Maximum Network Throughput Research Articles

Virtualized network functions resource allocation in network functions virtualization using mathematical programming

Network Functions Virtualization (NFV) revolutionizes network services by eliminating the need for dedicated hardware. This virtualization enables flexible and efficient deployment of various network functions like proxies, firewalls, and load balancers. Providing the service requested by the user in the network is done by a sequence of virtual network functions, which are known as service functions chain. One of the main challenges in the development of network functions virtualization architecture is the allocation of resources to the requested network services in network infrastructures, this challenge is called network function virtualization resource allocation problem. Therefore, this paper addresses the resource allocation problem in Network Functions Virtualization (NFV) architectures using mathematical programming techniques. A multi-objective mixed-integer linear programming (MILP) model is proposed to optimize resource allocation for virtual network functions (VNFs). The model incorporates constraints related to node and link resource capacities, as well as delay requirements. The objective functions focus on maximizing network throughput, minimizing node resource costs (CPU cores and memory), reducing capital and operational expenses, and ensuring efficient execution time. These constraints and objective functions are formally defined by mathematical functions. The proposed mathematical model is implemented and solved using the Cplex solver. To evaluate the effectiveness of the proposed mathematical model, various network topologies were evaluated under different parameters. These parameters included the length of Service Function Chains (SFCs), the number and length of flows, node resource capacities, the number of nodes and VNFs. The experimental results demonstrated the model’s ability to efficiently allocate resources to VNFs across these different scenarios.

A Survey on Throughput Maximization in IoT Networks

This paper presents a comprehensive survey on Non-Orthogonal Multiple access for multiple access in wireless networks. As networks are now migrating towards the wireless domain as opposed to the wired domain, the need for more efficient bandwidth allocation mechanism is necessary. With the advent of big data applications, increasing number of users, increased bandwidth and data requirement yet limited bandwidth availability have become serious challenges. Hence future generation wireless networks would need improved multiple access techniques than their present day counterparts. Non-Orthogonal Multiple access is a technique in which multiple users data is separated in the power domain. The problems addressed by NOMA are low overall bandwidth for multiple users. This paper presents a comprehensive review and taxonomy of the NOMA based multiple access framework. Index Terms:- Non-Orthogonal Multiple Access, Wireless Networks, Quality of Service (QoS), Bit Error Rate, Throughput.

Maximizing Network Throughput in Heterogeneous UAV Networks

Maximizing Network Throughput in Heterogeneous UAV Networks

D2PG: deep deterministic policy gradient based for maximizing network throughput in clustered EH-WSN

D2PG: deep deterministic policy gradient based for maximizing network throughput in clustered EH-WSN

Multi-Channel Cooperative Spectrum Sensing and Scheduling for Cognitive IoT Networks

This paper presents a novel multi-channel cooperative spectrum sensing and scheduling (MC$_2$S$_3$) framework for spectrum and energy harvesting cognitive Internet of Things (IoT) networks. We address the challenge of maximizing network throughput by formulating a combinatorial problem that jointly optimizes the sensing scheduling of primary channels (PCs), the assignment of IoT devices for sensing scheduled PCs, and the clustering and allocation of IoT nodes to efficiently use discovered idle PCs; subject to spectrum utilization and collision avoidance constraints. Recognizing the inherent complexity of the underlying NP-hard mixed-integer non-linear programming (MINLP) problem, we propose a decomposition strategy that decouples the master problem into PC exploration and exploitation sub-problems. In the exploration phase, we derive closed-form solutions for optimal sensing durations and detection thresholds that satisfies spectrum utilization and collision avoidance constraints, which are then used to develop a priority metric to rank PCs. The proposed PC ranking informs a sequential PC scheduling and IoT sensing assignment approach that exploits a linear bottleneck assignment (LBA) strategy, proceeding until further scheduling does not enhance network utility. For the exploitation phase, we leverage a non-orthogonal multiple access (NOMA) strategy to multiplex multiple IoT nodes on a single PC, employing an iterative linear sum assignment (LSA) method for efficient allocation to maximize utilization of idle PCs. Numerical results validate the efficacy of our proposed methodologies, reaching an accuracy of approximately 99% in the order of milliseconds, significantly outperforming time complexity of brute-force benchmarks.

Queue stability and dynamic throughput maximization in multi-agent heterogeneous wireless networks

Queue stability and dynamic throughput maximization in multi-agent heterogeneous wireless networks

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning

Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.

Efficient Throughput Maximization in Dynamic Rechargeable Networks

In the Dynamic Rechargeable Networks (DRNs), to maximize the throughput by efficient energy allocation, the existing studies usually consider the spatio-temporal dynamic factors of the harvested energy, and seldom the network dynamic factors simultaneously, such as the time variable network resources and wireless interference. To take the network dynamic factors together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs. We introduce the Time-Expanded Graph (TEG) to describe the above dynamic factors in an obvious way and design the Single Pair Throughput maximization (SPT) algorithm based on TEG. In the case of multiple pairs of source-targets, this paper introduces the Garg and Könemann's framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. To reduce the time complexity, this paper proposes a Distributed and Parallel Throughput algorithm (DPT). In real applications, the network dynamic factors may not be known in advance. This paper proposes an Online Time-Span Algorithm (OTA) with Markov approximation and Lyapunov optimization, and conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms.

Throughput Maximization in Cellular Networks With Wireless Backhaul and Energy Harvesting

Throughput Maximization in Cellular Networks With Wireless Backhaul and Energy Harvesting

Interference-Aware Resource Allocation Algorithm for D2D-Enabled Cellular Networks Using Matching Theory

To enhance spectral efficiency and network throughput in Device-to-Device (D2D)-enabled cellular networks, various resource sharing methods can be utilized. However, when users in such dense and complicated networks share the same resources, co-channel interference arises. Therefore, more efficient and comprehensive frameworks should be used to accurately model the network and then decrease co-channel interference and increase resource efficiency. This work proposes a novel and practical resource allocation framework that considers more realistic assumptions based on matching theory with externalities. The goal of the proposed framework is to maximize network throughput by minimizing the interference between co-channel cellular and D2D communications. Moreover, the effect of the interference between various types of communications on the network’s quality of service is studied. The spatial positions of the users’ equipments are modeled by using the Poisson Point Process. It is shown that the proposed method results in a stable resource allocation matching after a finite number of iterations. Based on the analytical and simulation results, the proposed algorithm achieves near-optimal network performance with a low computational cost.

Throughput Maximization of Dynamic TDD Networks with a Full-Duplex UAV-BS

Throughput Maximization of Dynamic TDD Networks with a Full-Duplex UAV-BS

Performance Enhancement of Cross-Layer Cognitive Media Access Control Protocol for Wireless Sensing Networks Using Hybrid Intelligent Optimization Approach

In recent times, wireless sensing networks (WSNs) application usage is increased in a great way. Access to the communication channel is handled by the Medium Access Control (MAC) layer. At the MAC, a control channel is utilized to determine collision-free data transport pathways. As a result, with cognitive radio (CR) technology, the control channel architecture is critical to obtaining mandatory quality of Service (QoS). However, latency, network communication delay, and energy consumption are the main problem. In this article, a novel African Buffalo allied Jellfish Optimization (AFJO) is proposed for clustering and optimal Cluster Head (CH) selection. The hybrid intelligence method uses a unique probabilistic assessment rule purpose as a fitness role to find the best data transmission path while avoiding congestion, which is named as Fuzzy Interfaced Red Deer (FIRD). The proposed protocol’s performance is evaluated using Network Simulator (NS2), which takes into account parameters such as energy consumption, computational complexity, and Quality of Service (QoS) performance with radio frequency integrated parameters. The suggested technique decreases energy consumption, end-to-end latency, communication overhead, and maximizes network throughput when compared to state-of-the-art cross layer cognitive mac protocol for WSNs system approaches.

Effect of transfer costs on traffic dynamics of multimodal transportation networks

The study of traffic dynamics on complex networks has attracted much attention due to its practical role in transportation networks. Different transportation networks, such as aviation, railway, and road networks, are combined to form the multimodal transportation network. On the multimodal transportation network, the transfer cost, which is required for changing the mode of transportation, is one of the factors affecting the network traffic dynamics. Therefore, this paper focuses on the traffic dynamics on a multimodal transportation network considering transfer costs and analyzes the impact of transfer costs on network throughput. The simulation results show that the multimodal transportation network with low transfer cost has a higher throughput compared to the network without transfer cost. Meanwhile, high transfer cost significantly restricts transfer, which reduces the network throughput. To achieve the maximum network throughput, the optimal ratio of economic cost and time cost with the consideration of transfer cost is found through theoretical analysis in this paper.

Reveal: Robustness-aware VNF placement and request scheduling in edge clouds

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal–dual based algorithm with a competitive ratio of 1−ϵ,O(log1/ϵ) where ϵ∈(0,1). Experiment and simulation results show that Reveal can achieve better performance and robustness than other alternatives.

Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks

One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales.

Efficient resource allocation algorithms for multihop D2D approach in 5G network

SummaryNow the 5G network systems are expected to enhance their throughput. Device‐to‐device (D2D) communication is one such technique that offers an effective solution. The existing cellular user (CU) communication must be ensured that this solution had minimized its interference with it. Hence, designing methods for resource allocation for D2D transmission is also important. Such allocation will maximize network throughput without causing much interference with the present communication. Apart from these, it ensures delay minimization and achieves fairness among user data rates. But when distance is increased, an extension from direct D2D communication (single‐hop) to multi‐hop is necessary. At the same time, its objectives shall be retained. In the proposed work, initially, a comparative study is done on different algorithms for multi‐hop (two‐hop) D2D communication pairs. While studying different algorithms such as random allocation (RA), graph allocation (GA), particle swamp optimization (PSO), and technique for order preference by similarity to the ideal solution (TOPSIS), they are also analyzed, looking for the possibility of a particular combination of them to achieve specific objectives. The realized method has improved performance even in the situation of an increase in distance. The arrived two methods also support a better data rate. The first method allocates resources based on orthogonal sharing. The second method is for allocating resources to them that have cellular orientation. The simulated results exhibit the improved capability of the proposed methods. The method based on the graph to allocate the mentioned resources functions better. Finally, for throughput and data rates, PSO and TOPSIS techniques are implemented.

New Energy Efficient Routing Protocol in Wireless Sensor Networks Using Firefly Algorithm

Energy constraint has become the major challenge in designing wireless sensor networks. Network lifetime is considered the most substantial metric in these networks. The routing technique is one of the best choices for maintaining a network lifetime. This paper demonstrates the implementation of the new methodology of routing in WSN using Firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor network routing. For network cutoff avoidance while maximizing net lifetime, energy exhaustion must be balanced. Balancing energy consumption is the key feature for the rising net lifetime of WSNs. This routing technique involves the determination of the optimal route from the node toward the sink to make energy exhaustion balance in the network and at the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime.

Throughput properties and optimal locations for limited deployment of Max-pressure controls

Max-pressure (MP) control is one of the few traffic signal controllers proven to maximize network throughput or maximally stabilize the network. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections. Previous work did not consider a limited number of MP controlled intersections while proving the stability properties. Therefore, it is not clear whether a network can still be stabilized with a limited deployment of MP control. Using Lyapunov drift techniques this paper proves that even with a limited deployment, MP control can stabilize a network within feasible demand. Then we present an optimization formulation to find the optimal intersections to install MP control given a limited budget. We also present a greedy efficient algorithm to solve that optimization problem and prove that the algorithm solves the problem to optimality. Numerical results from simulations conducted on the downtown Austin network using an in-house custom simulator called AVDTA are then presented. The change in theoretical maximum servable demands for different amounts of deployments obtained from the optimization problem seemed to match with simulation results most of the times. We found that limited deployment of MP control almost always performed better than random deployment of MP control in terms of servable stable demand. Average total queue length and link density were observed to decrease as the number of MP controls increased which indicates better network performance. Average travel times per vehicle also decreased with installations of MP controls which shows how the travelers would benefit from more MP controls.

Singular Direction-Based Quantizer and Receiver Designs for User Cooperative Distributed Reception

In this article, we develop a distributed reception system to fully utilize spatial multiplexing gain by exploiting the massive connections in beyond fifth-generation networks. In a distributed reception system, assistant receiving nodes have to quantize the received signals in order to forward them to a final destination node. A signal quantizer with high-resolution codebooks can improve signal quantization performance, although it burdens the data-exchange link between devices. We propose signal quantization and reception strategies to facilitate the distributed reception system by allowing a small amount of data-exchange. First, we develop a singular direction-based signal quantizer to include more channel gain in the received signals by employing low-resolution codebooks. Furthermore, we develop a minimum mean square error type receiver for a user cooperation network to facilitate distributed reception without the aid of a fusion center. Lastly, a quantization resource allocation algorithm is developed to maximize network throughput by using limited quantization resources. Simulation results are presented to evaluate the sum-rate performance of the quantized distributed reception system.