems. Therefore, it is an urgent need to propose an unlicensed spectrum access method to guarantee the harmonious and efficient coexistence between cellular network technologies such as LTE and incumbent users such as WiFi in the unlicensed spectrum. However, existing coexistence schemes such as licensed assisted access (LAA) and LTE-unlicensed (LTE-U) still suffer from inefficient spectrum utilization and unsatisfactory fairness. In the paper, we formulate the optimization problem of the unlicensed spectrum access among multiple small bases (SBSs) as a game, and then solve the Nash Equilibrium (NE) with cooperative and distributed multi-agent deep reinforcement learning (MADRL). Specifically, a two level access framework for the coexistence scenario, which consists of feedback cycle and executive cycle, is first proposed, and then the key elements of MADRL including state, action, reward and Q-network are designed in detail based on the proposed access framework. To overcome the problems of learning divergence and prohibitive computation overhead in the coexistence scenario with multiple SBSs due to the non-stability phenomena, we adopt the mean field technology to solve the NE, which can simplify the process of solving NE by converting the interaction of an agent with the remaining multiple agents into an action with the average effect of them. Simulation results show that 1) the proposed algorithm can overcome the learning divergence problem and converge to the NE quickly, and 2) the proposed algorithm can achieve the bi-objective optimization of total throughput and fairness of the coexistence network, and can achieve better performance in terms of throughput and fairness compared with the baseline methods such as Cat-4 LBT, Cooperative LBT and Random schemes.

Spectrum sharing mechanisms in the unlicensed band: Performance limit and comparison

AbstractDeploying networks in unlicensed spectrum has been drawing significant attention, which serves to alleviate the increasing demands in licensed spectrum. However, the network coexistence in unlicensed channel may lead to throughput degradation and unfairness. An appropriate spectrum‐sharing mechanism is therefore of great significance. In this paper, we study the performance limit of two representative mechanisms used in the coexistence with WiFi, including Duty Cycle (DC) and Listen‐Before‐Talk (LBT). In particular, both the throughputs of the coexisting network and WiFi under two mechanisms are derived as explicit expressions of system parameters, based on which the maximum total throughput of the coexisting network and WiFi is characterized under throughput fairness and 3GPP fairness, respectively. A systematic comparison between the optimal throughput performance of DC and LBT is conducted. It is found that if the coexisting network with LBT occupies the channel for a large period each time it successfully accesses the channel, then the maximum total throughput in LBT would be close to that in DC under both throughput fairness and 3GPP fairness. The optimal settings for DC and LBT mechanisms to achieve maximum total throughput are obtained, respectively, which sheds important light on the design of fair and efficient spectrum‐sharing protocols.

A Privacy-Preserving Scheme for Medical Diagnosis Records Based on Encrypted Image Steganography

Healthcare diagnosis records are essential as they establish perpetual reports on the health of patients. These records are in nature meant to be kept confidential. It is for this reason that medical data should be secured before transmission. Steganography and cryptography are old concepts used to secure communications. Steganography is hiding data into a carrier, for instance, images, videos, texts, and files. It seeks to hide the existence of communications. Cryptography is communication in deciphering secret writings or ciphers. In our privacy-preserving scheme for healthcare records, a hybrid approach is used to ensure the maximum multi-level security of medical records before transmission. The proposed system utilizes AES-128 which is a symmetric key encryption algorithm to scramble the data. As compared to asymmetric key encryption, symmetric key encryption is faster as only one key is used for both encryption and decryption. To resolve the problem of key distribution that plagues symmetric cryptosystems, the proposed system makes use of the Diffie-Hellman key exchange algorithm for key agreement. In this way, the encryption essentials are securely exchanged before the actual transmission of the date. To ensure efficient and optimal steganography throughput, the Least significant bit (LSB) which involves the hiding of information in the most repetitive bits of each pixel of an image was implemented for steganography. The technique was chosen because the level of distortion made to the image is low hence the image quality change is minimal. As such, the resultant robust system, is highly impeccable, utilises efficient compression, and has improved capacity which are all desirable features for securing medical health records.

How Should We Regulate Cryptocurrencies via Consensus?: A Strategic Framework for Optimal Legal Transaction Throughput

Permissionless blockchain consensus protocols have been leveraged for defining decentralized economies for the (commercial or private) trade of virtual and physical assets, using cryptocurrencies. In most instances, the assets being traded are regulated , which mandates that the legal right to their trade and their trade value are determined by the governmental regulator of the jurisdiction in which the trade occurs. Unfortunately, existing blockchains do not formalize proposal of legal cryptocurrency transactions, as part of the execution of their respective consensus protocols, resulting in illegal activities in the associated crypto-economies. In this contribution, unlike existing non-consensus solutions, which are prone to be more compute-time and audit-time intensive, we present a novel regulatory framework for blockchain protocols, for ensuring legal transaction confirmation as part of the blockchain consensus. As per our regulatory framework, we derive, through a stochastic game analysis, block proposal strategies under which legal transaction throughput supersedes throughput of traditional transactions, which are, in the worst case, an indifferentiable mix of legal and illegal transactions. Finally, we show that when a majority of the consensus protocol participants are licensed by the regulator to propose legal transactions, there exists a fair consensus execution policy to maximize the legal transaction throughput in the blockchain network.

Improve Throughput and spectrum efficiency using Cell – Free MIMO-NOMA network with user–centric clustering

In this article, the user resource allocation and user scheduling in a cell-free user-centric clustering network and coherent transmissions is investigated. However, the setup of the user-centric clustering is not sufficient for multi-user transmissions, and user scheduling becomes difficult. To overcome the aforementioned problem and to provide user fairness, an optimum resource allocation algorithm with perfect user scheduling, beam forming, and radio frequency transmitter (RFT) clustering is proposed. Thereby, the cell edge user achieves the optimum throughput and spectrum efficiency. The simulation result shows that the proposed method outperforms the conventional method. In addition, the pilot-reusing factor is used to obtain loss due to imperfect channel state information and pilot train overhead.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE– a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

Throughput Optimization for Noma Energy Harvesting Cognitive Radio With Multi-UAV-Assisted Relaying Under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.

Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning

<p><span lang="EN-US">Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity.</span></p>

Precoded Universal MIMO Superposition Transmission for Achieving Optimal Coverage and High Throughput in 6G and Beyond Networks

A wireless network can utilize its entire resources to serve users without any allocation and provide upgraded performances such as enhanced throughput and high reliability without interference among users. However, previous wireless communication technologies could not bring the full utilization of network resources and overcome the challenge of forgoing scheduling among users. Therefore, to address these requirements, we propose and develop a novel design coined as Precoded Universal MIMO Superposition Transmission (PU-MIMO-ST) that can also be applicable for the most challenging and worst case interference scenario where the number of antenna points (APs) and the number of user equipment (UEs) is equal. In the considered system model, the APs are linked to a central processing unit (CPU) via backhaul and the UEs receive cooperation from the network resulting in achieving the optimal usage of network resources. The results obtained from computer simulations proof and verify the effectiveness of the proposed design called PU-MIMO-ST compared with other competitive works in terms of reliability, throughput, reduced complexity on the reception side, as well as power conservation.

Joint energy and throughput optimization for MEC-enabled multi-UAV IoRT networks

In this paper, we study an Unmanned Aerial Vehicle (UAV) enabled Mobile Edge Computing (MEC) service provisioning to the Internet of Remote Things (IoRT) devices spread randomly on the ground in a remote area. The data generated by the IoRT devices is collected by the UAVs, which immediately relay the data collected to an MEC device installed on the ground at a nearby location. The MEC device receives the data from the UAVs, and sends the results back to the UAVs, which in turn relay them to IoRT devices. We aim to minimize the energy consumption by the IoRT devices and the UAVs, while maximizing the system throughput subject to bandwidth, power, information-causality, and UAVs’ trajectory constraints. We formulate the problem as a Mixed Integer Non Linear Programming problem, which is a complex and non-convex optimization problem. To make the problem tractable, we use variable relaxation. We further develop an iterative algorithm based on Block Coordinate Descent method, to jointly optimize the connection scheduling, power control, bit transmission scheduling, bandwidth allocation, and trajectories of the UAVs. Numerical results demonstrate the convergence of the algorithm and superiority of the proposed model with respect to conventional methods. Our proposed system model of placing MEC at ground shows 9% improvement in energy consumption when compared to carrying out computations at MEC carried by UAV and a 99% improvement when compared to placing MEC at the satellite. The proposed system model shows a 0.2% lower system throughput on average, compared to placing MEC at UAV, which is tolerable considering gains in terms of energy consumption.

Machine Learning-Based GPR with LBFGS Kernel Parameters Selection for Optimal Throughput Mining in 5G Wireless Networks

Considering the ever-growing demand for an efficient method of deductive mining and extrapolative analysis of large-scale dimensional datasets, it is very critical to explore advanced machine learning models and algorithms that can reliably meet the demands of modern cellular networks, satisfying computational efficiency and high precision requirements. One non-parametric supervised machine learning model that finds useful applications in cellular networks is the Gaussian process regression (GPR). The GPR model holds a key controlling kernel function whose hyperparameters can be tuned to enhance its supervised predictive learning and adaptive modeling capabilities. In this paper, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) with kernel parameters selection (KPS) algorithm is employed to tune the GPR model kernel hyperparameters rather than using the standard Bayesian optimization (BOP), which is computationally expensive and does not guarantee substantive precision accuracy in the extrapolative analysis of a large-scale dimensional dataset. In particular, the hybrid GPR–LBFGS is exploited for adaptive optimal extrapolative learning and estimation of throughput data obtained from an operational 5G new radio network. The extrapolative learning accuracy of the proposed GPR–LBFGS with the KPS algorithm was analyzed and compared using standard performance metrics such as the mean absolute error, mean percentage error, root mean square error and correlation coefficient. Generally, results revealed that the GPR model combined with the LBFGS kernel hyperparameter selection is superior to the Bayesian hyperparameter selection method. Specifically, at a 25 m distance, the proposed GPR–LBFGS with the KPS method attained 0.16 MAE accuracy in throughput data prediction. In contrast, the other methods attained 46.06 and 53.68 MAE accuracies. Similarly, at 50 m, 75 m, 100 m, and 160 m measurement distances, the proposed method attained 0.24, 0.18, 0.25, and 0.11 MAE accuracies, respectively, in throughput data prediction, while the two standard methods attained 47.46, 49.93, 29.80, 53.92 and 47.61, 52.54, 53.43, 54.97, respectively. Overall, the GPR–LBFGS with the KPS method would find valuable applications in 5G and beyond 5 G wireless communication systems.

Throughput optimization with Wind Energy harvesting

Throughput optimization with Wind Energy harvesting

Analysis and Evaluation of Fully TCP-Compatible Backpressure-Driven Traffic Engineering

Throughput optimality is a core objective for the operation of modern networks. The backpressure routing and scheduling scheme is well-known for being analytically-optimal in terms of throughput maximization. Its operation is driven by packet queue build-ups at the network nodes, and constant status updates among neighbors in order to deduce the best next packet forwarding step. However, this operating principle is also incompatible to TCP, which uses congestion control to avoid queue build-ups. These factors have so far limited the use of backpressure at the edge only. The present work presents a novel combination of TCP and backpressure at the network layer, overcoming any compatibility issues and allowing for backpressure to be active at all network nodes without exceptions. The key idea is to apply the Lyapunov framework cumulatively, over a large set of router hardware clock ticks. This approach allows for the throughput-optimal backpressure decisions to be expressed via regular traffic engineering routing rules, instead of explicit per-packet forwarding directives. Simulation results validate the analytical findings, and demonstrate significant throughput gains over state-of-the-art related approaches. Proof of concept emulations showcase the simplicity in the implementation of the proposed scheme.