Relevant bibliographies by topics / Bandwidth Allocation Problem

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Bandwidth Allocation Problem'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Bandwidth Allocation Problem"

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Ramakrishnan, Sangeeta, Xiaoqing Zhu, Frank Chan, et al. "Optimizing Quality-of-Experience for HTTP-based Adaptive Video Streaming." International Journal of Multimedia Data Engineering and Management 7, no. 4 (2016): 22–44. http://dx.doi.org/10.4018/ijmdem.2016100102.

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In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.
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Thabet, Saqr Khalil Saeed, Emmanuel Osei-Mensah, Omar Ahmed, Abegaz Mohammed Seid, and Olusola Bamisile. "Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation." Applied Sciences 12, no. 10 (2022): 5047. http://dx.doi.org/10.3390/app12105047.

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During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power–bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.
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You, Peng-Sheng, Chun-Chieh Lee, and Yi-Chih Hsieh. "Bandwidth allocation and pricing problem for a duopoly market." Yugoslav Journal of Operations Research 21, no. 1 (2011): 65–78. http://dx.doi.org/10.2298/yjor1101065y.

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This research discusses the Internet service provider (ISP) bandwidth allocation and pricing problems for a duopoly bandwidth market with two competitive ISPs. According to the contracts between Internet subscribers and ISPs, Internet subscribers can enjoy their services up to their contracted bandwidth limits. However, in reality, many subscribers may experience the facts that their on-line requests are denied or their connection speeds are far below their contracted speed limits. One of the reasons is that ISPs accept too many subscribers as their subscribers. To avoid this problem, ISPs can set limits for their subscribers to enhance their service qualities. This paper develops constrained nonlinear programming to deal with this problem for two competitive ISPs. The condition for reaching the equilibrium between the two competitive firms is derived. The market equilibrium price and bandwidth resource allocations are derived as closed form solutions.
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Wang, Heng, Aijun Liu, and Xiaofei Pan. "Optimization of Joint Power and Bandwidth Allocation in Multi-Spot-Beam Satellite Communication Systems." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/683604.

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Multi-spot-beam technique has been widely applied in modern satellite communication systems. However, the satellite power and bandwidth resources in a multi-spot-beam satellite communication system are scarce and expensive; it is urgent to utilize the resources efficiently. To this end, dynamically allocating the power and bandwidth is an available way. This paper initially formulates the problem of resource joint allocation as a convex optimization problem, taking into account a compromise between the maximum total system capacity and the fairness among the spot beams. A joint bandwidth and power allocation iterative algorithm based on duality theory is then proposed to obtain the optimal solution of this optimization problem. Compared with the existing separate bandwidth or power optimal allocation algorithms, it is shown that the joint allocation algorithm improves both the total system capacity and the fairness among spot beams. Moreover, it is easy to be implemented in practice, as the computational complexity of the proposed algorithm is linear with the number of spot beams.
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Bhattacharya, Hindol, Samiran Chattopadhyay, Matangini Chattopadhyay, and Avishek Banerjee. "Storage and Bandwidth Optimized Reliable Distributed Data Allocation Algorithm." International Journal of Ambient Computing and Intelligence 10, no. 1 (2019): 78–95. http://dx.doi.org/10.4018/ijaci.2019010105.

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Distributed storage allocation problems are an important optimization problem in reliable distributed storage, which aims to minimize storage cost while maximizing error recovery probability by optimal storage of data in distributed storage nodes. A key characteristic of distributed storage is that data is stored in remote servers across a network. Thus, network resources especially communication links are an expensive and non-trivial resource which should be optimized as well. In this article, the authors present a simulation-based study of the network characteristics of a distributed storage network in the light of several allocation patterns. By varying the allocation patterns, the authors have demonstrated the interdependence between network bandwidth, defined in terms of link capacity and allocation pattern using network throughput as a metric. Motivated by observing the importance of network resource as an important cost metric, the authors have formalized an optimization problem that jointly minimizes both the storage cost and the cost of network resources. A hybrid meta heuristic algorithm is employed that solves this optimization problem by allocating data in a distributed storage system. Experimental results validate the efficacy of the algorithm.
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Wayer, Shahaf I., and Arie Reichman. "Resource Management in Satellite Communication Systems: Heuristic Schemes and Algorithms." Journal of Electrical and Computer Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/169026.

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The high cost of frequency bandwidth in satellite communication emphasizes the need for good algorithms to cope with the resource allocation problem. In systems using DVB-S2 links, the optimization of resource allocation may be related to the classical multi-knapsack problem. Resource management should be carried out according to the requests of subscribers, their priority levels, and assured bandwidths. A satisfaction measure is defined to estimate the allocation processes. Heuristic algorithms together with some innovative scaling schemes are presented and compared using Monte Carlo simulation based on a traffic model introduced here.
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Park, Jae Hyun, Doyle Kwon, and Duk Kyung Kim. "Resource Allocation for GBR Services in D2D-Enabled Communication." Electronics 9, no. 10 (2020): 1585. http://dx.doi.org/10.3390/electronics9101585.

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In device-to-device (D2D)-enabled communications, a channel can be matched to a cellular user (CU)–D2D user (DU) pair, where either two separate resources can be allocated in a dedicated mode, or a single resource is shared in a shared mode. It affects the channel capacity and the channel bandwidth needed to guarantee the target data rate. The transmit power also needs to be adaptively controlled according to channel bandwidth, as well as channel quality. In this paper, we propose a resource allocation scheme to minimize the total channel bandwidth while guaranteeing the target data rate of guaranteed bit rate services. The original problem is separated into two sub-problems; (1) the channel bandwidth calculation and channel sharing mode selection for arbitrary matching (2) the 3-D matching problem of the CU, DU and channel. To mitigate computational complexity, the 3-D matching problem is transformed into two 2-D matching problems, and a sub-optimal solution is derived using a sub-gradient algorithm. From numerical results, it was confirmed that the average channel bandwidth was almost the same even though the computational complexity of the sub-optimal algorithm was very low compared to the original algorithm. We also investigate the impact of various parameters, such as maximum D2D distance and the number of users.
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Zhang, Chongli, Tiejun Lv, Pingmu Huang, Zhipeng Lin, Jie Zeng, and Yuan Ren. "Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning." Sensors 23, no. 15 (2023): 6822. http://dx.doi.org/10.3390/s23156822.

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Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4–15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.
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Zhang, Xun, Kehao Wang, Xiaobai Li, Kezhong Liu, and Yirui Cong. "Joint Task Allocation and Resource Optimization Based on an Integrated Radar and Communication Multi-UAV System." Drones 7, no. 8 (2023): 523. http://dx.doi.org/10.3390/drones7080523.

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This paper investigates the joint task allocation and resource optimization problem in an integrated radar and communication multi-UAV (IRCU) system. Specifically, we assign reconnaissance UAVs and communication UAVs to perform the detection, tracking and communication tasks under the resource, priority and timing constraints by optimizing task allocation, power as well as channel bandwidth. Due to complex coupling among task allocation and resource optimization, the considered problem is proved to be non-convex. To solve the considered problem, we present a loop iterative optimization (LIO) algorithm to obtain the optimal solution. In fact, the mentioned problem is decomposed into three sub-problems, such as task allocation, power optimization and channel bandwidth optimization. At the same time, these three problems are solved by the divide-and-conquer algorithm, the successive convex approximation (SCA) algorithm and the improved particle swarm optimization (PSO) algorithm, respectively. Finally, numerical simulations demonstrate that the proposed LIO algorithm consumes fewer iterations or achieves higher maximum joint performance than other baseline schemes for solving the considered problem.
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Abu-Ein, Ashraf A., Waleed Abdelkarim Abuain, Mohannad Q. Alhafnawi, and Obaida M. Al-Hazaimeh. "Security Enhanced Dynamic Bandwidth Allocation-Based Reinforcement Learning." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 22 (November 5, 2024): 21–27. https://doi.org/10.37394/23209.2025.22.3.

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Recently, the problem of allocating bandwidth has arisen due to the limitation of bandwidth resources. Reinforcement learning is a good technique that can be used for improving throughput, and efficiency and minimizing the overall blocking of the network. To optimize performance metrics such as throughput and Quality of Service (e.g., QoS), this research employs Reinforcement Learning (e.g., RL) and models bandwidth allocation in networking as a Markov Decision Process (e.g., MDP). Interacting with the network and modifying rewards-based policies, the agent acquires the ability to allocate bandwidth efficiently using RL techniques like Q-learning. Resource management, quality of service (e.g., QoS), fairness, security, and privacy are among the challenges the approach addresses in Dynamic Bandwidth Allocation (e.g., DBA). This approach illustrates how RL can enhance network performance and decision-making across a variety of applications. The obtained results indicate that RL algorithms are more effective in enhancing network performance, Quality of Service (e.g., QoS), and user fairness.
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Dissertations / Theses on the topic "Bandwidth Allocation Problem"

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Rasmusson, Lars. "Network capacity sharing with QoS as a financial derivative pricing problem : algorithms and network." Doctoral thesis, SICS, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22556.

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A design of an automatic network capacity markets, often referred to as a bandwidth market, is presented. Three topics are investigated. First, a network model is proposed. The proposed model is based upon a trisection of the participant roles into network users, network owners, and market middlemen. The network capacity is defined in a way that allows it to be traded, and to have a well defined price. The network devices are modeled as core nodes, access nodes, and border nodes. Requirements on these are given. It is shown how their functionalities can be implemented in a network. Second, a simulated capacity market is presented, and a statistical method for estimating the price dynamics in the market is proposed. A method for pricing network services based on shared capacity is proposed, in which the price of a service is equivalent to that of a financial derivative contract on a number of simple capacity shares.Third, protocols for the interaction between the participants are proposed. The market participants need to commit to contracts with an auditable protocol with a small overhead. The proposed protocol is based on a public key infrastructure and on known protocols for multi party contract signing. The proposed model allows network capacity to be traded in a manner that utilizes the network efficiently. A new feature of this market model, compared to other network capacity markets, is that the prices are not controlled by the network owners. It is the end-users who, by middlemen, trade capacity among each other. Therefore, financial, rather than control theoretic, methods are used for the pricing of capacity.
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Wang, Chen. "Variants of Deterministic and Stochastic Nonlinear Optimization Problems." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112294/document.

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Les problèmes d’optimisation combinatoire sont généralement réputés NP-difficiles, donc il n’y a pas d’algorithmes efficaces pour les résoudre. Afin de trouver des solutions optimales locales ou réalisables, on utilise souvent des heuristiques ou des algorithmes approchés. Les dernières décennies ont vu naitre des méthodes approchées connues sous le nom de métaheuristiques, et qui permettent de trouver une solution approchées. Cette thèse propose de résoudre des problèmes d’optimisation déterministe et stochastique à l’aide de métaheuristiques. Nous avons particulièrement étudié la méthode de voisinage variable connue sous le nom de VNS. Nous avons choisi cet algorithme pour résoudre nos problèmes d’optimisation dans la mesure où VNS permet de trouver des solutions de bonne qualité dans un temps CPU raisonnable. Le premier problème que nous avons étudié dans le cadre de cette thèse est le problème déterministe de largeur de bande de matrices creuses. Il s’agit d’un problème combinatoire difficile, notre VNS a permis de trouver des solutions comparables à celles de la littérature en termes de qualité des résultats mais avec temps de calcul plus compétitif. Nous nous sommes intéressés dans un deuxième temps aux problèmes de réseaux mobiles appelés OFDMA-TDMA. Nous avons étudié le problème d’affectation de ressources dans ce type de réseaux, nous avons proposé deux modèles : Le premier modèle est un modèle déterministe qui permet de maximiser la bande passante du canal pour un réseau OFDMA à débit monodirectionnel appelé Uplink sous contraintes d’énergie utilisée par les utilisateurs et des contraintes d’affectation de porteuses. Pour ce problème, VNS donne de très bons résultats et des bornes de bonne qualité. Le deuxième modèle est un problème stochastique de réseaux OFDMA d’affectation de ressources multi-cellules. Pour résoudre ce problème, on utilise le problème déterministe équivalent auquel on applique la méthode VNS qui dans ce cas permet de trouver des solutions avec un saut de dualité très faible. Les problèmes d’allocation de ressources aussi bien dans les réseaux OFDMA ou dans d’autres domaines peuvent aussi être modélisés sous forme de problèmes d’optimisation bi-niveaux appelés aussi problèmes d’optimisation hiérarchique. Le dernier problème étudié dans le cadre de cette thèse porte sur les problèmes bi-niveaux stochastiques. Pour résoudre le problème lié à l’incertitude dans ce problème, nous avons utilisé l’optimisation robuste plus précisément l’approche appelée « distributionnellement robuste ». Cette approche donne de très bons résultats légèrement conservateurs notamment lorsque le nombre de variables du leader est très supérieur à celui du suiveur. Nos expérimentations ont confirmé l’efficacité de nos méthodes pour l’ensemble des problèmes étudiés<br>Combinatorial optimization problems are generally NP-hard problems, so they can only rely on heuristic or approximation algorithms to find a local optimum or a feasible solution. During the last decades, more general solving techniques have been proposed, namely metaheuristics which can be applied to many types of combinatorial optimization problems. This PhD thesis proposed to solve the deterministic and stochastic optimization problems with metaheuristics. We studied especially Variable Neighborhood Search (VNS) and choose this algorithm to solve our optimization problems since it is able to find satisfying approximated optimal solutions within a reasonable computation time. Our thesis starts with a relatively simple deterministic combinatorial optimization problem: Bandwidth Minimization Problem. The proposed VNS procedure offers an advantage in terms of CPU time compared to the literature. Then, we focus on resource allocation problems in OFDMA systems, and present two models. The first model aims at maximizing the total bandwidth channel capacity of an uplink OFDMA-TDMA network subject to user power and subcarrier assignment constraints while simultaneously scheduling users in time. For this problem, VNS gives tight bounds. The second model is stochastic resource allocation model for uplink wireless multi-cell OFDMA Networks. After transforming the original model into a deterministic one, the proposed VNS is applied on the deterministic model, and find near optimal solutions. Subsequently, several problems either in OFDMA systems or in many other topics in resource allocation can be modeled as hierarchy problems, e.g., bi-level optimization problems. Thus, we also study stochastic bi-level optimization problems, and use robust optimization framework to deal with uncertainty. The distributionally robust approach can obtain slight conservative solutions when the number of binary variables in the upper level is larger than the number of variables in the lower level. Our numerical results for all the problems studied in this thesis show the performance of our approaches
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Liaw, Guan-Hsiung, and 廖冠雄. "On the Bandwidth Allocation Problems for Connection-Oriented Communications in WDM-based Optical Networks." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/87434936221898789953.

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博士<br>國立清華大學<br>資訊工程學系<br>88<br>As more andmore development in the broadband services and multimedia applications, the bandwidth requirement continues to grow. Providing high-speed and high-capacity communication networks has become a significant subject. Owing to the progress of optical technologies in recent years, the optical networks have become the most potential candidate for future high-speed and high-capacity communications networks. Especially, the Wavelength-Division Multiplexing (WDM) based systems have received significant investigations. The bandwidth allocation problem is a significant research subject in WDM-based networks since the number of wavelength channels in a fiber is limited. In this dissertation, the bandwidth allocation problems for connection-oriented communications in three different kinds of WDM-based optical networks are investigated. The first subject is the isochronous bandwidth allocation problem on an interconnected WDM Network. A novel architecture named as Star Coupler Bridge (SCB) is proposed for interconnecting WDM-based LANs or MANs with slot-based bandwidth access scheme. The novel characteristic of SCB is its capability of merging and splitting the bandwidth of different isochronous connections into and from the same time-slots. This scheme will raise the bandwidth utilization and reduce the call blocking rate. The architecture and the operation of SCB is detailed. The isochronous bandwidth allocation problem is formally defined and its NP-hardness is proved. A heuristic algorithm is proposed for the isochronous bandwidth allocation problem. The simulation results demonstrates the effectiveness of merging and splitting by the proposed SCBs. In the second subject, a novel architecture of all-optical WDM-based transport network with time-shared wavelength channels is developed and the the corresponding bandwidth allocation problem is discussed. The network is composed by interconnecting the novel architecture named as Time-Wavelength-Space Router (TWSR). The major property of TWSR is that no complicated optical buffering and optical contention resolution schemes is exploited, but only the simplified time-slot synchronization and alignment components are adopted. This design compromises the merits and demerits of the conventional wavelength routers and optical packet switches. The bandwidth of each wavelength is accessed by cyclic TDM mode. The time-slot, route and wavelength assignment of each connection request must be decided before starting the communications. According this pre-determined assignment, the TWSRs tune their switching of the input/output ports to let the optical signal of the connections passes by. This bandwidth allocation problem is named as Routing, Wavelength and Time-slot Assignment (RWTA) problem. The RWTA problem is formally defined and a heuristic algorithm is proposed. The effectiveness of the proposed network is evaluated by comparing the simulation results with the ones of the traditional wavelength-routed networks. The third subject is the node placement problem in WDM-based multihop local network. The network consists of a passive star-coupler and the attached communication nodes. Each communication node is equipped with fixed wavelength transmitters and fixed wavelength receivers. The wavelengths and the communication nodes compose the virtual topology which is independent of the physical star structure of the network. The node placement problem is defined as to place each node on a appropriate vertex of the given virtual topology under the condition of given amount of traffic between each pair of nodes. Double Fixed-step Loop Network (DFLN) is used as the virtual topology. The mathematical model of the node placement problem is formally defined. In addition, the post-optimization problem occurring when the objective values become non-optimal as the traffic has been changed is also investigated. The heuristic algorithms for these problems are proposed and the effectiveness is demonstrated by simulation results.
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Book chapters on the topic "Bandwidth Allocation Problem"

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Confessore, Giuseppe, Paolo Dell’Olmo, and Stefano Giordani. "An Approximation Result for a Bandwidth Allocation Problem." In Operations Research Proceedings. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58891-4_19.

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Marcinkowski, Bartosz, and Piotr Ostrowski. "The Problem of Bandwidth Allocation in the Business Activity of Service Providers: Comparison and Analysis of Costs." In Computer Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_37.

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Hesselbach, Xavier, Christos Kolias, Ramón Fabregat, Mónica Huerta, and Yezid Donoso. "Problems in Dynamic Bandwidth Allocation in Connection Oriented Networks." In Texts in Theoretical Computer Science. An EATCS Series. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02250-0_7.

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Leonardi, Stefano, Alberto Marchetti-Spaccamela, and Andrea Vitaletti. "Approximation Algorithms for Bandwidth and Storage Allocation Problems under Real Time Constraints." In FST TCS 2000: Foundations of Software Technology and Theoretical Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44450-5_33.

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Tsiropoulou, Eirini Eleni, Panagiotis Vamvakas, and Symeon Papavassiliou. "Resource Allocation in Multi-Tier Femtocell and Visible-Light Heterogeneous Wireless Networks." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2023-8.ch010.

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The increasing demand in mobile data traffic, data hungry services and high QoS prerequisites have led to the design of advanced multi-tier heterogeneous cellular networks. In this chapter, a multi-tier heterogeneous wireless network is examined consisting of the macrocell, multiple femtocells and multiple Visible Light Communication (VLC) cells. Distributed resource allocation approaches in two-tier femtocells are presented focusing on (a) power allocation and interference management, (b) joint power and rate allocation, and (c) resource allocation and pricing policies. Similarly, the most prominent resource allocation approaches in two-tier VLC cells are examined, including (a) user association and adaptive bandwidth allocation, (b) joint bandwidth and power allocation, and (c) interference bounded resource blocks allocation and power control. The resource allocation problem in the two-tier heterogeneous environment where both femtocells and VLC-LANs are simultaneously present is also discussed. Finally, detailed future directions and comprehensive conclusions are provided.
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Wu Chien-Yu, Ho Hann-Jang, and Lee Sing-Ling. "Optimization of Downlink Bandwidth Allocation for Energy Efficiency over OFDMA Wireless Networks." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-484-8-345.

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For the multicast channel assignment issue with multi-layer, this paper studies a resource allocation problem for minimizing the energy consumption of subscriber stations in Orthogonal Frequency Division Multiplexing Access (OFDMA) wireless systems. Not only does the requirement have to be guaranteed when multicast users receive video content in layer-based video coding, but also the energy consumption needs to be minimized. In this paper, the energy consumption of each station is optimized by minimizing the number of active symbols. The scheduling process is divided into two phases. First, the number of requested tiles is minimized by proposed scheme Layer Determination Scheme with SVC (LDS) for the issue of optimizing energy consumption. The proposed LDS scheme determines the modulation of the selected layer and assigns available tiles to the selected layer. In the secondary phase, the symbol scheduling problem is minimizing the number of active symbols for each station. The Complementary Matching Algorithm (CMA) is proposed to minimize the total number of active symbols in an OFDMA wireless system. The problems of symbol scheduling are reduced to the subset sum optimization problem to find the complementary subset of a request. In the simulation results for the proposed LDS and CMA algorithms, we found that the solutions generated by our algortihms have better performance in terms of energy consumption and throughput.
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Abdelkhalek Ons, Krichen Saoussen, and Guitouni Adel. "Location-Allocation Planning of Heterogeneous Networks for Maritime Surveillance Applications." In NATO Science for Peace and Security Series - E: Human and Societal Dynamics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-201-1-228.

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In this paper, we address a network management problem, and propose scenarios to generate compromise solutions. The problem of location-allocation planning of heterogeneous networks can be defined by a set of agents, a set of communication devices, and a set of existing networks infrastructure. The goal is to find the optimal number, position, communication types and connections of agents in a special area of coverage. The problem is modelled as a multi-objective and multi-constraint problem with three conflicting objective functions: maximizing communication coverage, minimizing node placement with communication device cost, and maximizing the total capacity (bandwidth) of the network. To solve the problem, we apply a Multi-objective Genetic Algorithm (MOGA). The model is empirically validated in the simulation environment &amp;ldquo;Inform Lab,&amp;rdquo; and is implemented in a maritime surveillance application with real data instances.
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Umar, Raza, and Wessam Mesbah. "Throughput-Efficient Spectrum Access in Cognitive Radio Networks." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6571-2.ch017.

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Cognitive radio based on dynamic spectrum access has emerged as a promising technology to meet the insatiable demand for radio spectrum by the emerging wireless applications. In this chapter, the authors address the problem of throughput-efficient spectrum access in Cognitive Radio Networks (CRNs) using Coalitional Game-theoretic framework. They model the problem of joint Coalition Formation (CF) and Bandwidth (BW) allocation as a CF game in partition form with non-transferable utility and present a variety of algorithms to dynamically share the available spectrum resources among competing Secondary Users (SUs). First, the authors present a centralized solution to reach a sum-rate maximizing Nash-stable network partition. Next, a distributed CF algorithm is developed through which SUs may join/leave a coalition based on their individual preferences. Performance analysis shows that the CF algorithms with optimal BW allocation provides a substantial gain in the network throughput over existing coalition formation techniques as well as the simple cases of singleton and grand coalition.
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Banik, Abira, and Abhishek Majumder. "Classification of Channel Allocation Schemes in Wireless Mesh Network." In Algorithms, Methods, and Applications in Mobile Computing and Communications. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5693-0.ch004.

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Wireless mesh network (WMN) is a widely accepted network topology due to its implementation convenience, low cost nature, and immense adaptability in real-time scenarios. The components of the network are gateways, mesh routers, access points, and end users. The components in mesh topology have a dedicated line of communication with a half-duplex radio. The wireless mesh network is basically implemented in IEEE 802.11 standard, and it is typically ad-hoc in nature. The advantageous nature of WMN leads to its extensive use in today's world. WMN's overall performance has been increased by incorporating the concept of multi-channel multi-radio. This gives rise to the problem of channel assignment for maximum utilization of the available bandwidth. In this chapter, the factors affecting the channel assignment process have been presented. Categorizations of the channel assignment techniques are also illustrated. Channel assignment techniques have also been compared.
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"IoT System Resource Sharing Mechanisms." In Advances in Web Technologies and Engineering. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1952-2.ch003.

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As the IoT technology continues to grow, it needs to support an increasing range of services. Therefore, IoT networking over which services are provided has become an area of great importance. In particular, the management of IoT resources and the way new technology integrates into the network operator's infrastructure is critical to the success of IoT. The key to supporting a large number of services is IoT system resource. Therefore, all performance guarantees in IoT systems are conditional on currently available resource capacity. In this chapter, we focus our attention on the IoT resource allocation problem. First, an effective bandwidth allocation algorithm for heterogeneous networks is introduced. And then, a new Bitcoin mining protocol with the incentive payment process is explained. To share the computation resource, this Bitcoin protocol adopts the concept of the group bargaining solution by considering a peer-to-peer relationship.
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Conference papers on the topic "Bandwidth Allocation Problem"

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Chakaravarthy, Venkatesan T., Vinayaka Pandit, Yogish Sabharwal, and Deva P. Seetharam. "Varying bandwidth resource allocation problem with bag constraints." In 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). IEEE, 2010. http://dx.doi.org/10.1109/ipdps.2010.5470347.

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Gao, Bo, Ligang He, and Chao Chen. "Modelling the Bandwidth Allocation Problem in Mobile Service-Oriented Networks." In MSWiM'15: 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, 2015. http://dx.doi.org/10.1145/2811587.2811628.

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Al-Zayadi, Haider. "The problem of downlink channel bandwidth capacity allocation in LTE technology." In 2016 13th International Conference on Modern Problems of Radio Engineering. Telecommunications and Computer Science (TCSET). IEEE, 2016. http://dx.doi.org/10.1109/tcset.2016.7452231.

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Botero, Juan Felipe, and Xavier Hesselbach. "The bottlenecked virtual network problem in bandwidth allocation for network virtualization." In 2009 IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2009. http://dx.doi.org/10.1109/latincom.2009.5305042.

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Ding, Boping, and Xiang Yu. "UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation." In 4th International Conference on Natural Language Processing, Information Retrieval and AI. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130304.

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With the development of UAV technology, using UAV as a base station in the air can quickly restore vehicle communications after disasters. In order to reduce the delay and maximize the rational use of bandwidth and power, this paper applies TDMA technology to UAV communication network, and proposes a joint optimization allocation strategy of bandwidth and power. First of all, a deep learning network needs to be trained. The use of deep learning can improve the accuracy of prediction. The reward mechanism is set through the change of delay. The purpose of training is to enable the UAV to choose the optimal bandwidth allocation coefficient under the dynamic change of the environment. Then, a joint optimization strategy is proposed to set the SNR threshold to ensure the communication quality. The user's transmission rate is calculated according to the Shannon formula, Finally, the scheme with minimum delay is selected as the final bandwidth and power allocation value. In the simulation experiment, compared with the previous traditional algorithm, the network performance has been further improved in terms of reducing delay and energy consumption, and what needs to be improved may be the problem of computation.
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Bayless, Sam, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos, and Alan J. Hu. "Scalable Constraint-based Virtual Data Center Allocation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/77.

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Constraint-based techniques can solve challenging problems arising from highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To solve this problem, we introduce NETSOLVER, which is based on the general-purpose constraint solver MONOSAT. NETSOLVER represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. NETSOLVER scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, NETSOLVER can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Essential to our solution efficiency is our formulation of VDC allocation using monotonic theories, illustrating the practical value of the recently proposed SAT modulo monotonic theories approach.
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Lu, Jianfeng, Yue Chen, Shuqin Cao, Longbiao Chen, Wei Wang, and Yun Xin. "LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/515.

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Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to alleviate communication burdens, its model performance will be degraded by non-IID data and limited communication resources. Current works often assume that data is uniformly distributed, which however contradicts the heterogeneity of IoT. Solutions involving additional model training to check the data distribution inevitably increase computational costs and the risk of privacy leakage. The challenges in solving these issues are how to reduce the impact of non-IID data without involving raw data, and how to rationalize the communication resource allocation for addressing straggler problem. To tackle these challenges, we propose a novel optimization method based on coaLition formation gamE and grAdient Projection, called LEAP. Specifically, we combine edge data distribution with coalition formation game innovatively to adjust the correlations between clients and ESs dynamically, ensuring optimal correlations. We further capture the client heterogeneity to achieve the rational bandwidth allocation from coalition perception and determine the optimal transmission power within specified delay constraints at the client level. Experimental results on four real datasets show that LEAP is able to achieve 20.62% improvement in model accuracy compared to the state-of-the-art baselines. Moreover, LEAP effectively reduces transmission energy consumption by at least about 2.24 times.
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Gabriel Gussen, Camila Maria, Elena Veronica Belmega, and Merouane Debbah. "Pricing and bandwidth allocation problems in wireless multi-tier networks." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190295.

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Yifei Yuan, Anduo Wang, Rajeev Alur, and Boon Thau Loo. "On the feasibility of automation for bandwidth allocation problems in data centers." In 2013 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2013. http://dx.doi.org/10.1109/fmcad.2013.6679389.

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Mohammed Khodayer Al-Dulaimi, Aymen. "Linear model of bandwidth allocation in LTE downlink with RAT 1." In 2015 Second International Scientific-Practical Conference. Problems of Infocommunications Science and Technology (PIC S&T). IEEE, 2015. http://dx.doi.org/10.1109/infocommst.2015.7357276.

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