Maximize System Throughput Research Articles

A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks

AbstractThe world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.

A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.

Reinforcement learning-based resource allocation for dynamic aggregated WiFi/VLC HetNet

High transmission rates and low power consumption make visible light communication (VLC) a highly promising supplementary technology for the next generation of mobile communication. Taking into account the limited coverage area, VLC can be combined with WiFi as a heterogeneous network (HetNet), thanks to their non-overlapping spectrum. In order to fully utilize the advantages of the WiFi and VLC technologies, a new aggregated WiFi/VLC HetNet consisting of a single WiFi AP and multiple VLC APs, is designed, where the user equipment (UE), for the first time, can access multiple VLC APs and one WiFi AP simultaneously, and be allowed to acquire multiple resource blocks (RB) in each AP at the same time. To optimize the performance of the above designed HetNet, a multi-objective optimization problem (MOOP) is formulated, which aims to maximize system throughput while reducing the handover rate. Since the above MOOP is non-convex and nonlinear, the traditional resource allocation (RA) algorithm has a complex calculation process and poor timeliness performance to deal with this problem. To solve the above MOOP, a reinforcement learning (RL)-based RA algorithm is proposed, and considering the RB handover overhead in the aggregated WiFi/VLC HetNet, a reward function related to the system throughput and the UE handover rate is carefully designed. System throughput, system handover rate, user satisfaction, as well as user fairness performance are analyzed under three typical indoor illumination layouts. Finally, compared with the greedy algorithm and the hypergraph-based carrier aggregation algorithm, numerical results show that the proposed RL-based RA algorithm could improve the system throughput over the former two algorithms by 30.26% and 19.71%, while reducing the system handover rate by 0.15% and 0.02%, respectively.

Nonlinear energy harvesting based alternate cooperative nonorthogonal multiple access with adaptive interference cancellation

We consider an energy-limited network, where a source node intends to communicate with two energy harvesting (EH) near-users and one far-user. We design a nonlinear EH-based alternate cooperative nonorthogonal multiple access (CNOMA) scheme, where the source node alternately superimposes the data of each near-user and the far-user and broadcasts the composite signal. When one near-user decodes the data of both the far-user and itself by using the adaptive interference cancellation technique, the other near-user can harvest energy from the received signal. The two near-users alternately and adaptively forward data to the far-user. We properly model the energy statuses of both the two near-users by defining a group of unequally-spaced discrete energy levels. We analyze the average success probability and throughput of each user, and use the differential evolution (DE) algorithm to efficiently achieve the maximum system throughput by jointly optimizing both the time and power allocation factors. We also present two benchmark EH-based transmission schemes for comparison. Extensive simulations are conducted to validate our theoretical analysis and reveal the impacts of various parameters.

Node clustering scheme supporting mobility in NOMA-enabled backscatter communication networks

In this paper, we study the problem of mobile node clustering in non-orthogonal multiple access (NOMA) backscatter communication networks. The nodes are clustered based on the differences between their channel gains to accomplish NOMA transmission within the clusters. However, as the channel gains change constantly with node mobility, the nodes need to be re-clustered, which leads to a significant computational overhead. We propose a node clustering scheme (IGAC). This scheme formulates the node clustering problem as a combinatorial optimization problem to find the optimal node clustering that maximizes system throughput. To handle the large search space of multi-node clustering, we employ a genetic algorithm to solve this optimization problem effectively. Moreover, to overcome the NOMA principle violation problem(NPVP) caused by mobile nodes, we propose a node clustering adjustment scheme (SMSO) based on stable matching ideas and swapping operations. According to the theory of stable matching, optimizing the long-term throughput of the system can be achieved by swapping nodes between different clusters to eliminate blocking pairs in clustering. The simulation results show that our proposed node clustering scheme outperforms traditional schemes in terms of system throughput.

Application of IoT technology in cyber security prevention system

Abstract In the process of gradually expanding the scale of computer networks and the design of network systems becoming more and more complex, people pay more and more attention to the construction of network security protection systems. Starting from the blockchain encryption technology, the article establishes the authentication and access management key based on the elliptic curve encryption algorithm and combines the maximum entropy model with the hidden Markov model to construct the MEMM for intrusion detection of network security. Based on the effective signal-to-noise ratio model of the network channel, an adaptive channel selection strategy based on the UCB algorithm is proposed. The IoT security prevention system is built based on IoT technology, and each functional module of the system is designed. The system’s authentication security, network intrusion detection, adaptive channel selection, and concurrency performance were tested after the design was completed. The encryption operation time of the ECC algorithm was improved by 41.53% compared to the RSA algorithm, the average time of the MEMM network intrusion detection was 41.54ms, and the false alarm rate of the intrusion detection was kept below 16.5%. The average packet collection rate of the nodes in the adaptive channel selection algorithm is 90.98%. The maximum system throughput is up to 62.19MB, and the extreme difference in data volume between different nodes is only 38 entries. Constructing a network security prevention system based on IoT technology and combining multiple encryption techniques can ensure the secure transmission of network data.

Performance Analysis of Various Scheduling Algorithms using CloudSim

Cloud computing technologies have quickly changed how companies and organizations manage their IT resources. The core of this transformation has evolved as cloud datacenters, which offer scalable and affordable options for hosting and administering a variety of applications and services. One information technology typology that has been widely employed to deliver a range of services via the Internet is cloud computing. It guarantees simpler access to premium services and resources. Cloud systems' operation needs to be planned in order to effectively deliver services to individuals. Task scheduling seeks to maximize system throughput and distribute diverse computational resources to software programs. The unpredictability of the scenario grows as the task and has a strong potential for successful resolution. The study begins with an experimental setup to analyse the various performance metrics of task scheduling algorithms. Every experiment has several important stages. To replicate scenarios found in the real world where jobs are divided across many computing resources, the tasks are assigned to available data centers. A number of experiments were carried out to analyse the performance of First Come First Service (FCFS), Shortest Job First (SJF), Round Robin (RR) and Particle Swarm Optimization (PSO) scheduling algorithms using the parameters: makespan, average completion time, average waiting time, and average cost consumption. Thus, this study provides a description of task scheduling and the performance analysis of algorithms to task scheduling that is employed in cloud computing environments.

Optimal Coverage of Full Frequency Reuse in FFR Networks in Relation to Power Scaling of a Base Station.

As a strategy to coordinate inter-cell interference in cellular networks, a fractional frequency reuse (FFR) system is proposed, in which the frequency bandwidth is split into two orthogonal bands; users staying near the center of a FFR cell use the band with a frequency reuse (FR) factor of one (i.e., full FR), and users located close to the cell edge utilize the band with a FR factor greater than one (i.e., partial FR). Full FR coverage, which identifies full FR and partial FR regions (that is, near-center and near-edge regions) within a FFR cell, has a crucial effect on system performance. Some of the authors of this paper recently investigated the optimization of full FR coverage to maximize system throughput. They analytically showed that under the constraint of satisfying a specified target outage probability, the optimal full FR coverage is a non-increasing function of base station power when all base station powers in the cellular network are scaled at an equal rate. Interestingly, in this paper, it is proven that as the power of a single base station is scaled, the optimal full FR coverage in that cell is a non-decreasing function of base station power. Our results provide useful insight into the design of full FR coverage in relation to the transmit power of a base station. It gives a deeper understanding of the intricate relationship between important FFR system parameters of base station power and full FR coverage.

Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicle in Intelligent Transportation Gateway

To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent edge computing, brain-inspired computing techniques are proposed in this study, which is a promising biologically inspired method by using brain cognition mechanism for various applications. A neuromorphic approach in a scalable and fault-tolerant framework is presented, targeting to realize the navigation function for the edge computing in IoV applications. A novel fault-tolerant address event representation approach is proposed for the spike information routing, which makes the presented model both scalable and fault-tolerant. Experimental results reveal that the proposed approaches can enhance the communication distance, the load balancing and the maximum throughput of the neuromorphic system accordingly. Based on the proposed neuromorphic model, the effects of the dopamine level are investigated. Besides, the results show that the proposed work can realize the accurate obstacle avoidance for the edge IoV computing, and the performance of the proposed network is superior to the network without the proposed scalable and fault-tolerant design. Therefore, the proposed IoV model provides an experimental basis for the improvement of the IoV system.

Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks

Optimal buffer allocations can significantly improve system throughput by managing variability and disruptions in manufacturing or service operations. Organisations can minimise waiting times and bottlenecks by strategically placing buffers along the flow path, leading to a smoother and more efficient production or service delivery process. Determining the optimal size of buffers poses a challenging dilemma, as it involves balancing the cost of buffer allocation, system throughput, and waiting times at each service station. This paper presents a framework that utilises finite queueing networks for performance analysis and optimisation of topologies, specifically focusing on buffer allocations. The proposed framework incorporates a finite closed queuing network to model the intra-logistics material transfer process and a finite open queueing network to model the outbound logistics process within a manufacturing setup. The generalised expansion method (GEM) is employed to calculate network performance measures of the system, considering the blocking phenomenon. Discrete event simulation (DES) models are constructed using simulation software, integrating optimisation configurations to determine optimal buffer allocations to maximise system throughput. The findings of this study have significant implications for decision-making processes and offer opportunities to enhance the efficiency of manufacturing systems. By leveraging the proposed framework, organisations can gain valuable insights into supply chain performance, identify potential bottlenecks, and optimise buffer allocations to achieve improved operational efficiency and overall system throughput.

Sum throughput optimization of wireless powered IRS-assisted multi-user MISO system

Intelligent reflecting surface (IRS) is a promising technology for beyond-5G wireless communication systems. However, the energy demand of IRS is often overlooked in existing works, leading to performance issues in practical scenarios. To address this issue, this paper proposes an operating model based on time switching (TS) protocol for an IRS-assisted multi-user multiple-input single-output (MISO) system, which can provide energy for IRS through wireless power transfer (WPT) technology. The system throughput maximization problem is addressed to improve performance. Specifically, a two-stage algorithm combined with alternating optimization, denoted as TAO, is proposed. To further improve the optimization process in large-size IRS scenarios, an improved deep deterministic policy gradient (DDPG) method combined with TAO, denoted as TAO-DDPG, is also proposed. Numerical results demonstrate that the proposed TAO-DDPG algorithm achieves similar performance to TAO while greatly reducing the optimization time.

Two-way UAV-aided systems with short packet communications

In this paper, we investigate a two-way unmanned aerial vehicle (UAV)-aided system, where two sources communicate with each other with the aid of an UAV relay. Two downlink transmission schemes consisting of non-orthogonal multiple access (NOMA) and network coding (NC) have been studied under finite blocklength regime. Considering imperfect successive interference cancellation (SIC), we have derived the exact and approximate expressions of the systems’ average block-error rates (BLERs). In particular, we propose a solution to maximize system throughput by optimizing packet length. In addition, we have also quantified the effects of the system parameters on the BLER performance, including the transmit power, the power allocation coefficients, and the UAV’s altitude. Numerical results demonstrate that the performance gap between the NOMA-based and NC-based systems is negligible.

Empowering communication networks with MMR scheduler: A novel approach to balancing user throughput and fairness

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms.

Performance evaluation of opportunistic schedulers based on fairness and throughput in new-generation mobile networks

This paper discusses radio resource management in the telecommunications industry that utilizes opportunistic scheduling approaches and methods. It proposes a new scheduler called IFMR (Improved Fairness of MaxRate) to extend the opportunistic approach with the proposition of a new scheduling solution that enables to significantly maximize the system throughput. Our goal is to ensure better results that maximize system throughput, ensure perfect fairness at three levels and maximize the number of satisfied users who have achieved their desired throughput. Similarly, the performance of the proposed algorithm is evaluated and compared with reference algorithms: round robin, MaxSNR and proportional fair (PF). The focus of this study is to evaluate and analyze the quality of service guaranteed to each network subscriber, considering the heterogeneous data traffic based on simulations. The algorithms presented in this paper are programmed and simulated using the MATLAB simulation software. The study aims to effectively analyze the impact of radio resource management algorithms and schedulers, including the round robin algorithm, MaxSNR and PF, among others, with the main objective of evaluating the performance of the schedulers used by telecommunication and cellphone network operators. The scheduling phase, which precedes resource allocation and assignment, is a critical aspect of the study. The impact of these schedulers on throughput and fairness will also be evaluated and analyzed based on specific criteria.

K-Degree Parallel Comparison-Free Hardware Sorter for Complete Sorting

This paper presents a novel parallel comparison-free hardware sorting architecture that sorts N, n-bit elements consuming linear worst case sorting latency of O(N) clock-cycles utilizing k-parallel clusters. A parallel cluster contains a number of identical blocks utilizing a few fundamental logic components. The architecture achieves a speed-up of nnk+k over non-parallel architectures. The proposed sorting architecture identifies the largest element in every cycle with smaller clock periods compared to state-of-the-art comparison-free approaches and does not require any kind of pre-processing of the input data set, unlike most of the existing hardware sorters. The experimental result shows the degree of parallelism (k) for 16-bit, 32-bit, and 64-bit elements are respectively {4}, {4, 5, 6, 7, 8} and {8} to achieve maximum system throughput. This sorter achieves a throughput of ≈194 Million-Elements-per-second (MEps) for k=6 and, it is ≈142 MEps for k=8 to sort 256 elements of 32-bit and 64-bit, respectively, whereas it is ≈159 MEps for k=6 to sort 512 elements of 40-bit.

Study of Resource Allocation for 5G URLLC/eMBB-Oriented Power Hybrid Service.

With the rapid development of the 5G power Internet of Things (IoT), new power systems have higher requirements for data transmission rates, latency, reliability, and energy efficiency. Specifically, the hybrid service of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) has brought new challenges to the differentiated service of the 5G power IoT. To solve the above problems, this paper first constructs a power IoT model based on NOMA for the mixed service of URLLC and eMBB. Considering the shortage of resource utilization in eMBB and URLLC hybrid power service scenarios, the problem of maximizing system throughput through joint channel selection and power allocation is proposed. The channel selection algorithm based on matching as well as the power allocation algorithm based on water injection are developed to tackle the problem. Both theoretical analysis and experimental simulation verify that our method has superior performance in system throughput and spectrum efficiency.

An adaptive heuristic algorithm to solve the network slicing resource management problem

SummaryArtificial intelligence‐based (AI‐based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end‐user demands. Some researchers argue that AI‐enable network may run the danger of having private information compromised. We still need a backup rule‐based methodology to allocate bandwidth resource to each slice, if the AI‐based method suddenly encounters security issues. To design such a rule‐based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial‐time completeness (NP‐completeness)? (2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI‐based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP‐completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI‐empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%–97% of the maximal system throughput by brute‐and‐force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.

Joint User Association and Beamforming Multi-Objective Optimization for Distributed mmWave Networks

In this letter, we investigate multi-objective optimization methods for joint user association and beamforming in downstream distributed millimeter-wave (mmWave) networks. Multiple goals are considered together, including maximizing system throughput, minimizing end-to-end latency between users and remote radio units (RRUs), and RRU load balancing. We use Fuzzy Inference System (FIS) to dynamically infer the weights of different objective functions according to user needs, assisting operators to provide customer-centric network access services. In addition, we innovatively adopt the extended gene coding method in the genetic algorithm. The simulation results show that extending the genetic code can significantly improve the performance of the genetic algorithm. The user-centric network access service can be realized by the fuzzy logic and the genetic algorithm. In this way, network throughput, end-to-end delay between users and RRUs, and load balancing can be simultaneously improved.

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.

User Pairing and Power Allocation for UAV-NOMA Systems Based on Multi-Armed Bandit Framework

In this paper, we investigate the joint user pairing and power coefficient allocation for unmanned aerial vehicle (UAV) systems which employ non-orthogonal multiple access (NOMA) to communicate with multiple ground users. Aiming to maximize achievable sum rate and ensure the users' Quality-of-Service (QoS) requirements, we formulate an optimization problem which relies on reinforcement learning (RL) from Multi-Armed Bandit (MAB) framework to propose a solution based on Upper Confidence Bound (UCB) approach. The proposed solution can successfully identify the best action and selects it more often, which leads to maximum system throughput. The attained results show that the proposed scheme finds the best-performing action fast, while the others methods spend a lot of time exploring non-ideal user pairs. As a result, the proposed method accumulates less regret and achieves satisfactory results in terms of system throughput when compared to other user pairing strategies and power allocation (PA) policies.