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针对由小卫星组成的低地球轨道(LEO)卫星星座网络的星上计算能力和存储资源有限,以及传统的星座路由算法虽能很好地适应网络的动态性但对星上计算能力和存储资源的要求都较高的问题,在基于对实际LEO卫星星座网络充分分析的基础上,提出了一种基于离线计算的简洁高效的路由算法.该算法在保证路由有效性的前提下,能够通过使用备份路径来提供流量自适应机制.复杂性分析和仿真结果表明,该算法只需较小的星上存储开销和星上处理开销,而且具有较好的端到端时延性能.该算法简洁、高效的特点使其能作为实际LEO卫星星座网络的实用化路由协议. 相似文献
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Multicast-based data communication is an efficient communication scheme especially in multihop ad hoc networks where the MAC layer is based on one-hop broadcast from one source to multiple receivers. The problem of resource allocation for a set of homogeneous multicast sessions over multihop wireless network is addressed. An iterative algorithm is proposed that achieves the optimal rates for a set of multicast sessions such that the aggregate utility for all sessions is maximised. The authors demonstrate analytically and through simulations that the algorithm achieves optimal resource utilisation while guaranteeing fairness among multicast sessions. The algorithm in network environments with asynchronous distributed computations has been further analysed. Two implementations for the algorithm based on different network settings are presented and show that the algorithm not only converges to the optimal rates in all network settings but it also tracks network changing conditions, including mobility and dynamic channel capacity. 相似文献
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Fahd N. Al-Wesabi Imran Khan Mohammad Alamgeer Ali M. Al-Sharafi Bong Jun Choi Abdallah Aldosary Ehab Mahmoud Mohamed 《计算机、材料和连续体(英文)》2021,69(1):301-317
With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms. 相似文献
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As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks. 相似文献
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Azka Amin Xihua Liu Imran Khan Peerapong Uthansakul Masoud Forsat Seyed Sajad Mirjavadi 《计算机、材料和连续体(英文)》2020,65(2):1487-1505
One of the most effective technology for the 5G mobile communications is
Device-to-device (D2D) communication which is also called terminal pass-through
technology. It can directly communicate between devices under the control of a base
station and does not require a base station to forward it. The advantages of applying D2D
communication technology to cellular networks are: It can increase the communication
system capacity, improve the system spectrum efficiency, increase the data transmission
rate, and reduce the base station load. Aiming at the problem of co-channel interference
between the D2D and cellular users, this paper proposes an efficient algorithm for
resource allocation based on the idea of Q-learning, which creates multi-agent learners
from multiple D2D users, and the system throughput is determined from the
corresponding state-learning of the Q value list and the maximum Q action is obtained
through dynamic power for control for D2D users. The mutual interference between the
D2D users and base stations and exact channel state information is not required during
the Q-learning process and symmetric data transmission mechanism is adopted. The
proposed algorithm maximizes the system throughput by controlling the power of D2D
users while guaranteeing the quality-of-service of the cellular users. Simulation results
show that the proposed algorithm effectively improves system performance as compared
with existing algorithms. 相似文献
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《Communications, IET》2009,3(1):1-9
The IEEE 802.15.3 medium access control (MAC) protocol is an emerging standard for high-rate wireless personal area networks (WPANs), especially for supporting high-quality real-time multimedia applications. Despite defining quality of service (QoS) signalling mechanisms for interoperability between devices, IEEE 802.15.3 does not specify resource allocation algorithms that are left to manufacturers. To guarantee the QoS of real-time variable bit rate (VBR) videos and utilise the radio resource efficiently, the authors propose a dynamic resource allocation algorithm. The proposed bandwidth allocation algorithm is based on a novel traffic predictor. Recently, the variable step-size normalised least mean square (VSSNLMS) algorithm was employed for on-line traffic prediction of VBR videos. However, the performance of the VSSNLMS algorithm significantly degrades due to the abrupt traffic variation occurring at the scene boundary. To tackle this problem, the authors design a novel traffic predictor based on a simple scene detection algorithm and the VSSNLMS algorithm. Analyses using real-life MPEG video traces indicate that the proposed traffic predictor significantly outperforms the VSSNLMS algorithm with respect to the prediction error. The performance of the proposed bandwidth allocation algorithm is also investigated by comparing several existing algorithms. Simulation results demonstrate that the proposed bandwidth allocation algorithm surpasses other mechanisms in terms of channel utilisation, buffer usage and packet loss rate. 相似文献
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A stochastic-flow network consists of a set of nodes, including source nodes which supply various resources and sink nodes at which resource demands take place, and a collection of arcs whose capacities have multiple operational states. The network reliability of such a stochastic-flow network is the probability that resources can be successfully transmitted from source nodes through multi-capacitated arcs to sink nodes. Although the evaluation schemes of network reliability in stochastic-flow networks have been extensively studied in the literature, how to allocate various resources at source nodes in a reliable means remains unanswered. In this study, a resource allocation problem in a stochastic-flow network is formulated that aims to determine the optimal resource allocation policy at source nodes subject to given resource demands at sink nodes such that the network reliability of the stochastic-flow network is maximized, and an algorithm for computing the optimal resource allocation is proposed that incorporates the principle of minimal path vectors. A numerical example is given to illustrate the proposed algorithm. 相似文献
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In this paper, maximizing energy efficiency (EE) through radio resource
allocation for renewable energy powered heterogeneous cellular networks (HetNet) with
energy sharing, is investigated. Our goal is to maximize the network EE, conquer the
instability of renewable energy sources and guarantee the fairness of users during
allocating resources. We define the objective function as a sum weighted EE of all links
in the HetNet. We formulate the resource allocation problem in terms of subcarrier
assignment, power allocation and energy sharing, as a mixed combinatorial and
non-convex optimization problem. We propose an energy efficient resource allocation
scheme, including a centralized resource allocation algorithm for iterative subcarrier
allocation and power allocation in which the power allocation problem is solved by
analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a
water-filling problem thereafter and a low-complexity distributed resource allocation
algorithm based on reinforcement learning (RL). Our numerical results show that both
centralized and distributed algorithms converge with a few times of iterations. The
numerical results also show that our proposed centralized and distributed resource
allocation algorithms outperform the existing reference algorithms in terms of the
network EE. 相似文献
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The optimal allocation of distributed manufacturing resources is a challenging task for supply chain deployment in the current competitive and dynamic manufacturing environments, and is characterised by multiple objectives including time, cost, quality and risk that require simultaneous considerations. This paper presents an improved variant of the Teaching-Learning-Based Optimisation (TLBO) algorithm to concurrently evaluate, select and sequence the candidate distributed manufacturing resources allocated to subtasks comprising the supply chain, while dealing with the trade-offs among multiple objectives. Several algorithm-specific improvements are suggested to extend the standard form of TLBO algorithm, which is only well suited for the one-dimensional continuous numerical optimisation problem well, to solve the two-dimensional (i.e. both resource selection and resource sequencing) discrete combinatorial optimisation problem for concurrent allocation of distributed manufacturing resources through a focused trade-off within the constrained set of Pareto optimal solutions. The experimental simulation results showed that the proposed approach can obtain a better manufacturing resource allocation plan than the current standard meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimisation and Harmony Search. Moreover, a near optimal resource allocation plan can be obtained with linear algorithmic complexity as the problem scale increases greatly. 相似文献
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Xiaoli He Yu Song Yu Xue Muhammad Owais Weijian Yang Xinwen Cheng 《计算机、材料和连续体(英文)》2022,70(1):195-212
Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications. 相似文献
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Satellite networks have high requirements for security and data processing
speed. In order to improve the reliability of the network, software-defined network (SDN)
technology is introduced and a central controller is set in the network. Due to the
characteristics of global perspective, control data separation, and centralized control of
SDN, the idea of SDN is introduced to the design of the satellite network model. As a
result, satellite nodes are only responsible for data transmission, while the maintenance of
the links and the calculation of routes are implemented by the controller. For the massive
LEO satellite network based on SDN, a state evaluation decision routing mechanism is
proposed. The designed mechanism monitors the status of the entire network effectively
and reduces the on-board load on the satellite network. The best routing decision is made
under the comprehensive consideration of the current and historical status of each intersatellite link between Low Earth Orbit (LEO) satellite network nodes. The calculation
and storage requirements are controlled within a reasonable range. Based on the curve
parameter transmission fuzzy encryption algorithm, a safe and reliable condition
assessment decision routing mechanism (CADRM) is designed. It ensures that the
personal information of the LEO satellite network can be transmitted safely and
effectively. The experimental simulation results show the improvement of network
throughput, the reduction of packet loss rate and the enhancing of network reliability. 相似文献
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Beam width and transmitter power adaptive to tracking system performance for free-space optical communication 总被引:3,自引:0,他引:3
The basic free-space optical communication system includes at least two satellites. To communicate between them, the transmitter satellite must track the beacon of the receiver satellite and point the information optical beam in its direction. Optical tracking and pointing systems for free space suffer during tracking from high-amplitude vibration because of background radiation from interstellar objects such as the Sun, Moon, Earth, and stars in the tracking field of view or the mechanical impact from satellite internal and external sources. The vibrations of beam pointing increase the bit error rate and jam communication between the two satellites. One way to overcome this problem is to increase the satellite receiver beacon power. However, this solution requires increased power consumption and weight, both of which are disadvantageous in satellite development. Considering these facts, we derive a mathematical model of a communication system that adapts optimally the transmitter beam width and the transmitted power to the tracking system performance. Based on this model, we investigate the performance of a communication system with discrete element optical phased array transmitter telescope gain. An example for a practical communication system between a Low Earth Orbit Satellite and a Geostationary Earth Orbit Satellite is presented. From the results of this research it can be seen that a four-element adaptive transmitter telescope is sufficient to compensate for vibration amplitude doubling. The benefits of the proposed model are less required transmitter power and improved communication system performance. 相似文献
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应急管理决策通常包括站点选址、资源配置、运输调度等内容,如何从应急处置整体流程控制的视角对决策内容进行集成建模及优化,是应急管理研究付诸实际应用的关键。本文提出具有资源和不确定时间约束的应急工作流网模型,通过三类库所(状态库所、动作库所、资源库所)及三类时间属性(可视时间、静态时间、动态时间),揭示多部门联合应急中的作业时序与资源占用关系。在给定整体流程最大完成时间的条件下,以资源消耗与占用成本、资源运输与惩罚成本总和为目标函数,建立应急资源配置与路径规划的集成问题模型,并采用遗传粒子群混合算法对问题进行求解。根据遗传优化得到的应急资源配置方案,借助应急工作流网计算各动作库所、状态库所的时间参数,以此作为约束条件利用嵌套的粒子群算法进行资源运输策略优化。 相似文献
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Device-to-Device (D2D) communication is a promising technology that can
reduce the burden on cellular networks while increasing network capacity. In this paper, we
focus on the channel resource allocation and power control to improve the system resource
utilization and network throughput. Firstly, we treat each D2D pair as an independent agent.
Each agent makes decisions based on the local channel states information observed by itself.
The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user
system. We assume that the D2D pair do not possess any information on the availability
and quality of the resource block to be selected, so the problem is modeled as a stochastic
non-cooperative game. Hence, each agent becomes a player and they make decisions
together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm
based on game theory is established. Secondly, in order to accelerate the convergence rate
of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats
each group as an agent, and then performs multi-agent Q-learning algorithm to determine
the power for each group of D2D users. The simulation results show that the Q-learning
algorithm based on multi-agent can improve the throughput of the system. In particular,
FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while
improving system throughput. 相似文献