Throughput In Wireless Networks Research Articles

Improving Throughput and Sum Secrecy Rate in Wireless Networks Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

Power allocation scheme for sum rate and fairness trade-off in downlink NOMA networks

Non-orthogonal multiple access (NOMA) is an essential enabler technology that is expected to help satisfy the key requirements of increased system throughput in future wireless networks. However, another equally important aspect that should go hand-in-hand with system throughput is user fairness for any network. But, to the best of our knowledge, even though there have been works that look at system throughput or user fairness maximization for NOMA-based networks, they looked at these as a single objective optimization problem, where one is the objective and the other is one of the constraints. However, quite often, joint optimization of both system throughput and user fairness is required to make optimized decisions in the face of trade-offs between these two equally important but conflicting objectives. In this regard, this paper formulates a multi-objective optimization problem to jointly maximize the sum rate and user fairness in a downlink transmission NOMA system, through optimize power allocation (PA), under system-imposed constraints. A weighted sum approach is used to turn the multi-objective optimization problem into a single-objective optimization problem to make it analytically tractable. The optimized PA is then obtained using Lagrange dual decomposition method and Karush–Kuhn–Tucker (KKT) conditions. Using our derived expressions, we propose an iterative PA algorithm that converges fast enough to be employed in practical NOMA networks. We also present simulation results to highlight the effectiveness of the proposed solution. Further, the performance of the proposed method is compared with that of the benchmark methods.

Machine Learning for Wireless Network Throughput Prediction

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

Virtual Private Network—Firewall Integration for Wireless Local Area Network Improvement against Jammers

Wireless networks are susceptible to many security problems since the wireless medium is open. Jammer (jamming attack) is the most important problems in wireless networks. It is denial-of-service attack in which attackers send malicious signals or messages on a channel that is intended for normal traffic by intentionally interfering with the network. One of the solutions is the development of tunneling technology as an attempt to secure communication networks. Virtual Private Network (VPN) tunneling technology when integrated with firewall would provide a suitable security. In this study, number of Jammers would interfere with the normal operation of the network resulting in performance degradation of network. These Jammers decrease throughput and increase delay and data dropped. The aim of this study is to improve the performance of Wireless Local Area Network (WLAN) by integrating Firewall with VPN in number of different Riverbed Modeler simulation scenarios which it is utilized in this study for video and data applications. Firewall is a technology employed to regulate the level of network connectivity. It can block unauthorized access from an external network to an internal network’s resources. In this study, the tunneling technology of VPN was integrated with firewall to improve the performance by increasing the throughput and decreasing delay and data dropped. The firewall would block any access to the server from the workstations and the VPN would filter the packets to allow a specific access to the specific server. The results showed that VPN when integrated with firewall increased the throughput from 1,350,000 to 1,500,000 bits/sec and decreased the delay from 0.0054 sec to 0.0047 sec and data dropped from 7,800 to 5,000 bits/sec so that this study proved that VPN incorporated with firewall provides a good throughput and delay improvement for wireless local area network.

WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things

Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.

Enhancing QoS Performance for Cell Edge Users Through Adaptive Modulation and Coding in IEEE 802.11ac WLANs

Wireless communication networks, such as IEEE 802.11ac Wireless Local Area Networks (WLANs), often encounter challenges in providing consistent Quality of Service (QoS) to users situated at the cell edge. The inherent variations in channel conditions, particularly lower signal-to-noise ratios (SNRs) in these regions, lead to compromised data rates and reliability, resulting in significant degradation of throughput. This study presents an innovative solution in the form of an Adaptive Modulation and Coding Scheme (AMCS) algorithm tailored to enhance QoS performance for cell edge users. The primary objective of the AMCS algorithm is to optimize QoS by dynamically adjusting the transmission data rate based on the observed channel conditions, quantified using SNR as a channel state indicator. Conventional approaches might unilaterally select the lowest data rate in challenging conditions, prioritizing reliability at the expense of throughput. However, the proposed AMCS algorithm takes a distinct approach by intelligently determining the Modulation and Coding Scheme (MCS) that offers an optimal balance between throughput and reliability for the given SNR level. To achieve this, the algorithm utilizes real-time SNR measurements to select an MCS that ensures a stable connection while also maintaining an acceptable data rate. By adapting the MCS based on the current SNR, the algorithm aims to mitigate the adverse effects of poor channel conditions experienced by cell edge users. The innovation of the AMCS algorithm lies in its ability to make dynamic adjustments, allowing users to experience improved data rates without compromising connection stability. Through extensive simulations and evaluations, the proposed AMCS algorithm showcases its efficacy in enhancing QoS performance at the cell edge. The algorithm's adaptive approach successfully achieves higher data rates and improved reliability by selecting appropriate MCS configurations tailored to the observed SNR levels. This innovative technique provides a promising solution to the challenge of striking the right balance between throughput and reliability in wireless communication networks, ultimately leading to an improved user experience for those at the network's periphery.

PERFORMANCE ANALYSIS OF WIRELESS COMPUTER NETWORKS IN CONDITIONS OF HIGH INTERFERENCE INTENSITY

Context. The decrease in the probability of successful frame transmission in the infrastructure domain of IEEE 802.11 DCF wireless network is caused both by the influence of the collision intensity and by the impact of external interference in the radio path. Using the Markov chain approach as a baseline, we explicitly expressed the dependence of the network throughput on the number of operating stations, bit error rate (BER), and the frame fragmentation factor.
 Objective. The purpose of this article is to study the influence of interference intensity on the throughput of a wireless network domain in a wide range of the number of operating stations when transmitting frames of various lengths in the absence and with the use of the fragmentation mechanism.
 Method. The performed mathematical modelling showed, that in the range of increased and high noise intensity (BER = 10–5 – 10–4), a decrease in the length of the frame data field from the standard length of 12000 bits to 3000 bits is accompanied by a decrease in the throughput for all values of the number of competing stations. At the same time, it must be noted that as the amount of the frame data decreases, the throughput becomes less susceptible to an increase in the noise intensity. Qualitatively different results are obtained in the region of very high interference intensity (BER = 2·10–4). A significant increase in the probability of frame transmission in this region observed with a decrease in the standard length of the frame data field by 2–3 times, made it possible to increase the throughput compared to the original one. This effect is especially pronounced when the length is halved.
 Results. The study of the standard frame transmitting process, but with a fragmented data field, showed that if for BER = 5·10–5 and less with an increase in fragmentation factor, the throughput values decrease, in the entire range of the number of stations due to the predominant increase in overhead costs, then in the region of high (BER = 10–4) and very high noise intensity (BER = 2·10–4) we have the opposite effect. To the greatest extent, the throughput increases when the frame data is transmitted in two equal fragments. We have made a comparison of the network throughput determined by simply reducing the length of the frame data field and using fragmentation of a standard frame. The comparison showed that the use of the fragmentation mechanism is more beneficial both when throughput is stabilized under conditions of increased noise intensity and when the throughput is increased under conditions of high and very high noise intensity.
 Conclusions. In this article, a mathematical model has been modified for direct calculation of the wireless network throughput. Using this model, we studied the changes in throughput over a wide range of BER and a number of operation stations for various values of the transmitted frame fragmentation factor. The conditions for increasing the throughput are determined.

Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

Small cells are a promising technique to improve the capacity and throughput of future wireless networks. However, user association and power allocation in heterogeneous networks is complicated by the dense deployment of small cells, resulting in non-convex and combinatorial problems. Conventionally, machine learning techniques are applied to the joint optimization problem, which has different action spaces. Gauging the continuous spaces to discrete spaces results in the loss of granularity due to discretization (e.g. potential power values in power allocation). Due to its hybrid action space, it is sub-optimal to solve joint user association (discrete spaces) and power allocation (continuous spaces) problems by applying traditional machine learning approaches. This work proposes a Multi-Agent Parameterized Deep Reinforcement Learning (MA-PDRL) approach to address the joint user association and power allocation problem efficiently. According to simulation results, the proposed multi-agent PDRL performs better in energy efficiency and QoS satisfaction than WMMSE, game theory, Q-learning, and DRL techniques.

End to end delay distribution analysis and interference aware high throughput for Wireless Mesh Networks

The wireless mesh network is an innovative and efficient wireless communication approach with immense potential to transform future wireless networks. This cutting-edge wireless technology provides a wide range of wireless services for personal, local, campus, and metropolitan environments. The wireless mesh network consists of a cluster of mobile wireless nodes that can dynamically and instantaneously create random and temporary network topologies, forming a wireless ad-hoc network. Ad hoc wireless networks are particularly advantageous due to their cost-effectiveness and ease of deployment. In order to further optimize the performance of ad-hoc networks, different routing algorithms such as Ad-hoc On-Demand Distance Vector Routing (AODV), Dynamic Source Routing Algorithm (DSR) and Destination Sequenced Distance Vector (DSDV) have been thoroughly researched via simulation.

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

Unsupervised Deep-Learning for Distributed Clock Synchronization in Wireless Networks

One of the major factors which limits the throughput in wireless communications networks is the accuracy of time synchronization between the nodes in the network. Synchronization methods based on pulse-coupled oscillators (PCOs) have the advantage of simple implementation and achieve high accuracy when the nodes are closely located. However, such schemes tend to have poor synchronization performance for distant nodes, as well as in the presence of clock frequency offsets between the nodes. In this paper we present a novel PCO-based Deep neural network (DDN)-Aided Synchronization Algorithm coined DASA. We design DASA as a novel low-complexity and interpretable architecture by converting classic PCO-based synchronization into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a trainable discriminative model</i> . To enable DASA to operate in dynamic settings, we propose a novel, unsupervised, distributed, fast <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> training scheme which is able to train DASA <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within a few sampling instances, locally</i> , thereby avoiding the need for information exchange between the nodes or for a central node for coordination. DASDA is demonstrated to achieve an improvement by a factor greater than ten compared to the classic reference scheme. Lastly, we propose another novel, distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> training scheme for DASA, which is demonstrated to offer a tradeoff between performance and simplicity of deployment compared to the online training scheme, yet, at the same time, DASA with offline training still achieves superior performance compared to the classic reference scheme.

Joint Learning and Optimization-Based Resource Management in Hybrid Network of Cooperative and Non-Cooperative Massive MIMO Systems

Multi-cell multiple-input multiple-output (MIMO) systems have the potential to significantly improve wireless network throughput. However, the cooperative MIMO network experiences the limited backhaul capacity, while the non-cooperative MIMO network requires high fronthaul bandwidth. To overcome these issues, we consider the hybrid MIMO networks to take advantages of both non-cooperative and cooperative MIMO network. In such hybrid system, the decision of whether cells operate in non-cooperative or cooperative mode, as well as the allocated bandwidth for cells are challenging due to the unavailability of channel state information (CSI). Furthermore, cells operating in the cooperative mode should optimize the quantized level selection and power allocation when the backhaul capacity is limited. To address this problem, we propose a joint learning-based and optimization-based framework. Particularly, we employ both large-scale and small-scale fading models to formulate the optimization problems, and then use the obtained solutions of these problems to determine fixed-size features in dynamic networks for the learner. Our numerical results show when the placement decision and the bandwidth assignment for cells are determined, the proposed algorithm can compute the quantized level and allocate power to significantly improve the system performance. Moreover, the proposed hybrid network model controlled by our algorithms outperforms both the non-cooperative and the cooperative MIMO systems in term of net throughput.

A survey on semantic communications: technologies, solutions, applications and challenges

Semantic Communications (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarised. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarised. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarised. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.

VariBan: A Variable Bandwidth channel allocation algorithm for IEEE 802.15.4e-based networks

Among several link-layer protocols, Time-Slotted Channel Hopping (TSCH), defined in the IEEE 802.15.4e standard, offer a good performance improving reliability and throughput for wireless sensor network using in the best way two parameters: time and frequency. Many works in the current literature present scheduling schemes for the protocol. In this scenario, our work is among a small group that aims to explore the improvements of the protocol by exploiting different techniques over the transport layer of the network protocol. Channels with wider bandwidth can improve network performance by increasing the throughput and improving packet delivery. Therefore, this paper proposes a scheduling algorithm to dynamically allocate channels with variable bandwidth to improve the overall throughput and packet delivery. The scheduling and channel allocation happen using the conflict mapping method to identify links in congestion and schedule them. This work proposes a procedure for allocating channels with bandwidth proportional to the demand for packets per link. We implemented the algorithm in the Contiki-NG platform and evaluated the result through simulations. Results show improved time slot allocation, packet delivery ratio, and throughput. Our experiments show that VariBan achieves a throughput of over 99% and improves Packet Delivery Ratio by over 50% in all three scenarios compared to the state-of-the-art algorithm.

Improved Congestion Control in Wireless Sensor Networks Using Clustering with Metaheuristic Approach

The wireless sensor network (WSN) assists an extensive range of sensor nodes and enables several real-time uses. Congestion on the WSN is based on high pocket traffic and low wireless communication capabilities under network topology. Highly loaded nodes will consume power quickly and increase the risk of the network going offline or breaking. Additionally, loss of packet and buffer overflows would result in an outcome of increased end-to-end delay, performance deterioration of heavily loaded nodes, and transport communication loss. In this paper, a novel congestion control system is proposed to diminish the congestion on network and to enhance the throughput of the network. Initially, cluster head (CH) selection is achieved by exhausting K-means clustering algorithm. After the selection of cluster head, an efficient approach for congestion management is designed to select adaptive path by using Adaptive packet rate reduction (APTR) algorithm. Finally, Ant colony optimization (ACO) is utilized for enhancement of wireless sensor network throughput. The objective function increases the wireless sensor network throughput by decreasing the congestion on network. The proposed system is simulated with (Network Simulator NS-2). The proposed K-means C-ACO-ICC-WSN attains higher throughput 99.56%, 95.62% and 93.33%, lower delay 4.16%, 2.12% and 3.11% and minimum congestion level 1.19%, 2.33% and 5.16% and the proposed method is likened with the existing systems as Fuzzy-enabled congestion control through cross layer protocol exploiting OABC on WSN (FC-OABC-CC-WSN), Optimized fuzzy clustering at wireless sensor networks with improved squirrel search algorithm (FLC-ISSA-CC-WSN) and novel energy-aware clustering process through lion pride optimizer (LPO) and fuzzy logic on wireless sensor networks (EAC-LPO-CC-WSN), respectively. Finally, the simulation consequences demonstrate that proposed system may be capable of minimizing that congestion level and improving the throughput of the network.

An evolutionary routing protocol for load balancing and QoS enhancement in IoT enabled heterogeneous WSNs

Energy awareness is a key concern of recent advances in the Internet of Things (IoT) enabled wireless sensor networks (WSNs), and many optimization approaches to reduce energy consumption have been proposed. The most widely used routing technique for achieving energy efficiency in WSNs is the clustering hierarchy. Data transmission over long distances has a negative impact on network efficiency in terms of stability period, network lifetime, and QoS due to the selection of inadequate Cluster Heads (CHs). This paper describes a new Evolutionary Gateway-based Load-Balanced Routing (E-GLBR) algorithm for efficiently selecting appropriate CHs. The proposed algorithm is based on the genetic algorithm optimization method with a new fitness function that takes into account four major parameters to achieve the following goals: improving the CH selection process, reducing energy transmission range, increasing network stability and lifetime, and improving WSN coverage. A comparison simulation with the most recent related methods in MATLAB simulator is performed to evaluate the performance of our proposed algorithm. The simulation findings demonstrate that applying the developed evolutionary approach reduces the network’s energy consumption rate and increases the wireless network throughput. In various network scenarios, our suggested approach surpasses all other examined methods, extending the network coverage and prolonging the stability periods of Evolutionary Routing Protocol (ERP), Energy-Efficient Weighted Clustering (EEWC), Distance Incorporated Modified Stable Election Protocol (D-MSEP), and Energy dependent cluster formation (EDCF) by 55%, 43%, 26%, and 12% respectively.

An enhanced routing algorithm based on a re-position particle swarm optimization (RA-RPSO) for wireless sensor network

Energy saving is a major requirement in the design of routing algorithms to maximize the throughput and lifetime of wireless sensor networks. To address this requirement, this work proposes a routing algorithm based on a novel Reposition Particle Swarm Optimization (RPSO) algorithm and a new fitness function. The role of the RPSO algorithm is to salvage particles that may have fallen into local minima, if any. The algorithm is evaluated using three types of benchmark functions, namely unimodal, multimodal, and rotated multimodal. The evaluation results demonstrate that RPSO outperforms native Particle Swarm Optimization and Particle Swarm Optimization with Levy Flight in terms of convergence speed and global optimum identification. The proposed RPSO is then employed to develop an efficient routing algorithm. The routing algorithm is tested in terms network lifetime, number of dead sensor nodes, energy consumption, and number of packets delivered to base station, against four competitive Meta-Heuristic algorithms. The test results clearly indicate that the routing algorithm outperforms all the competitive algorithms for all the performance metrics. In particular, it achieves 14% to 29% higher network lifetime, 52% to 58% less dead sensor nodes, 61% to 70% less energy consumption, and 13% to 36% more throughput.

An intelligent access algorithm for large scale multihop wireless networks based on mean field game

In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query ID="Q1" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network.

Power and delay optimization based uplink resource allocation for wireless networks with device-to-device communications

In this paper, the resource allocation problem for all devices is investigated in a multi-sharing uplink scenario of mmWave cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) based wireless networks with device-to-device (D2D) communications. More specifically, a power and delay optimization based uplink resource allocation (PDO-URA) algorithm is proposed divided into two steps. At first, network resources are allocated to cellular user equipments (CUEs) in terms of power and transmission rate through a proposed approach that intends to maximize throughput. Next, idle resources are allocated by considering delay minimization. To this end, we propose to apply an algorithm based on delay optimization where idle resources are shared with D2D user equipments (DUEs) in the network considering the formed conflicts graphs and the delay information estimated through Network Calculus concepts such as envelope process and service curve. Computational simulations are carried out considering a communication scenario with 5G characteristics such as millimeter waves at frequencies above 6 GHz, comparing also the performance of other algorithms from the literature in terms of quality of service (QoS) parameters such as throughput and delay. The results confirm that the proposed algorithm presents superior performance in terms of throughput and delay for the mmWave CP-OFDM wireless networks with D2D communications than the other considered algorithms.

QoS Enhancement of Wireless Campus Network Using Throughput Parameter

In recent years, broadband wireless systems developed themselves as one of the fastest growing and emerging sector in the field of telecommunications. It is a network of multiple interconnected local area networks in a limited geographical area. Quality of Service (QoS) is considered as one of the key issue in Wireless Campus Network due to the bottleneck and the competition among different kinds of traffic flow on the network. In this paper a Short-Term and Long-Term models are developed to enhancements QoS performance for Wireless Campus Network using throughput parameter. The models were designed and simulated using Optimized Network Engineering Tool, OPNET IT GURU Academic Edition with linkages between wireless workstations suitable for implementation to determine the performance of the network throughput. The two versions contain the same number of wireless workstations, but with some additional access points and multiple processors in the long-term model, a point-to - point protocol (PPP) was used to link the access points to the routers and the cloud while using 100BaseT cable to connect. In each case, the wireless network throughput was obtained. The findings obtained from these models showed that, relative to the short-term model, the Long-Term model achieves higher wireless network performance. That may be due to the fact that while in both models the total number of wireless workstations is the same, the long-term model topology includes additional access points and double processors resulting in the observed minimum throughput.