Throughput Performance Research Articles

Vector Coding Optical Wireless Links

The quasi-static nature of the optical wireless channel means that the channel state information (CSI) can be readily available at the transmitter and receiver prior to data transmission. This implies that electrically band-limited optical wireless communication (OWC) systems can make use of optimal channel partitioning or vector coding based multi-channel modulation (MCM) to achieve high throughput by mitigating the non-linearities arising from the optical and electrical channel. This paper proposes a pulse amplitude modulation (PAM) based DC-biased optical vector coding (DCO-VC) MCM scheme for OWC. The throughput performance of DCO-VC is evaluated and compared to the well known DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) over hybrid (line-of-sight and diffuse) and diffuse (non line-of-sight only) visible light communication (VLC) channels with additive white Gaussian noise. For the completeness of the VLC physical layer, the performance comparison is based on an uncoded and a forward error correction transmission mode using well-known convolutional codes with Viterbi decoder. The results show that the coded DCO-VC outperforms DCO-OFDM system by achieving up to 2 and 3 dB signal to noise ratio gains over hybrid and diffuse VLC channels, respectively.

DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.

Securing Financial Transactions with a Robust Algorithm: Preventing Double-Spending Attacks

A zero-confirmation transaction is a transaction that has not yet been confirmed on the blockchain and is not yet part of the blockchain. The network propagates zero-confirmation transactions quickly, but they are not secured against double-spending attacks. In this study, the proposed method is used to secure zero-confirmation transactions by using the security hashing algorithm 512 in elliptic curve cryptography (ECDSA) instead of the security hashing algorithm 256. This is to generate a cryptographic identity to secure the transactions in zero-confirmation transactions instead of security hashing algorithm 256. The results show that SHA-512 is greater than SHA-256 in throughput. Additionally, SHA-512 offers better throughput performance than SHA-256 while also having a larger hash size. Results also show that SHA-512 is more secure than SHA-256.

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs

In this work, we enhance the total network throughput performance of a FANET system consisting of multiple UAVs and a ground control station (GCS) by optimizing the multihop routing structure and total power budget required for communication in the presence of inter-UAV interference. To achieve this, an optimization problem is formulated to maximize the network throughput within a given power budget. However, due to the non-convex and integer nature of the problem, it is NP-hard. Hence, it is dealt with differently by solving two daughter problems iteratively, i.e., (1) power allocation (PA) and (2) optimal link selection (LS) for the given solution of PA. These subproblems are solved iteratively by utilizing alternating optimization to obtain the global optimal solution of the main problem. The study presents various numerical results, which validate the effectiveness of the proposed routing structure and provide insights into the impact of interference on system parameters.

Improvement of Hierarchical Byzantine Fault Tolerance Algorithm in RAFT Consensus Algorithm Election

Currently, blockchain technology is not only gaining momentum in the financial industry but also showing promising developments in other sectors. Due to its suitability for various societal needs, an increasing number of non-financial institutions are expanding the scope of blockchain services beyond finance to areas such as healthcare service management and enterprise governance. The core of a blockchain system lies in consensus, which plays a crucial role in its performance and security. The PBFT algorithm is a widely used fault-tolerant consensus algorithm. However, it suffers from issues like high consensus latency, low throughput, and poor performance. In recent years, the HBFT algorithm has addressed these concerns. This paper proposes an improved HBFT blockchain consensus algorithm tailored for large enterprises and non-financial institutions with multiple independent functions. The goal is to achieve more efficient data management and election strategies that are both more secure than PBFT and more efficient than HBFT. Leveraging the enhanced HBFT consensus algorithm ensures stability and efficiency in the consensus and verification processes between organizations. Additionally, it enables the efficient management of data and value creation in the application context.

Novel MAC protocol for handling correlation and dynamics of medical traffic in WBANs

In Wireless Body Area Networks (WBANs), it is crucial to ensure reliable and timely delivery of gathered data, despite their intrinsic challenges related to limited resources at the sensor level. In fact, in the medical context, most real-time and life-critical applications are both delay-sensitive and loss-sensitive; the loss or corruption packets carrying critical data due to unreliable wireless networks could have severe consequences. In this regard, the concept of context awareness can be exploited; the more an application acquires additional information about users, sensors, and their context, the better it can adapt its functional behavior to improve its performance of delay, throughput and packet loss.Thus, acquiring some knowledge about the patient’s medical state over time may enhance the interpretation and intervention of the medical staff. In this context, we propose a dynamic context-aware MAC protocol (DMTM-MAC) that takes into account the dynamic and correlation aspects of medical traffic in order to adjust the channel access mechanisms and the general network behavior. We assign to each sensor a context that reflects the type of traffic generated and its QoS constraints. We aim to track specific biosensors, which are related to vital indicators relevant to a specific disease; the associated measurements may reflect the variation of the patient’s state over time. To such nodes, we assign a new node context, called dynamic context, which can help to predict the eventual health decline over time. The body node coordinator will have the ability to analyze the received data in order to detect abnormalities and disease evolution. Observed changes in collected measurements can be an early predictor of an advanced or complicated situation and can allow this BAN coordinator to handle dynamic context changes in these types of sensors by increasing their priority and transmission frequency to cope with the current situation. Performance evaluation showed that DMTM-MAC outperforms both MTM-MAC and IEEE 802.15.6 in terms of delay and packet delivery ratio. The proposed approach enhances the delay up to 80 % over IEEE 802.15.6.

Nearby connections strategies: Features, usage, and empirical performance evaluation

The Device-to-Device (D2D) communications involve a direct, peer-to-peer link between devices that operates independently of fixed network infrastructures. It can either complement existing network infrastructures or be used as a standalone network to provide services, such as distributing content, supporting emergency services during natural disasters, and enabling smartphone applications like AirDrop (iOS) and Nearby Share (Android). However, the behavior of D2D communication is not always predictable, and there are many challenges to implementing and deploying D2D communication systems in real-world situations. In particular, it is hard to know what throughput one can obtain, as nominal numbers provided by the documentation are seldom observed in practice. This paper focuses on Nearby Connections Application Programming Interface (API) provided by Google. This API offers distinct strategies in function of the network’s topology, and choosing the right one for an application is a challenging task as they perform differently. In this paper, we contribute to the research community in two ways. Firstly, we investigate the real-time performance of throughput of two strategies (STAR and POINT-TO-POINT, as they are the only ones to use Bluetooth and Wi-Fi Direct to transfer data). Our study shows that the POINT-TO-POINT strategy generally achieves high throughput performance and is suitable for use cases involving high network traffic. In contrast, the STAR performs poorly in throughput, which can result in link instability and slow transfers in some cases. Secondly, we disclose a tool to help network designers evaluate their networks and fine-tune their protocols and algorithms according to their specificities.

A Task-Oriented Hybrid Routing Approach based on Deep Deterministic Policy Gradient

With the development of communication and transmission technologies, the applications of Internet of Things (IoT) and telemedicine are increasingly sensitive to network latency. To meet this urgent demand, learning-based routing approaches emerge, with better performance and higher flexibility. These routing approaches can be divided into single-path and multipath, which have lower transmission delay and better load balancing respectively. However, the traffic scheduling policy is not only affected by the network states but also needs to consider the requirements for traffic tasks. We propose a Task-Oriented Hybrid Routing Approach (TOHRA) based on Deep Deterministic Policy Gradient (DDPG) in Software-Defined Networking (SDN). First, the hybrid routing optimization algorithm comprehensively considers the network states and task requirements, and outputs a task-oriented hybrid routing that combines the advantages of single-path and multipath. Moreover, we design a hop-by-hop traffic segmentation model based on DDPG to output the traffic segmentation ratio on hybrid routing to adapt to the changing transmission path. Experimental results show that TOHRA achieves the best performance of load balancing and network throughput. Especially in the case of Type C tasks in Germany topology, the average network throughput of THORA is increased by 32.86%, and the average variance of link load rate is reduced by 46.62%.

Throughput performance optimization of NOMA-assisted cooperative relay system with realistic impairments

Abstract This work aims to investigate the outage and throughput performance of non-orthogonal multiple access assisted cooperative relay system (CRS-NOMA) considering the realistic impairments caused due to in-phase and quadrature-phase imbalance (IQI), channel estimation errors (CEE), and successive interference cancellation (SIC) errors. More specifically, we investigate a model in which two-phase downlink transmission is carried out in two different modes: (i) CRS-NOMA without direct links and (ii) CRS-NOMA with direct links (CRS-DLNOMA). In CRS-NOMA mode, the source broadcasts a composite NOMA signal to destination users with the assistance of a decode-and-forward (DF) relay. In contrast, in CRS-DLNOMA, direct and cooperative links are available for transmission. We derive the analytical expressions of outage probability and throughput for both the NOMA destinations to evaluate the system performance of both CRS-NOMA and CRS-DLNOMA modes of transmission. Furthermore, numerical simulations also study and validate the influence of IQI, CEE, and SIC errors on the outage and throughput performance. The simulation results verify that realistic impairments degrade the system performance, but the presence of direct link has a positive impact on outage and throughput. Additionally, we use the golden search method to optimize the power allocation factor (PAF) and transmission rate to maximize the throughput at the near user while ensuring the throughput constraint at the far user.

Software Defined Network-Based Multi Service Routing Algorithm for Spatial Information Network

In order to solve the problems of unbalanced utilization of link resources due to the different capabilities of nodes in the spatial information network and the difficulty of satisfying the quality of service(QoS) with the change of task requirements, this paper proposes a multi-service routing algorithm for a spatial information network based on Software Defined Network(SDN), which divides the network transmission services into three categories: delay sensitive, bandwidth sensitive and packet loss rate sensitive. The link state information between satellite nodes is collected by an SDN controller, and then the topology-based bidirectional search algorithm is used to establish a “Source-Destination” set of routes between satellite nodes, the algorithm counts the delay, bandwidth, and packet loss rate of each route in the set, and selects routes that meet the needs of different business classes for data forwarding. The results show that the routing cost of this algorithm is lower than other algorithms, and it has better throughput performance.

Distributed Batteryless Access Control for Data and Energy Integrated Networks: Modeling and Performance Analysis

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices. Battery-powered and batteryless wireless devices with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a three-dimensional (3D) Markov chain, we model the operating behaviors of batteryless wireless devices and an HAP in a DEIN. Apart from backoff operations of devices, the 3D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. Wireless devices consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided.

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.

Impulsive noise mitigation based adaptive filtering and Reed Solomon coding for power line communication

In the realm of power line communication (PLC), impulsive noise is often regarded as the most difficult challenge to face in PLC. The impulsive noise effects are reduced by employing a combined adaptive least mean square (LMS) filtering and Reed Solomon (RS) coding. As a result, the bit error rate (BER) and throughput performance over this channel are enhanced. The results of a MATLAB computer simulation indicated that a significant improvement is achieved by utilizing the LMS adaptive filter in conjunction with the RS code. This is in contrast to a system that utilizes RS code alone or a system that utilizes the conventional time domain clipping approach.

A novel handover scheme for millimeter wave network: An approach of integrating reinforcement learning and optimization

The millimeter-Wave (mmWave) communication with the advantages of abundant bandwidth and immunity to interference has been deemed a promising technology to greatly improve network capacity. However, due to such characteristics of mmWave, as short transmission distance, high sensitivity to the blockage, and large propagation path loss, handover issues (including trigger condition and target beam selection) become much complicated. In this paper, we design a novel handover scheme to optimize the overall system throughput as well as the total system delay while guaranteeing the Quality of Service (QoS) of each User Equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the Reinforcement Learning (RL) algorithm and optimization theory. The RL algorithm known as Multi-Agent Proximal Policy Optimization (MAPPO) plays a role in determining handover trigger conditions. Further, we propose an optimization problem in conjunction with MAPPO to select the target base station. The aim is to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. The numerical results show the overall system throughput and delay with our method are slightly worse than that with the exhaustive search method but much better than that using another typical RL algorithm Deep Deterministic Policy Gradient (DDPG).

Polar-Coded Cross-Packet HARQ Based On Polarizing Matrix Extension

In this letter, polar-coded cross-packet hybrid automatic repeat request (XP-HARQ) using multilevel coded modulation (MLCM) to improve throughput performance is investigated. Firstly, a novel polar-coded XP-HARQ framework combining polar codes, MLCM, and level-independent XP-HARQ by using polarizing matrix extension is proposed. Then, a two-step throughput-based code design method is developed to generate optimal code rates and corresponding bit protect pairs and new information bit sets in extended codes. Simulation results demonstrate that the proposed scheme outperforms level-independent Turbo-coded XP-HARQ and state-of-art hybrid automatic repeat request (HARQ) using polar-coded modulation.

An Optimal Reflective Elements Grouping Model for RIS-Assisted IoT Networks Using Q-Learning

One of the critical challenges of the next-generation Internet of Things (IoT) network is to ensure reliable and low latency data transfer at the IoT devices (IoD), while minimizing the overall power consumption. Data latency and throughput performance degrade in a dense IoT network due to obstacles between IoD and the access point (AP). Therefore, an efficient signal processing model needs to be developed to address the aforementioned challenges. Towards this end, in this work, an optimal reflective elements (RE) grouping model over reconfigurable intelligent surface (RIS)-assisted IoT network is proposed. The proposed RE grouping model enhances the signal strength at the IoD when the direct link between AP and IoD is blocked. Additionally, in case of reliable direct link between AP and IoD, reflected beam power is controlled over RIS-IoD link, resulting in minimized power consumption. Hence, in this work, a novel method to regularize the power consumption over the IoT network is proposed. The proposed method groups the RE to reflect the incident signal. This grouping is based on the joint common group phase-shift and channel correlation between RE. Moreover, an optimization problem is formulated to minimize the total latency with respect to the received power and RIS reflection matrix. Due to the coupled nature of constraints, the optimization problem is non-convex and is solved using the Q-learning. Finally, the performance of the proposed method is compared with the benchmark methods, taking various network parameters into account.

A Genetic Algorithm-Enhanced Sensor Marks Selection Algorithm for Wavefront Aberration Modeling in Extreme-UV (EUV) Photolithography

In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way, a near-optimal image is exposed on the wafer surface. Optimizing the parameters of these models, however, involves several time costly sensor measurements which reduce the throughput performance of the machine in terms of exposed wafers per hour. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore, one of the most common optimal experimental design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a sensor mark selection algorithm which exploits genetic algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher-based criteria of G-, D-, and A-optimality, is selected and added to the measurement scheme. This process, however, is considered “greedy”, and for this reason, genetic algorithms (GA) are exploited to further improve the solution. By repeating in parallel the “greedy” part several times, we obtain an initial population that will be the input to our GA. This meta-heuristic approach outperforms the “greedy” approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry as well as academical results.

Analysis of throughput and error rate of 16-QAM, 64-QAM, and 256-QAM O-NOMA waveforms

Abstract This study presents a comprehensive analysis of the throughput performance, spectrum efficiency, and block error rate (BLER) of optical non-orthogonal multiple access (O-NOMA) waveforms using 16-quadrature amplitude modulation (QAM), 64-QAM, and 256-QAM modulation schemes. The aim is to assess the trade-offs between data rate, spectral efficiency, and error performance in O-NOMA systems. The analysis reveals that higher-order modulations, such as 64-QAM and 256-QAM, offer higher data rates and improved spectrum efficiency compared to 16-QAM. Furthermore, the study investigates the spectrum performance of the O-NOMA waveforms. The results indicate that higher-order modulations may utilise the spectrum more efficiently, maximising the data throughput within the available bandwidth. Moreover, the BLER analysis provides insights into the error performance of the O-NOMA waveforms. It quantifies the probability of errors occurring in a block of transmitted data and evaluates the system’s reliability. The analysis reveals that 256-QAM O-NOMA achieves lower BLER and high throughput in uplink and downlink as compared with the 16 and 64-QAM O-NOMA frameworks.

Outage Performance of Asymmetrical Cognitive Simultaneous Wireless Information and Power Transfer Networks Based on Non-Orthogonal Multiple Access with an Incremental Cooperation Relay and Hardware Impairments

This work aims to investigate an asymmetrical NOMA transmission network based on cognitive radio, where both the source and relay nodes of the secondary user are energy limited, and the energy could be harvested from the transmission signal of the power beacon. Considering some particular hardware impairments and asymmetry in the NOMA transmission, the closed-form expressions of outage probability for relay and destination nodes were derived using incremental relay methods. Furthermore, built on the expressions of the outage probability, the throughput performance was analyzed. Finally, the correctness of our theoretical analysis was verified using simulations.