System Throughput Research Articles

AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems.

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability.

A Comprehensive Analytical Framework under Practical Constraints for a Cooperative NOMA System Empowered by SWIPT IoT

Over the past decade, there has been notable attention directed towards spectrum and energy efficiency in conjunction with simultaneous wireless information and power transfer (SWIPT), aimed at extending the operational lifespan of energy-constrained wireless devices within cooperative non-orthogonal multiple access (C-NOMA) systems. This article delves into a system model comprising a transmitter, a relay, and two users, where the time-switching receiver (TSR) protocol is employed for energy harvesting at the relay. The objective of this study is to evaluate the performance of the system in terms of outage probability (OP), and system throughput (ST), and to determine the optimal time-switching (TS) factor. Our analysis encompasses the evaluation of OP, ST, and the determination of the optimal TS factor. Numerical simulation results reveal that the Nakagami-m fading channel exhibits the lowest outage probability, and that system throughput escalates with the signal-to-noise ratio (SNR) augmentation. These findings underscore the potential of the Nakagami-m fading channel to enhance energy efficiency in C-NOMA systems and suggest promising directions for future research.

Bonifacio strait natural reserve (BSNR): Investigating ecosystem functioning through comparative modelling of marine protected areas

The Bonifacio Straits Natural Reserve (BSNR) is a Mediterranean Marine Protected Area (MPA) located in Corsica, characterized by rocky coasts, steep bathymetry, and regulated exploitation of marine resources. In this study, we updated the BSNR Ecopath model from 2010, providing a re-evaluation of the MPA's impact 13 years after the reserve's establishment and incorporating improved catch estimates, particularly for recreational fisheries. The model encompassed four primary producer groups, fourteen invertebrate groups, two Chondrichthyes groups, sixteen teleost groups, one seabird group, and one cetacean group. The BSNR model depicted a Mediterranean ecosystem where piscivorous fishes and cephalopods link primary producers to top predators (Tursiops truncatus, Dentex dentex and Epinephelus marginatus), emphasizing complex trophic relationships. Although the ecosystem faced moderate fishing pressure, the model revealed uneven pressure among groups, with rays, for example, being particularly vulnerable to fishing nets. Additionally, a mixed trophic impact analysis revealed an absence of competition among fishing fleets for resources, suggesting that, overall, recreational and artisanal fisheries do not impact each other. By comparing MPA models across the Mediterranean Sea, our research suggests that MPA establishment might enhance the overall biomass of ecosystems within their boundaries. This abundance can spill over into adjacent areas, thereby increasing biomass and diversity in those regions. In more exploited MPAs, higher values for the Finn cycling index and the consumption to total system throughput ratio indicated that fishing activities increased pressure on prey resources by diminishing the system's energy, pushing the ecosystem to optimize recycling.

User Scheduling and Path Planning for Reconfigurable Intelligent Surface Assisted MISO UAV Communication

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Deep reinforcement learning-based dynamic multi-beam power allocation for GEO-LEO co-existing satellites

This paper first formulates a novel long-term beam power allocation (BPA) problem to tackle the harmful co-linear interference issue in the geostationary earth orbit (GEO) and low earth orbit (LEO) co-existing satellite system. This BPA problem intends to optimize the long-term weighted sum rate of the LEO system while ensuring that GEO user’s received interference from the LEO satellite system is lower than a pre-fixed threshold. To solve it in a real-time manner, a deep reinforcement learning (DRL) framework based on the proximal policy optimization (PPO) algorithm is proposed, named as drlBPA. In addition, for the existing most relevant baseline, the fractional optimization (FO)-based BPA scheme, on the one hand, this paper improves it via a greedy strategy to fully exploit time resource. On the other hand, to further reduce the computational complexity stemming from its iterative solving procedure, a deep neural network approximation scheme is also developed. Simulation results demonstrate that (i) The trained DRL model of the proposed drlBPA scheme has good convergence and generality performance. (ii) Compared with the three FO-based benchmarks, the drlBPA scheme not only achieves the highest throughput of the LEO system within a significantly reduced computation time, but also yields the best system stability.

Outage and throughput performance analysis of incremental relaying‐based underlay cognitive nonorthogonal multiple access networks with maximal ratio combining

SummaryIn this paper, we investigate the effectiveness of incremental relaying (IR)‐based underlay cognitive nonorthogonal multiple access (CNOMA) system with maximal ratio combining (MRC). Considering imperfect successive interference cancelation (i‐SIC), we derive analytical expressions for the outage probabilities experienced by the secondary users (SUs) and the system outage probability of the IR‐CNOMA system. We also derive the analytical expression for the outage probability of far SUs by considering IR‐CNOMA system with MRC. The maximum transmit power constraint of the secondary nodes and the interference threshold constraint of the primary receiver are considered in the analysis. We also evaluate the system throughput of IR‐CNOMA network. We also derive outage and throughput expression for the traditional cooperative relaying‐based CNOMA system (CR‐CNOMA) for the purpose of comparison. We derive analytical expression for the asymptotic outage probability and the asymptotic system outage probabilities of the SUs of IR‐CNOMA system. Further, we derive expressions for the optimal power allocation (OPA) factor at the secondary source that minimizes the asymptotic system outage probability of the IR‐CNOMA system. Through numerical and simulation investigations, we demonstrate that the system outage probability significantly lowers when the OPA factor is selected based on the criteria outlined in this work. It can be observed from the results that the IR‐CNOMA system outperforms the conventional CR‐CNOMA system in terms of outage probabilities and throughput. The outage probability is further reduced when IR‐CNOMA system with MRC is considered for the far SUs.

Dynamic Power Allocation for Downlink NOMA

Dynamic Power Allocation for Downlink Non-Orthogonal Multiple Access (NOMA) is a critical research area in wireless communication systems. This study focuses on implementing and evaluating dynamic power allocation strategies in the context of downlink NOMA. We compare the performance of dynamic power allocation with fixed power allocation schemes to assess their impact on system throughput, interference management, and overall efficiency. Through extensive simulations and analysis in this paper using MATLAB, we demonstrate a comparison between dynamic power allocation and fixed power allocation, and the advantages of dynamic power allocation in adapting to changing channel conditions and user requirements, leading to improved system performance. Our results show the data rate and outage probability which provides valuable insights into the benefits of dynamic power allocation in downlink NOMA systems and highlight the importance of adaptive power management techniques in enhancing wireless communication networks.

Distributed resource optimisation using the Q-learning algorithm, in device-to-device communication: A reinforcement learning paradigm

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

Measuring Fiber Positioning Accuracy and Throughput with Fiber Dithering for the Dark Energy Spectroscopic Instrument

Highly multiplexed, fiber-fed spectroscopy is enabling surveys of millions of stars and galaxies. The performance of these surveys depends on accurately positioning fibers in the focal plane to capture target light. We describe a technique to measure the positioning accuracy of fibers by dithering fibers slightly around their ideal locations. This approach also enables measurement of the total system throughput and point-spread function delivered to the focal plane. We then apply this technique to observations from the Dark Energy Survey Instrument (DESI), and demonstrate that DESI positions fibers to within 0.″08 of their targets (5% of a fiber diameter) and achieves a system throughput within about 7% of expectations.

Factors affecting deposit formation in foul condensate stripping systems

In kraft pulp mills, foul condensates are often steam-stripped to produce clean condensate for use as process water. The formation of organic deposits in the stripped condensate is a common problem. A systematic study was conducted to examine the deposit composition and the most likely operating parameters responsible for stripped condensate contamination experienced at a kraft mill in Brazil. Daily averaged data of 170 operating parameters over a 15-month period were analyzed by means of multivariate discriminant analysis and random forest classification analysis. The results showed that the deposit formation is related to high temperature, pressure, and dry solids operations in various evaporator effects. These conditions, combined with the poor demisting efficiency in these effects, may have increased black liquor carryover mist in the vapor. Deposit formation also appeared to be related to increased throughput of the foul condensate stripping system and increased pressure in the stripper. Results of Fourier transform infrared spectroscopy (FTIR) and pyrolysis-gas chromatography mass spectrometry (Py-GCMS) analyses show that the deposit consists of mostly organic matter that likely originated from wood extractives and lignin.

A selection procedure for the design of mixed-model assembly systems considering walking workers and fixed workers

Mixed-model assembly systems are widely used to guarantee higher flexibility, variety and throughput. In such assembly systems, fixed worker (FW) and walking worker (WW) workforce strategies are mainly adopted, influencing tactical and operational decisions with different and sometimes contrasting strengths and weaknesses. Thus, several aspects require a joint investigation to evaluate in which context a WW workforce strategy is preferable to the FW one. In this paper, the effect of assembly and station time variability on WW systems performance is investigated and compared to FW systems through a simulative study. Further, the number of workers and workstations and the effect of the travel time are also analysed. Results show that time variability has an impact on the throughput of both systems, in a different way. Starting from the target throughput and the given time variability among tasks, results derived from data elaboration allow us to propose a decision tree and a selection procedure for choosing between FW and WW systems, highlighting their most proper application conditions. According to the results, the time variability among tasks and the ratio between the number of workers and workstations are the two main drivers influencing the choice between WW and FW strategies.

Multi-Armed Bandit-Based User Network Node Selection.

In the scenario of an integrated space-air-ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node's network state conforms to a normal distribution, and by constructing the distribution's expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput.

An Approach to Enhancing Manufacturing Throughput Capability through System Design

<div>This article presents a new approach that integrates and balances cycle time and reliability considerations during system design to enhance manufacturing system capability, with direct applicability to automotive and high-volume manufacturing. The method addresses system throughput capability in three steps without complex mathematical modeling. The steps are: (1) managing workload cycle times, (2) considering real-world operational availability, and (3) achieving capability balance. Through analytical estimation and discrete event simulation of an example manufacturing system, this approach’s application shows a substantial increase in throughput capacity compared to a traditional workload-based design. This throughput-centric design approach, serving as a valuable tool for manufacturing system synthesis aimed at maximizing throughput, can apply to volume production systems to enhance throughput capacity through system design.</div>

TortoiseBFT: An asynchronous consensus algorithm for IoT system

Traditional partial synchronous Byzantine fault tolerant (BFT) protocols are confronted with new challenges when applied to large-scale networks like IoT systems, which bring about rigorous demand for the liveness and consensus efficiency of BFT protocols in asynchronous network environments. HoneyBadgerBFT is the first practical asynchronous BFT protocol, which employs a reliable broadcast protocol (RBC) to broadcast transactions and an asynchronous binary agreement protocol (ABA) to determine whether transactions should be committed. DumboBFT is a follow-up proposal that requires fewer instances of ABA and achieves higher throughput than HoneyBadgerBFT, but it does not optimize the communication overhead of HoneyBadgerBFT.In this paper, we propose TortoiseBFT, a high-performance asynchronous BFT protocol with three stages. We can significantly reduce communication overhead by determining the order of transactions first and requesting missing transactions after. Our two-phase transaction recovery mechanism enables nodes to recover missing transactions by seeking help from 2f+1 nodes. To improve the overall throughput of the system, we lower the verification overhead of threshold signatures in HoneyBadgerBFT, DumboBFT, and DispersedLedger from On3 to On2. We develop a node reputation model that selects producers with stable network conditions, which helps to reduce the number of random lotteries. Experimental results show that TortoiseBFT improves system throughput, reduces transaction delays, and minimizes communication overhead compared to HoneyBadgerBFT, DumboBFT, and DispersedLedger.

A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.

Support of resourses redistribution in NB-IоT LTE networks

The NB-IoT is expected to be used with the deployed LTE network as well as future 5G networks. The rapid development of the IoT concept has entailed the need to provide wireless communication to a huge number of devices included in the infrastructure. As part of the 5G NR standard for such devices, Massive Machine Communications (m MTC) technology is focused on optimizing the use of network resources to support a large number of stable connections per unit area. The Narrowband Internet of Things (NB-IoT) has been analyzed to enable the connectivity of a wide range of new IoT devices and services to the mobile network. The NB-IoT is also shown to be designed for fixed devices with low data transmission and low consumption, leading to an increase in the number of interconnected devices. In turn, standard NB-IoT modules attempting to simultaneously request radio channel resources for uplink data transmission may suffer from random access preamble collision. The proposed model describes the macro- and microcells of an NB-IoT LTE cluster. An increase in the efficiency of using the bandwidth of networks based on macro- and microcells with a high concentration of users of the NB-IoT LTE networks is shown. Numerical results of an analysis of identifying factors affecting system performance are presented. A linear increase in throughput has been revealed depending on the throughput of the macrocell when using the shared resource of the macrocell for a microcell of equal size without prior repacking of channels when servicing moving subscribers. In the absence of moving subscribers, the additional load is serviced, and the efficiency of using a macro cell increases almost 3 times. In addition, repacking channels significantly increases system throughput.

Comparison of floating and traditional artificial reef areas using an ecosystem-based approach in the Dashentang area of Bohai Bay, China

Instead of the conventional fishery survey-based approach, this study used an ecosystem-based approach to evaluate the ecological impacts of artificial reefs (ARs). Trophic models were initially employed for floating artificial reef area (FARA), traditional artificial reef area (TARA), and reef-free control area (CONA) in the Dashentang area of Bohai Bay, China. Ecosystem-based analyses of FARA and TARA highlighted similar trophic functioning characteristics. A substantial proportion of energy occurred at trophic levels I and II, and most of the primary production was utilized through grazing pathways. As a structuring group, zoobenthos played a pivotal role in enabling pelagic-benthic coupling in both ecosystems. Additionally, the top-down control in the FARA and TARA ecosystems was enhanced. Conventional surveys and comparative ecosystem-based analyses revealed distinct differences between the two ecosystems. In FARA, a significant increase was observed in pelagic fish and zoobenthos, with increases of 3.60- and 4.44-fold, respectively, compared with CONA. Conversely, TARA showed a considerable increase in demersal fish, approximately 2.6 times greater than that of CONA. Based on the biomass disparity between the two ARs, the ecosystem-based approach revealed that the FARA ecosystem led to enhanced energy pathways from detritus, greater total system throughput, higher energy cycle proportion, and more efficient nutrient transfer. Furthermore, the FARA ecosystem showed relatively high stability and maturity (total primary production/total respiration, 1.616<1.946) compared to the TARA ecosystem, suggesting a greater potential to withstand disturbances. Consequently, marine ecosystems respond differently to AR management initiatives. This study emphasizes the reliability of ecosystem-based evaluations during the initial phases of AR construction. Despite being a preliminary pilot study, this study offers insights into the impact on AR ecosystems, especially the role of floating ARs in coastal management.

High-Resolution Recognition of Orbital Angular Momentum Modes in Asymmetric Bessel Beams Assisted by Deep Learning

Abstract Fractional orbital angular momentum (OAM) vortex beams present a promising way to increase the data throughput in optical communication systems. Nevertheless, high-precision recognition of fractional OAM with different propagation distances remains a significant challenge. We develop a convolutional neural network (CNN) method to realize high-resolution recognition of OAM modalities, leveraging asymmetric Bessel beams imbued with fractional OAM. Experimental results prove that our method achieves a recognition accuracy exceeding 94.3% for OAM modes, with an interval of 0.05, and maintains a high recognition accuracy above 92% across varying propagation distances. The findings of our research will be poised to significantly contribute to the deployment of fractional OAM beams within the domain of optical communications.

Cluster-Based Strategy for Maximizing the Sum-Rate of a Distributed Reconfigurable Intelligent Surface (RIS)-Assisted Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) System.

This article proposes a distributed intelligent Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) collaborative transmission model with the assistance of reconfigurable intelligent surfaces (RISs) to address the issues of poor communication quality, low fairness, and high system power consumption for edge users in multi-cellular networks. By analyzing the interaction mechanisms and influencing factors among RIS signal enhancement, NOMA user scheduling, and multi-point collaborative transmission, the model establishes RIS-enhanced edge user grouping and coordinates NOMA user clusters based on this. In the multi-cell RIS-assisted JT-CoMP NOMA downlink transmission, joint optimization of the power allocation (PA), user clustering (UC), and RIS phase-shift matrix design (PS) poses a challenging Mixed-Integer Non-Linear Programming (MINLP) problem. The original problem is decomposed by optimizing the formulas into joint sub-problems of PA, UC, and PA and PS, and solved using an alternating optimization approach. Simulation results demonstrate that the proposed scheme effectively reduces the system's power consumption while significantly improving the system's throughput and rates.