Throughput Demands Research Articles

High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to Now

We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of-the-art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics.

Forming Single-Cell-Derived Colon Cancer Organoid Arrays on a Microfluidic Chip for High Throughput Tumor Heterogeneity Analysis.

Single-cell-derived tumor organoids (STOs) possess a distinct genetic background, making them valuable tools for demonstrating tumor heterogeneity. In order to fulfill the high throughput demands of STO assays, we have developed a microfluidic chip containing 30 000 microwells, which is dedicated to a single cell culture approach for selective expansion and differential induction of cancer stem cells. The microwells are coated with a hydrophilic copolymer to eliminate cell adhesion, and the cell culture is supported by poly(ethylene glycol) (PEG) to establish a nonadhesive culture environment. By utilizing an input cell density of 7 × 103·mL-1, it is possible to construct a 4000 single cell culture system through stochastic cell occupation. We demonstrate that the addition of 15% PEG10000 in the cell culture medium effectively prevents cell loss while facilitating tumor stem cell expansion. As were demonstrated by HCT116, HT29, and SW480 colon cancer cells, the microfluidic approach achieved a STO formation rate of ∼20%, resulting in over 800 STOs generated from a single culture. Comprehensive analysis through histomorphology, immunohistochemistry, drug response evaluation, assessment of cell invasion, and biomarker detection reveals the heterogeneity among individual STOs. Specifically, the smaller STOs exhibited higher invasion and drug resistance capabilities compared with the larger ones. The developed microfluidic approach effectively facilitates STO formation and offers promising prospects for investigating tumor heterogeneity, as well as conducting personalized therapy-focused drug screening.

P4Rex: Accelerating regular expression matching with programmable switches

Regular expression matching is pivotal in numerous network applications. With the ever-increasing scale of data center traffic, deploying regular expression matching modules on traditional servers struggles to meet throughput demands. Emerging programmable switches have brought new prospects for high-speed pattern matching. However, deploying regular expression matching onto programmable switches presents the challenge of space explosion incurred by compiling regular expressions into a Deterministic Finite Automata (DFA). In this paper, we introduce P4Rex, a regular expression matching system designed for programmable switches. P4Rex synergistically leverages two following techniques: an efficient regular expression grouping algorithm that partitions regular expressions into different groups to reduce memory consumption, and DFA compression technology to achieve transition sharing. Experimental results demonstrate that P4Rex exhibits an average improvement of 17% and maximum improvement of 30% on memory consumption compared to prior regular expression grouping schemes and saves more than 10x memory consumption compared to deploying DFA directly on programmable switch.

Challenges and Solutions in High-Speed SerDes Data Path Design

Modern high-performance communication systems require high-speed Serializer/Deserializer (SerDes) data path design to meet the growing demand for data throughput and reliability due to technological advancements and data-intensive applications. This research article examines the complex problems of designing high-speed SerDes data routes and suggests novel solutions. Data fidelity across high-speed channels requires signal integrity, the first key difficulty. Attenuation, crosstalk, and EMI may degrade SerDes signal performance. Advanced methods like equalization, impedance matching, and effective shielding mitigate these impacts. Equalization, including feedforward and feedback methods, reduces channel losses and distortions, enhancing signal quality. Power consumption is another major issue as data rates and system complexity rise. Power-hungry high-speed SerDes circuits cause thermal management and energy efficiency difficulties. Adaptive power management, low-power design, and new materials with higher thermal performance and lower power dissipation are discussed in the study. Design complexity is another issue in high-speed SerDes data route design. High-precision timing and synchronization and many component integration in a small area need advanced design tools and methods. The study emphasizes simulation and modeling for complexity management and design for testability (DFT) for full verification and debugging.

A Novel CNFET SRAM-Based Compute-In-Memory for BNN Considering Chirality and Nanotubes

As AI models grow in complexity to enhance accuracy, supporting hardware encounters challenges such as heightened power consumption and diminished processing speed due to high throughput demands. Compute-in-memory (CIM) technology emerges as a promising solution. Furthermore, carbon nanotube field-effect transistors (CNFETs) show significant potential in bolstering CIM technology. Despite advancements in silicon semiconductor technology, CNFETs pose as formidable competitors, offering advantages in reliability, performance, and power efficiency. This is particularly pertinent given the ongoing challenges posed by the reduction in silicon feature size. We proposed an ultra-low-power architecture leveraging CNFETs for Binary Neural Networks (BNNs), featuring an advanced state-of-the-art 8T SRAM bit cell and CNFET model to optimize performance in intricate AI computations. Through meticulous optimization, we fine-tune the CNFET model by adjusting tube counts and chiral vectors, as well as optimizing transistor ratios for SRAM transistors and nanotube diameters. SPICE simulation in 32 nm CNFET technology facilitates the determination of optimal transistor ratios and chiral vectors across various nanotube diameters under a 0.9 V supply voltage. Comparative analysis with conventional FinFET-based CIM structures underscores the superior performance of our CNFET SRAM-based CIM design, boasting a 99% reduction in power consumption and a 91.2% decrease in delay compared to state-of-the-art designs.

Stability of regional traffic networks employing maximum throughput demand management

This paper considers the stability and optimality properties of traffic demand management schemes, motivated by the integration of smart monitoring and control technologies in traffic networks. First, a suitable optimization problem is formulated that aims to obtain demand input values that maximize the throughput within traffic networks adhering to regional traffic dynamics with triangular macroscopic fundamental diagrams. We show that optimal solutions to this problem may lead to unstable behaviour, revealing a trade-off between stability and optimality. To address this issue, we analytically study the stability properties of traffic networks at the presence of constant demand input and provide suitable local conditions that guarantee stability when the system’s equilibrium densities are strictly within the free-flow region, but not at the critical density. The latter case is significant, since the maximum throughput behaviour coincides in many cases with the local critical density. We resolve this by proposing a decentralized proportional demand control scheme and suitable local design conditions such that stability is guaranteed. Our analytic results are validated with numerical simulations in a 3-region system that demonstrate the effectiveness and practicality of the proposed approach.

A low-complexity crest factor reduction design for 5G NR sub-6 GHz digital front-end with restricted peak regrowth

In recent years, orthogonal frequency division multiplexing (OFDM) has gained widespread adoption in communication technologies, exemplified by long-term evolution (LTE), 5th generation new radio (5G NR sub-6 GHz), and others. These technologies are instrumental in meeting the increasing demands for high data rates, throughput, and reliable downlink network access. However, a notable challenge associated with OFDM is the high peak-to-average power ratio (PAPR), imposing limitations on the efficiency of power amplifiers that often operate with significant back-offs. To mitigate this issue, the widely employed crest factor reduction (CFR) technique aims to enhance efficiency. This paper introduces a novel CFR methodology utilizing mixed and multi-iterative signal distortion-based techniques. Our approach is versatile, demonstrating effectiveness for low-bandwidth signals like LTE, as well as large-bandwidth signals reaching up to 100 MHz, as seen in 5G NR FR1. Also, digital up-sampling used to achieve the required high sample rate causes the issue of peak regrowth even after the required PAPR reduction achieved by CFR processing, which defeats the purpose of CFR. Our proposed design offers a solution to this challenge, enabling operation at a low data rate before digital up-sampling while effectively mitigating peak regrowth. Importantly, the implementation significantly reduces FPGA resource utilization, alleviating the complexity associated with multiple-input multiple-output (MIMO) radio systems. Demonstrating compliance with 3rd generation partnership project (3GPP) specifications for 5G NR on a real-time testbed, our approach stands as a practical and efficient solution for contemporary communication systems.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Simulation and Analysis of the Code Domain NOMA with UFMC for 5G Wireless Networks

In fifth generation (5G) wireless networks, radio access techniques and multi-carrier waveforms play a vital role in meeting the diversified demands of ultra-low latency, massive connectivity, and higher throughput. Multi-access schemes used conventionally in 4G system was Orthogonal Multiple Access (OMA) technique. The OMA techniques suffer from inefficient spectrum utilization, high latency, and supports a limited number of users. Next-generation networks, Non-Orthogonal Multiple Access (NOMA), has a great potential, in which multiple users are simultaneously served using the same time, frequency, or code resource increasing the throughput. Code domain-NOMA (CD-NOMA) is the key technique implemented in the system design where multiple users are distinguished based on unique user-specific spreading codes. The NOMA system could significantly benefit from Universal Filtered Multi-Carrier (UFMC) modulation waveform in terms of flexibility, spectral efficiency, compatibility with Multiple Input Multiple Output (MIMO) technique, and relaxed synchronization requirements. The novel integrated system proposed in the paper is CD-NOMA-UFMC with convolutional codes. The major outcome of the paper is that the combination of UFMC air interface modulation technique with CD-NOMA access method can be the most effective way to meet the growing demands of 5G.

Reproductive toxicity of DDT in the Japanese medaka fish model: Revisiting the impacts of DDT+ on female reproductive health

The organochlorine pesticide dichlorodiphenyltrichloroethane (DDT) is an endocrine-disrupting compound (EDC) that has been banned by most countries for decades. However, it continues to be detected in nearly all humans and wildlife due to its biological and environmental persistence. The ovarian dysgenesis syndrome hypothesis speculates that exposure to EDCs during sensitive developmental windows such as early gonadal differentiation lead to reproductive disorders later in life. Yet, mechanisms by which DDT affects developing gonads remain unclear due to the inherent challenge of getting developmental exposure data from adults presenting with reproductive disease. The Japanese medaka (Oryzias latipes) is a valuable fish model for sex-specific toxicological studies due to its chromosomal sex determination, external embryonic development, short generation time, and extensively mapped genome. It is well documented that medaka exposed to DDT and its metabolites and byproducts (herein referred to as DDT+) at different developmental time points experience permanent alterations in gonadal morphology, reproductive success, and molecular and hormonal signaling. However, the overwhelming majority of studies focus primarily on functional and morphological outcomes in males and females and have rarely investigated long-term transcriptional or molecular effects. This review summarizes previous experimental findings and the state of our knowledge concerning toxic effects DDT + on reproductive development, fertility, and health in the valuable medaka model. It also identifies gaps in knowledge, emphasizing a need for more focus on molecular mechanisms of ovarian endocrine disruption using enhanced molecular tools that have become increasingly available over the past few decades. Furthermore, DDT forms a myriad of over 45 metabolites and transformation products in biota and the environment, very few of which have been evaluated for environmental abundance or health effects. This reinforces the demand for high throughput and economical in vivo models for predictive toxicology screening, and the Japanese medaka is uniquely positioned to meet this need.

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

Abstract 316: A novel, high-throughput full-length scRNA-seq workflow for improved biomarker discovery

Abstract Objective: Single-cell RNA-seq (scRNA-seq) analysis has been widely applied in oncology research for biomarker discovery. Although droplet-based methods are commonly used for such studies owing to their high throughput, they still miss important insights due to their lack of full-length transcript coverage. While full-length methods are available, to date, they have not been able to meet the throughput demands of many researchers. Moreover, both droplet and full-length scRNA-seq methods do not currently provide adequate readouts for non-coding genes, thereby limiting investigation of gene regulatory networks to protein coding genes. To close these gaps, we have developed a new high-throughput full-length scRNA-seq workflow that comprehensively profiles both protein-coding and non-coding genes in up to 60,000 cells within two days. Methods: Our new high-throughput workflow uses two rounds of combinatorial indexing, starting with a 96-well plate format for the first barcoding step followed by an automated second barcoding step in a 5,184-nanowell chip using an automated nanodispensing system. Initial testing demonstrated that our method could handle up to 60,000 cells without generating significant levels of doublets due to barcode collisions. To further illustrate the capacity of the new scRNA-seq approach, we profiled a total of approximately 11,000 isogenic A549 cells that either express WT TP53 or are TP53 null. In addition, both isogenic cell lines were treated with epigenetic therapy or mock treatment. Libraries were generated and sequenced using an Illumina® NextSeq®2000 sequencer. The sequencing data was then analyzed to define differential gene expression for both protein-coding and non-coding transcripts as a function of TP53 genotype and treatment condition, using Cogent™ NGS software. Results: Preliminary analysis showed that, on average, approximately 11,000 genes and 40,000 transcripts were detected per single cell at a read depth of 100,000 reads per cell. UMAP-based clustering confidently separated the cells according to their genotypes and treatment conditions using either protein-coding genes or non-coding genes. Furthermore, differential expression analysis identified both protein-coding and non-coding transcripts with significant expression differences, underscoring biological significance. Conclusion: Our new high-throughput full-length scRNA workflow enables preparation of high-quality full-length RNA-seq libraries for up to 60,000 cells with only two rounds of barcoding and shows high sensitivity and specificity in gene/transcript detection and quantification. The technology significantly improves the ability to identify new biomarkers by enabling comprehensive profiling of both protein-coding and non-coding full length transcripts. Citation Format: Peng Xu, Joseph Liu, Yana Ryan, Kazuo Tori, Xuan Li, Hima Anbunathan, Mike Covington, Tomoya Uchiyama, Mohammad Fallahi, Xuan Qu, Xiaoyun Xing, Ting Wang, Bryan Bell, Shuwen Chen, Yue Yun, Andrew Farmer. A novel, high-throughput full-length scRNA-seq workflow for improved biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 316.

Scale perception data compression method with blockchain technology for UAV communication

The demand for high data throughput and security in Unmanned Aerial Vehicle (UAV) communication is rising. Efficient data transmission remains challenging due to limited wireless resources. This paper introduces a Scale Perception Compression Method that leverages compression and blockchain technology to optimize UAV communication. The method involves normalizing data into matrices, generating compression and decompression matrix codebook pairs, and selecting suitable pairs for small or large-scale matrices. Simulation results show that the error rate is associated with the distributions of the data and compression matrix. It remains relatively low when the data and compression matrix subjected to Uniform distribution compared to Gaussian distribution, regardless of the values of the number of rows m and columns r in compression matrix. Bubble method is more suitable to find out the proper matrix pair to compress and decompress transmitted data for small-scale codebook because it is stable, fast and results in lower difference between original and recovered data matrices. The same applies to Merge method for the large-scale codebook. The ER is always lower based on a small-scale codebook searching by Bubble method than that of on a large-scale codebook searching by Merge method, which indicates the former is sufficient for compression.

Experimental analysis of efficacy of carrier aggregation

Abstract3GPP introduced carrier aggregation (CA) in Release‐10 to mitigate the increasing bandwidth demand of users. The efficacy of CA in meeting user demand for higher throughput in real‐life scenarios is an important consideration. This letter undertakes a simulation route using NS3 to understand the impact of variations in specific parameter values on the performance of CA in 3GPP‐specified scenarios. This work further establishes an empirical relation between throughput and some varying parameters. The work also shows that maintaining steady cell throughput in a densely populated cell comes at the cost of an individual user throughput. Simulation results exhibit that optimal CA design by tuning physical layer parameters alone cannot provide sustained throughput to the users.

Microfluidic methods for the diagnosis of acute respiratory tract infections.

Acute respiratory tract infections (ARTIs) are caused by sporadic or pandemic outbreaks of viral or bacterial pathogens, and continue to be a considerable socioeconomic burden for both developing and industrialized countries alike. Diagnostic methods and technologies serving as the cornerstone for disease management, epidemiological tracking, and public health interventions are evolving continuously to keep up with the demand for higher sensitivity, specificity and analytical throughput. Microfluidics is becoming a key technology in these developments as it allows for integrating, miniaturizing and automating bioanalytical assays at an unprecedented scale, reducing sample and reagent consumption and improving diagnostic performance in terms of sensitivity, throughput and response time. In this article, we describe relevant ARTIs-pneumonia, influenza, severe acute respiratory syndrome, and coronavirus disease 2019-along with their pathogenesis. We provide a summary of established methods for disease diagnosis, involving nucleic acid amplification techniques, antigen detection, serological testing as well as microbial culture. This is followed by a short introduction to microfluidics and how flow is governed at low volume and reduced scale using centrifugation, pneumatic pumping, electrowetting, capillary action, and propagation in porous media through wicking, for each of these principles impacts the design, functioning and performance of diagnostic tools in a particular way. We briefly cover commercial instruments that employ microfluidics for use in both laboratory and point-of-care settings. The main part of the article is dedicated to emerging methods deriving from the use of miniaturized, microfluidic systems for ARTI diagnosis. Finally, we share our thoughts on future perspectives and the challenges associated with validation, approval, and adaptation of microfluidic-based systems.

Performance Enhancement of LDPC Codes Based on Protograph Construction in 5G-NR Standard

To meet the high throughput demands, the 3rd Generation Partnership Project has specified the low-density parity check (LDPC) codes in the fifth generation-new radio 5G-NR standard with rate and length compatibility and scalability. This paper presents an extensive performance evaluation and enhancement of LPDC using the protograph-based construction defined in the 5G-NR standard. Firstly, the protograph-LDPC with layered offset min-sum (OMS) decoding, polar with successive cancellation list (SCL), and block turbo code are implemented and compared. Puncturing and shortening are applied to maintain block length at 1024 and code rate at 1/2 for all codes for comparison fairness. The results showed that P-LDPC outperforms its counterparts in terms of bit/ frame error rate (BER/ FER) behavior for given signal-to-noise ratios. Then, different P-LDPC settings were realized to study the effects of base graph selection (Graph1 or Graph2), code rate change (1/3 - 2/3), and block lengths increase (260 – 4160 bits). The simulation outcomes proved that BER performed better for lower coding rates or higher block lengths. Furthermore, P-LDPC behavior was examined over a Rayleigh flat-fading channel to achieve a 12.5 dB coding gain at 0.001 BER compared with uncoded transmission.

Design of Efficient routing protocol achieving high QoS and energy Efficiency based for Wireless Sensor Network

Wireless sensor networks (WSN), which until recently were primarily employed to convey modest amounts of data, have started to be used in recent years for multimedia applications. The routing protocol needs to be streamlined and improved in order to handle the demand for higher throughput and real-time characteristics of multimedia data. The smart, greedy forwarding algorithm based on throughput and energy-awareness (HBOA-Honey Badger optimization Algorithm) that we provide in this research offers minimal end-to-end packet delay and low packet loss. Instead of always choosing the shortest path to the sink, this method chooses high-throughput paths to transport multimedia packets over a WSN. A network void-bypass is used when it runs into network gaps to increase network reliability. Energy management is also used to improve the lifespan of sensor nodes by lowering the amount of radio coverage to a sufficient range that the sensor nodes can access. The simulation findings demonstrate that, compared to other algorithms, HBOA reduces end-to-end time by 8%, packet loss ratio by 18% and energy consumption by maintaining. . Load balancing occurs as a result of energy management, which makes the remaining energy of sensor nodes uniformly dispersed throughout the entire network. Through Simulation, the performance of HBOA is also compared to that of AGEM, TPGF, GPSR, and AODV.the esults suggests the HBOA performance better under conditions that are similat to these, such as low packet end-to-end- delay, low packet loss, and load balancing of routing channels.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

OTOP: Optimized Transmission Power Controlled OBSS PD-Based Spatial Reuse for High Throughput in IEEE 802.11be WLANs

As wireless local area network (WLAN) devices become more prevalent in various environments, the IEEE 802.11ax standard (Wi-Fi 6), released in 2021, aims to improve the spectral efficiency. The overlapping basic service set (OBSS) packet detection (PD) based spatial reuse (SR) scheme is a main technology for this purpose which can increase channel access opportunities by adjusting parameters. However, the standard only provides boundary values for parameter adjustment and does not provide details on how to optimize the performance, which can lead to a performance degradation. The IEEE 802.11be standard (Wi-Fi 7), established to satisfy extremely high throughput (EHT) demands of next-generation applications, includes the OBSS PD based SR scheme and the coordinated spatial reuse (CSR) scheme to maximize area throughput in dense networks. The CSR scheme optimizes the transmission performance through coordination between access points (APs), which results in additional signaling overhead and protocol complexity. In this paper, an enhanced SR scheme named optimized transmission power based OBSS PD (OTOP) SR is proposed to achieve high throughput in dense networks. The OTOP scheme enhances the parameter control operation of OBSS PD based SR to take advantage of its distributed nature which keeps the signaling overhead and protocol complexity low. The OTOP scheme derives the optimal transmission power that can maximize the transmission success probability of WLAN devices based on stochastic geometry analysis. The simulation results show that the proposed OTOP scheme performs better than the existing SRGM and OBSS PD based SR schemes in terms of throughput, frame error rate, and fairness.

A quantitative analysis of MIMO channel performance in new generation networks: Prospects and limitations

The objective of this article is to analyze the behavior of channels in mobile radio transmission chains and propose a solution for reducing propagation attenuation and ensuring Quality of Service (QoS) in telecommunications. As network coverage and throughput demands continue to grow, a thorough characterization of channels is essential. The primary contribution of this work is to investigate the different phases of the digital transmission chain jointly to achieve optimal reliability and QoS. Spatial and temporal representations must be considered at the same level when characterizing and modeling MIMO system channels. Accurate and realistic modeling can be achieved through transmission channel characterization in the spatial domain, which is critical in a MIMO context to improve throughput and performance compared to conventional systems. The article explores the behavior of information carriers in the propagation environment and the impact of fading, diversity, multiplexing, and multipath phenomena.