Maximum Network Throughput Research Articles

FPGA Based Hardware Implementation For Green Cognitive Radio

Greening of communication systems leads to provide a solution for spectrum scarcity, high energy consumption, secure and fast transmission, and diversity for user access. OFDM with cognitive radio offer satisfactory solutions for all these heads. The cognitive radio system senses the surrounding environment possesses an electromagnetic spectrum. CR can adapt the radio parameter dynamically and autonomously. It improves the performance of the wireless communication system by endowing the opportunity to unlicensed secondary users temporarily. This arrangement maximizes network throughput with an interoperability facility. This paper presents a hardware implementation of a Green Cognitive Radio Communication (GCR) using an FPGA board. It defines the complete process of software and hardware implementation for the practical execution of this novel approach.

Max-pressure signal control with cyclical phase structure

Max-pressure traffic signal control has many desirable properties. It is analytically proven to maximize network throughput if demand could be served by any signal control. Despite its network-level stability properties, the control itself is decentralized and therefore easily computed by individual intersection controllers. Discussions with city engineers have suggested that a major barrier to implementation in practice is the non-cyclical phase actuation of max-pressure control, which can actuate any phase, in arbitrary order, to serve the queue(s) with highest pressure. This arbitrary phase selection may be confusing to travelers expecting a signal cycle, and is therefore unacceptable to some city traffic engineers. This paper revises the original max-pressure control to include a signal cycle constraint. The max-pressure control must actuate an exogenous set of phases in order, with each phase actuated at least one time step per cycle. Each cycle has a maximum length, but the length can be reduced if desired. Within those constraints, we define a modified max-pressure control and prove its maximum stability property. The revised max-pressure control takes the form of a model predictive control with a one cycle lookahead, but we prove that the optimal solution can be easily found by enumerating over phases. The policy is still decentralized. Numerical results show that as expected, the cyclical max-pressure control performs slightly worse than the original max-pressure control due to the additional constraints, but with the advantage of greater palatability for implementation in practice.

Bandwidth-constrained task throughput maximization in IoT-enabled 5G networks

Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse task demands (e.g. heterogeneous tasks with different priority values) under interference constraints in the presence of limited communication and computational resources. In this paper, our goal is to maximize the total number of tasks served by an IoT-enabled 5G network, labeled task throughput, in the presence of heterogeneous task demands and limited resources. Since our original problem is intractable, we propose an efficient two-stage solution based on multi-graph-coloring. We analyze the computational complexity of our proposed algorithm, and prove the correctness of our algorithm. Lastly, simulation results are presented to demonstrate the effectiveness of the proposed algorithm, in comparison with state-of-the-art approaches in the literature.

Throughput Maximization in Uncooperative Spectrum Sharing Networks

Throughput-optimal transmission scheduling in wireless networks has been a well considered problem in the literature, and the method for achieving optimality, MaxWeight scheduling, has been known for several decades. This algorithm achieves optimality by adaptively scheduling transmissions relative to each user’s stochastic traffic demands. To implement the method, users must report their queue backlogs to the network controller and must rapidly respond to the resulting resource allocations. However, many currently-deployed wireless systems are not able to perform these tasks and instead expect to occupy a fixed assignment of resources. To accommodate these limitations, adaptive scheduling algorithms need to interactively estimate these uncooperative users’ queue backlogs and make scheduling decisions to account for their predicted behavior. In this work, we address the problem of scheduling with uncooperative legacy systems by developing algorithms to accomplish these tasks. We begin by formulating the problem of inferring the uncooperative systems’ queue backlogs as a partially observable Markov decision process and proceed to show how our resulting learning algorithms can be successfully used in a queue-length-based scheduling policy. Our theoretical analysis characterizes the throughput-stability region of the network and is verified using simulation results.

A memory-oriented MAC-layer design for future IoT systems

The Internet of Things (IoT) paradigm envisions billions of interconnected things. This high density of things generates a huge amount of data, which results in communication delays and increases the amount of contention. Thus, it is necessary to have efficient Medium Access Control (MAC) protocols to coordinate channel access. In this paper, we consider a network throughput maximization problem in which a set of selfish nodes compete for transmission opportunities in the IoT scenario. To enhance cooperation among the nodes and to reduce the collision rate, we formulate a memory-one channel access game in which the nodes maximize their payoffs by optimizing their channel access probabilities, conditioned on their previous transmission state. A distributed learning algorithm is used by the nodes to solve the problem efficiently in order to overcome any coordination overhead. We investigate the impact of the network topology on the solution to the problem. Our simulation results show that the throughput achieved by the memory-one game outperforms the throughput achieved by other methods including IEEE 802.11 DCF protocol.

RESEARCH THROUGHPUT INDICATORS CORPORATE MULTISERVICE NETWORKS

The performance indicators corporate multi-service networks based on the architectural concept of the following Next Generation Network (NGN) and Future Networks (FN) using a number of innovative technologies are considered. A significant difference between such networks and previous generation networks is that the architecture of future networks is based on technologies software defined networks, virtualization network functions and the multimedia communications subsystem. As a criterion for complex performance indicators corporate multiservice networks, we selected throughput, the coefficient effective use hardware and software systems, various resources, and probability-time characteristics. As a result of the study, a method for calculating the complex throughput indicators corporate multiservice networks based on the architectural concept NGN and FN using SDN (Software Defined Networking), NFV (Network Functions Virtualization) and IMS (Internet Protocol Multimedia Subsystem) technologies is proposed. Th essence of the new method is to find relationships with which you can calculate the maximum throughput of the communication network and the areas feasible solutions of the basic equation that belong to it. System and technical analysis showed that the transmitted heterogeneous traffic has a special structure – self-similarity properties (scale invariance) and a quantitative assessment of the degree traffic self-similarity using the Hurst parameter. Based on the proposed calculation method, the complex indicators of corporate multiservice networks are examined taking into account the parameters traffic self-similarity, QoS (Quality of Service) and QoE (Quality of Experience). Resource management methods in network nodes were studied to improve the throughput characteristics taking into account the numerous QoS & QoE parameters and the coefficient effective use hardware and software systems was determined. Based on the calculation method, analytical expressions have been obtained to evaluate the complex indicators corporate multiservice networks in establishing connections and in providing multimedia services with the necessary parameters. A numerical analysis is carried out and a graphical dependence of the maximum network throughput on the total number software and hardware systems is constructed for a given system load factor.

3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization

This paper investigates a wireless system with multi-Unmanned Aerial Vehicles (UAVs) for improving the overall throughput. In contrast to previous studies that optimize the locations of UAVs and channel assignment separately, this paper considers the two issues jointly by exploiting Partially Overlapped Channels (POCs). The optimization problem of maximizing network throughput is formulated as a non-convex and non-linear problem. In order to find a practical solution, the problem is decomposed into two subproblems, which are iteratively optimized. First, the optimal locations of UAVs are determined under a fixed channel assignment scheme by solving the mixed-integer second-order cone problem. Second, an efficient POC allocation scheme is determined via the proposed channel assignment algorithm. Simulation results show that the proposed approach not only significantly improves system throughput and service reliability compared with the cases in which only orthogonal channels and stationary UAVs are considered, but also achieves similar performance using the exhaustive search algorithm with lower time complexity.

Feedback perimeter control with online estimation of maximum throughput for an incident-affected road network

This study develops a feedback perimeter control strategy to maximize the throughput of an incident-affected network. The proposed perimeter control strategy is innovative in two aspects. First, the control variables, i.e., the inflow rates to the controlled subnetwork within the incident-affected network, are adjusted based on the online estimation of maximum network throughput that is updated dynamically using real-time traffic data and road vulnerability. The incident-dependent network throughput provides the perimeter control a more legitimate control target. Second, the proposed perimeter control strategy applies the proportional-integral-derivative controller, which enhances control stability given the dynamically-updated control target. The results of simulation experiments demonstrate that the proposed strategy can enhance the average speed and reduce the total delay of the incident-affected traffic.

Resource Allocation for Wireless Cooperative IoT Network With Energy Harvesting

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

LTE-Advanced Optimum Relay Placement with Zero Link Overflow Using AMMCS

Multi-hop Relay networks are proposed to fulfill the demanding coverage and capacity requirements for current applications in a cost efficient way. It can be used in IMT-Advanced technologies such as 3GPP LTE-Advanced. In this paper, Resource Blocks (RBs) proposed scheduling scheme is considered for zero multi-hop links overflow in Uplink LTE-Advanced. Then based on this scheduling scheme and other network assumptions the Optimum Relay Station (RS) placement in the cell was estimated using nonlinear optimization problem in order to improve LTE-A Uplink performance. The average throughput, Throughput gain, and Mean File Transfer Time (MFTT) are considered as performance measures. The study takes into consideration the impact of Asymmetry between multi-hop links, the effect of different arrival rates from random positions of UEs in the cell. Adaptive MIMO, Modulations, and Coding Switching (AMMCS) scheme is used to maximize network throughput with low bit error rate (BER). Simulation results show effective improvement in uplink network performance using the proposed scheduling scheme and AMMCS with RS at optimum location in the cell.

Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles

This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures near-optimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides near-optimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.

Swarm Optimization based Gravitational Search Approach for Channel Assignment in MCMR Wireless Mesh Network

Wireless Mesh Networks offers cost-efficient and higher network efficiency by utilizing multiple channels multiple radio(MCMR) nodes. Also addition, the amalgamation of multiple radio nodes and multiple hops mesh framework tends to overcome the limitation of single radio networks like the ability to achieve the rising accessible system bandwidth. In spite of these benefits, certain MCMR wireless mesh networks still suffer from performance issues like network connectivity, network throughput degradation whenever network size increases. Thus, an effective channel assignment (CA) approach could minimize the number of interference co-channels and enhance the throughput of the network. Thus, a hybridized form of gravitational search approach and particle swarm optimization is presented in this paper to resolve the issue of CA. The velocity and position updates of PSO are merged with the GSA operations to obtain the best channel with good connectivity. This approach maximizes the capability of exploration and exploitation for global and local searches using PSO and GSA operations. The goal of this methodology is the minimization of a number of interfering links and the maximization of network connectivity and throughput. The experimental results for this approach are carried out using NS2 and compared with previously suggested heuristic optimization algorithms such as Learning Automated and Genetic Algorithm Approach, Improved Gravitational Search Approach and Dynamic particle swarm optimization Approach. The simulation outcome showed a better performance of the suggested methodology compared to existing methodologies.

A Back-Pressure-Based Model With Fixed Phase Sequences for Traffic Signal Optimization Under Oversaturated Networks

Traffic signal control under oversaturated conditions presents a major challenge in metropolitan transportation networks. Previous works have demonstrated the ability of back-pressure methods to maximize network throughput and guarantee network stability. However, most of these methods are implemented adaptively. At present, fixed phase sequences are still widely used in traffic signal control systems. Herein, we propose a new back-pressure-based signal optimization method that combines fixed phase sequences with spatial model predictive control. First, a spatial prediction model for traffic flow was constructed to analyze the movement of vehicles between a central intersection and four peripheral intersections. Then, a multi-objective optimization model of traffic signal timing was developed with the purpose to reduce the risk of spillover and to balance the distribution of vehicles across the whole network. Next, a method based on the Multi-Objective Particle Swarm Optimization algorithm and Technique for Order Preference by Similarity to an Ideal Solution principle was used to achieve the Pareto frontier and the optimal solution. Finally, traffic simulations were performed in Paramics to assess the performance of the proposed method. The results of the simulations suggest the performance of the proposed method surpasses fixed-time control and cycle-based back-pressure schemes under oversaturated conditions.

Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.

Intelligent IoT Connectivity: Deep Reinforcement Learning Approach

In this paper, we propose a distributed solution to design a multi-hop ad hoc Internet of Things (IoT) network where mobile IoT devices strategically determine their wireless transmission ranges based on a deep reinforcement learning approach. We consider scenarios where only a limited networking infrastructure is available but a large number of IoT devices are deployed in building a multi-hop ad hoc network to deliver source data to the destination. An IoT device is considered as a decision-making agent that strategically determines its transmission range in a way that maximizes network throughput while minimizing the corresponding transmission power consumption. Each IoT device collects information from its partial observations and learns its environment through a sequence of experiences. Hence, the proposed solution requires only a minimal amount of information from the system. We show that the actions that the IoT devices take from its policy are determined as to activate or inactivate its transmission, i.e., only necessary relay nodes are activated with the maximum transmit power, and nonessential nodes are deactivated to minimize power consumption. Using extensive experiments, we confirm that the proposed solution builds a network with higher network performance than the current state-of-the-art solutions in terms of system goodput and connectivity ratio.

A PHY/MAC cross-layer design with transmit antenna selection and power adaptation for receiver blocking problem in dense VANETs

The medium access control (MAC) protocol of IEEE 802.11p dedicated to vehicular ad hoc networks (VANETs) employs carrier sense multiple access with collision avoidance (CSMA/CA) with distributed coordination function (DCF) which prohibits simultaneous transmissions in the same detection area in order to avoid possible interference and collision between neighboring vehicles. This prohibition results in temporary blocking of data reception, which reduces the average network throughput. To solve this problem, we propose a physical (PHY)/MAC cross-layer design based on transmit antenna selection (TAS) and transmit power adaptation (TPA). We consider spatial multiplexing zero-forcing Bell-labs layered space-time (ZF-VBLAST) over multiple-input and multiple-output (MIMO) time-varying flat fading channel to be implemented in vehicle-to-vehicle (V2V) communication. The cross-layer approach is implemented to get the maximal network throughput on the MAC layer by using the channel state information (CSI) obtained from the PHY layer, while the MIMO spatial multiplexing technique is used to increase the spectral efficiency. This design helps transmitters to select the best combination of transmitting antennas to maximize throughput and also to choose the adequate transmit power level to minimize neighbors' interference and collision. Also, this solution comes with a multi-user interference cancellation method that allows simultaneous transmissions as long as the sum of transmit antennas within the same radio range does not exceed the number of receive antennas. This paper evaluates the proposed cross-layer architecture by calculating the average network throughput per V2V links concerning different network parameters such as the number of vehicles and antennas. The simulation results show that this solution allows more vehicles to communicate simultaneously and thus significantly improves the average network throughput compared to 802.11p MAC standard, in particular for VANETs with high density.

SWARM OPTIMIZATION BASED GRAVITATIONAL SEARCH APPROACH FOR CHANNEL ASSIGNMENT IN MCMR WIRELESS MESH NETWORK

Wireless Mesh Networks offers cost-efficient and higher network efficiency by utilizing multiple channels multiple radio(MCMR) nodes. Also addition, the amalgamation of multiple radio nodes and multiple hops mesh framework tends to overcome the limitation of single radio networks like the ability to achieve the rising accessible system bandwidth. In spite of these benefits, certain MCMR wireless mesh networks still suffer from performance issues like network connectivity, network throughput degradation whenever network size increases. Thus, an effective channel assignment (CA) approach could minimize the number of interference co-channels and enhance the throughput of the network. Thus, a hybridized form of gravitational search approach and particle swarm optimization is presented in this paper to resolve the issue of CA. The velocity and position updates of PSO are merged with the GSA operations to obtain the best channel with good connectivity. This approach maximizes the capability of exploration and exploitation for global and local searches using PSO and GSA operations. The goal of this methodology is the minimization of a number of interfering links and the maximization of network connectivity and throughput. The experimental results for this approach are carried out using NS2 and compared with previously suggested heuristic optimization algorithms such as Learning Automated and Genetic Algorithm Approach, Improved Gravitational Search Approach and Dynamic particle swarm optimization Approach. The simulation outcome showed a better performance of the suggested methodology compared to existing methodologies.

A Systematic Study of IEEE 802.11 DCF Network Optimization From Theory to Testbed

In IEEE 802.11 wireless local area networks (WLANs), an important technique for medium access control (MAC) is the distributed coordination function (DCF). Two access mechanisms are provided by DCF, the default basic access mechanism and the optional request-to-send/clear-to-send (RTS/CTS) mechanism. The performance of IEEE 802.11 DCF networks has been predicted recently by NS-2 simulator based on a unified analytical model presenting the delay, throughput and stability. NS-3 and OMNeT++ provide an essential platform to model IEEE 802.11 physical (PHY) and MAC layers, nevertheless the accuracy of which is yet not investigated. In this article we present two studies, first is a comparative simulation study of the unified IEEE 802.11 DCF analytical model, by considering distinct network conditions, various topologies, different access modes and discrete system parameters in NS-3 and OMNeT++. A Linux based testbed is setup to validate the mathematical model and the simulation results. The second is the optimization study to adaptively tune the RTS threshold, so that the network operates in an access mode which steers to the maximum network throughput performance. An explicit expression of RTS threshold, verified by the simulations in NS-3 and OMNeT++, is obtained in contrast to previous studies based on channel estimation and numerical calculations. Performance evaluation is done by comparing the simulation, testbed and theoretical results. This study not only proves the credibility of the theoretical model of IEEE 802.11 DCF, but also assures that the results obtained from NS-3 and OMNeT++ are persuasive and provides a foundation for RTS threshold analysis in IEEE 802.11 WLANs for practical network design considerations.

Priority‐based online flow scheduling for network throughput maximization in software defined networking

SummaryData transmission in current networks is usually associated with strict priority enforcement for the purpose of quality of service (QoS). Under the case that priority flow requests are injected into the network sequentially without the information of future flow request arrivals, it is a challenging to achieve network throughput maximization for on‐line flow requests under the joint constraints of the flow's priority, bandwidth demand, and resource capacity. Software Defined Networking (SDN) can effectively solve the flow scheduling equilibrium problem between the priority of dynamic flow requests and the maximization of network throughput. Therefore, in this paper, we study on‐line flow request admission in SDN, the goal of which is to maximize the network throughput under the constraints of critical network bandwidth resources, flow priority, and bandwidth demands. First, we present the concept of flow routing cost and profit and a model to characterize the cost of using link resources and routing paths. Then, we propose an efficient on‐line priority flow scheduling algorithm (OPFSA) to solve priority flow request scheduling problem and analyze the competitive ratio of OPFSA. Our on‐line algorithm can reach throughput within of the highest possible throughput that can be achieved by an off‐line algorithm, where n is the number of node in the network. Finally, experimental results demonstrate that compared with SHORTEST‐SC, our proposed algorithm can enhance the cumulative bandwidth about 9% and 40% when general network size is 30 and 170 nodes, respectively, and improve the throughput about 25% in Fat‐tree network when pod size is 4.

Effective Resource Planning for Interference Aware Throughput Maximization of Wireless Body Area Networks

Cross-layer design is an in-depth analysis area in especially wireless Body Area networks and is necessary under energy and throughput limitations. To improve network performance the all the layer increases energy consumption; thus, the collaborations connecting these layers ought to be examined. This study develops an optimal interrupt aware connection planning and power management policy that assist a certain data rate on each connection in the network, including signal to interference and noise ratio(SINR) and highest transmission power controls at one another node or center.in each connection as link cost metrics Creating an interrupt-aware development problem by considering the power of each node in the routing path and the associated delay in each connection.the problem is to discover an scheduling link planning, power management and routing policy that maximizes the life span and throughput and minimizes the end-to-end delay as well as the energy link cost subject to definite controls. To provide mathematical guides to our schemes and relationships between the parameters. In our proposed work is an advance step towards our distributed joint planning, power management and routing algorithm for wireless Body Area networks utilizing cross layer design.