Готові списки джерел за темами / Approximation algorithms; resource allocation; optimization

Добірка наукової літератури з теми "Approximation algorithms; resource allocation; optimization"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Approximation algorithms; resource allocation; optimization"

Du, Ning, Changqing Zhou, and Xiyuan Ma. "A Novel Subchannel and Power Allocation Algorithm in V2V Communication." Wireless Communications and Mobile Computing 2021 (October 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/5530612.

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This paper investigates resource allocation of latency constrained vehicle-to-vehicle (V2V) communication. When a subchannel of a vehicle-to-infrastructure (V2I) link can be reused by multiple V2V links, a nonlinear mixed integer optimization problem with the goal of maximizing the spectral efficiency of the system is derived under the constraints of minimum transmission rate of V2I links and transmission latency of V2V links. The subchannel allocation problem is solved by means of two-sided exchange matching theory, optimal transmission power of V2I and V2V links is solved based on the poly-block approximation (PBA) algorithm, and the system spectrum efficiency is maximized through loop iteration. In order to reduce the computational complexity of power allocation problem, a power allocation algorithm based on iterative convex optimization (ICO) is proposed. The convergence of the resource allocation algorithm is also proved. The simulation results show that the proposed algorithms can guarantee transmission latency requirements of V2V links and improve the system sum rate and access ratio of V2V links. Compared with two traditional algorithms, latency of poly-block approximation combined with many to one matching (PBAMTO) is reduced by 30.41% and 20.43%, respectively.

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Du, Ning, Kaishi Sun, Changqing Zhou, and Xiyuan Ma. "A Novel Access Control and Energy-Saving Resource Allocation Scheme for D2D Communication in 5G Networks." Complexity 2020 (January 8, 2020): 1–11. http://dx.doi.org/10.1155/2020/3696015.

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This paper investigates access link control and resource allocation for the device-to-device (D2D) communication in the fifth generation (5G) cellular networks. The optimization objective of this problem is to maximize the number of admitted D2D links and minimize the total power consumption of D2D links under the condition of meeting the minimum transmission rate requirements of D2D links and common cellular links. This problem is a two-stage nondeterministic polynomial (NP) problem, the solving process of which is very complex. So, we transform it into a one-stage optimization problem. According to the monotonicity of objective function and constraint conditions, a monotone optimization problem is established, which is solved by reverse polyblock approximation algorithm. In order to reduce the complexity of this algorithm, a solution algorithm based on iterative convex optimization is proposed. Simulation results show that both algorithms can maximize the number of admitted D2D links and minimize the total power consumption of D2D links. The proposed two algorithms are better than the energy efficiency optimization algorithm.

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Hameed, Iqra, Pham-Viet Tuan, and Insoo Koo. "Exploiting a Deep Neural Network for Efficient Transmit Power Minimization in a Wireless Powered Communication Network." Applied Sciences 10, no. 13 (July 3, 2020): 4622. http://dx.doi.org/10.3390/app10134622.

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In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

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Li, Huanyu, Hui Li, and Youling Zhou. "Optimization Algorithms for Joint Power and Sub-Channel Allocation for NOMA-Based Maritime Communications." Entropy 23, no. 11 (November 1, 2021): 1454. http://dx.doi.org/10.3390/e23111454.

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This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the South China Sea, we first establish a Longley–Rice-based channel model. Then, the weighted achievable rate (WAR) is considered as the optimization objective to weigh the information rate and user fairness effectively. Our work introduces an improved joint power and user allocation scheme (RBPUA) based on a single resource block. Taking RBPUA as a basic module, we propose three joint multi-subchannel power and marine user allocation algorithms. The gradient descent algorithm (GRAD) is used as the reference standard for WAR optimization. The multi-choice knapsack algorithm combined with dynamic programming (MCKP-DP) obtains a WAR optimization result almost equal to that of GRAD. These two NOMA-based solutions are able to improve WAR performance by 7.47% compared with OMA. Due to the high computational complexity of the MCKP-DP, we further propose a DP-based fully polynomial-time approximation algorithm (DP-FPTA). The simulation results show that DP-FPTA can reduce the complexity by 84.3% while achieving an approximate optimized performance of 99.55%. This advantage of realizing the trade-off between performance optimization and complexity meets the requirements of practical low-latency systems.

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Yu, Bencheng, Zihui Ren, and Shoufeng Tang. "Robust Secure Resource Allocation for RIS-Aided SWIPT Communication Systems." Sensors 22, no. 21 (October 28, 2022): 8274. http://dx.doi.org/10.3390/s22218274.

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Aiming at the influence of channel uncertainty, user information leakage and harvested energy improvement, this paper proposes a robust resource allocation algorithm for reconfigurable intelligent reflector (RIS) multiple-input single-output systems based on imperfect channel state information. First, considering the legal user minimum secret rate constraint, the base station maximum transmit power constraint and the RIS phase shift constraint with the bounded channel uncertainty, a joint optimization of the base station active beam, energy beam and RIS phase shift is established. A multivariate coupled nonlinear resource allocation problem for matrices is addressed. Then, using S-procedure and alternating optimization methods, the original non-convex problem is transformed into a deterministic convex optimization problem and an alternating optimization algorithm based on continuous convex approximation is proposed. The simulation results show that the proposed algorithm has better fairness harvested energy compared with the traditional robust algorithm.

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Han, Qinghua, Minghai Pan, Weijun Long, Zhiheng Liang, and Chenggang Shan. "Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System." Sensors 20, no. 4 (February 12, 2020): 981. http://dx.doi.org/10.3390/s20040981.

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In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.

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Yang, Xiaoxia, Zhengqiang Wang, Xiaoyu Wan, and Zifu Fan. "Secure Energy-Efficient Resource Allocation Algorithm of Massive MIMO System with SWIPT." Electronics 9, no. 1 (December 25, 2019): 26. http://dx.doi.org/10.3390/electronics9010026.

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In this paper, we consider the resource allocation problem to maximize the minimum (max–min) user’s secure energy efficiency (SEE) in downlink massive multiple-input multiple-output (MIMO) systems with simultaneous wireless information and power transfer (SWIPT). First, transmission power and power splitting ratio are designed to achieve the max–min user’s SEE subject to harvested energy threshold, the constraints of transmission power, and power splitting ratio. Secondly, the optimization problem is non-convex and very difficult to tackle. In order to solve the optimization problem, we converted to a series of parameter optimization subproblems by fractional programming. Then, we employ the first-order Taylor expansion and successive convex approximation (SCA) method to solve parameter optimization problems. Next, a secure energy-efficient resource allocation (SERA) algorithm with the bisection method is proposed to find the max–min SEE of the system. Finally, simulation results show the effectiveness and superiority of the SERA algorithm.

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Yu, Guanding, Xin Ding, and Shengli Liu. "Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network." Sensors 22, no. 24 (December 13, 2022): 9763. http://dx.doi.org/10.3390/s22249763.

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Due to the lack of places to employ communication infrastructures, there are many coverage blind zones in maritime communication networks. Benefiting from the high flexibility and maneuverability, unmanned aerial vehicles (UAVs) have been proposed as a promising method to provide broadband maritime coverage for these blind zones. In this paper, a multi-UAV-enabled maritime communication model is proposed, where UAVs are deployed to provide the transmission service for maritime users. To improve the performance of the maritime communication systems, an optimization problem is formulated to maximize the minimum average throughput among all users by jointly optimizing the user association, power allocation, and UAV trajectory. To derive the solutions with a low computational complexity, we decompose this problem into three subproblems, namely user association optimization, power allocation optimization, and UAV trajectory optimization. Then, a joint iterative algorithm is developed to achieve the solutions based on the successive convex approximation and interior-point methods. Extensive simulation results validate the effectiveness of the proposed algorithm and demonstrate that UAVs can be used to enhance the maritime coverage.

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An, Qi, Yu Pan, Huizhu Han, and Hang Hu. "Secrecy Capacity Maximization of UAV-Enabled Relaying Systems with 3D Trajectory Design and Resource Allocation." Sensors 22, no. 12 (June 15, 2022): 4519. http://dx.doi.org/10.3390/s22124519.

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Unmanned aerial vehicles (UAVs) have attracted considerable attention, thanks to their high flexibility, on-demand deployment and the freedom in trajectory design. The communication channel quality can be effectively improved by using UAV to build a line-of-sight communication link between the transmitter and the receiver. Furthermore, there is increasing demand for communication security improvement, as the openness of a wireless channel brings serious threat. This paper formulates a secrecy capacity optimization problem of a UAV-enabled relay communication system in the presence of malicious eavesdroppers, in which the secrecy capacity is maximized by jointly optimizing the UAV relay’s location, power allocation, and bandwidth allocation under the communication quality and information causality constraints. A successive convex approximation–alternative iterative optimization (SCA-AIO) algorithm is proposed to solve this highly coupled nonconvex problem. Simulation results demonstrate the superiority of the proposed secrecy transmission strategy with optimal trajectory design and resource allocation compared with the benchmark schemes and reveal the impacts of communication resources on system performance.

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Bu, Yinglan, Jiaying Zong, Xinjiang Xia, Yang Liu, Fengyi Yang, and Dongming Wang. "Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers." Electronics 11, no. 12 (June 9, 2022): 1836. http://dx.doi.org/10.3390/electronics11121836.

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Compared with the traditional collocated multi-input multi-output system (C-MIMO), distributed MIMO (D-MIMO) systems have the advantage of higher throughput and coverage, making them strong candidates for next-generation communication architecture. As a practical implementation of a D-MIMO cooperative network, the multi-TRP (multiple transmission/reception point) system becomes a hotspot in the research of advanced 5G. Different from previous research on a cooperative D-MIMO network with single narrowband transmission, this paper proposes a joint optimization scheme to address the user scheduling problem along with carrier allocation to maximize the total spectral efficiency (SE) in the downlink of coherent multi-TRP systems with multi-carriers. We establish a joint optimization model of user scheduling and resource allocation to maximize the system spectral efficiency under the constraints of power consumption and the backhaul capacity limits at each RAU (remote antenna unit), as well as the QoS (quality of service) requirement at each user. Since the optimization model is both non-covex and non-smooth, a joint optimization algorithm is proposed to solve this non-convex combinatorial optimization problem. We first smooth the mixed-integer problem by employing penalty functions, and after decoupling the coupled variables by introducing auxiliary variables, the original problem is transformed into a series of tractable convex optimization problems by using successive convex approximation (SCA). Numerical results demonstrate that the proposed joint optimization algorithm for user scheduling and resource allocation can reliably converge and achieve a higher system SE than the general multi-TRP system without carrier allocation, and this advantage is more pronounced under a higher backhaul capacity or higher power consumption constraints.

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Дисертації з теми "Approximation algorithms; resource allocation; optimization"

Chakrabarty, Deeparnab. "Algorithmic aspects of connectivity, allocation and design problems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24659.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2008.
Committee Chair: Vazirani, Vijay; Committee Member: Cook, William; Committee Member: Kalai, Adam; Committee Member: Tetali, Prasad; Committee Member: Thomas, Robin

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Tripathi, Pushkar. "Allocation problems with partial information." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44789.

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Allocation problems have been central to the development of the theory of algorithms and also find applications in several realms of computer science and economics. In this thesis we initiate a systematic study of these problems in situations with limited information. Towards this end we explore several modes by which data may be obfuscated from the algorithm. We begin by investigating temporal constraints where data is revealed to the algorithm over time. Concretely, we consider the online bipartite matching problem in the unknown distribution model and present the first algorithm that breaches the 1-1/e barrier for this problem. Next we study issues arising from data acquisition costs that are prevalent in ad-systems and kidney exchanges. Motivated by these constraints we introduce the query-commit model and present constant factor algorithms for the maximum matching and the adwords problem in this model. Finally we assess the approximability of several classical allocation problems with multiple agents having complex non-linear cost functions. This presents an additional obstacle since the support for the cost functions may be extremely large entailing oracle access. We show tight information theoretic lower bounds for the general class of submodular functions and also extend these results to get lower bounds for a subclass of succinctly representable non-linear cost functions.

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Kibria, Mirza Golam. "Radio Resource Allocation Optimization for Cellular Wireless Networks." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/189689.

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Bayrak, Ahmet Engin. "Optimization Algorithms For Resource Allocation Problem Of Air Tasking Order Preparation." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612325/index.pdf.

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In recent years, evolving technology has provided a wide range of resources for Military forces. However, that wideness also caused resource management difficulties in combat missions. Air Tasking Order (ATO) is prepared for various missions of air combats in order to reach objectives by an optimized resource management. Considering combinatorial military aspects with dynamic objectives and various constraints
computer support became inevitable for optimizing the resource management in air force operations. In this thesis, we study different optimization approaches for resource allocation problem of ATO preparation and analyze their performance. We proposed a genetic algorithm formulation with customized encoding, crossover and fitness calculation mechanisms by using the domain knowledge. A linear programming formulation of the problem is developed by integer decision variables and it is used for effectivity and efficiency analysis of genetic algorithm formulations.

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Salazar, Javier. "Resource allocation optimization algorithms for infrastructure as a service in cloud computing." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB074.

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L’informatique, le stockage des données et les applications à la demande font partie des services offerts par l’architecture informatique en Nuage. Dans ce cadre, les fournisseurs de nuage (FN) agissent non seulement en tant qu’administrateurs des ressources d'infrastructure mais ils profitent aussi financièrement de la location de ces ressources. Dans cette thèse, nous proposons trois modèles d'optimisation du processus d'allocation des ressources dans le nuage dans le but de réduire les coûts générés et d’accroitre la qualité du service rendu. Cela peut être accompli en fournissant au FN les outils formels nécessaires pour réduire au minimum le prix des ressources dédiées à servir les requêtes des utilisateurs. Ainsi, la mise en œuvre des modèles proposés permettra non seulement l’augmentation des revenus du FN, mais aussi l’amélioration de la qualité des services offerts, ce qui enrichira l’ensemble des interactions qui se produisent dans le nuage. A cet effet, nous nous concentrons principalement sur les ressources de l’infrastructure en tant que service (IaaS), lesquels sont contenus dans des centres de données (DCs), et constituent l'infrastructure physique du nuage. Comme une alternative aux immenses DCs centralisés, la recherche dans ce domaine comprend l’installation de petits centres de données (Edge DCs) placés à proximité des utilisateurs finaux. Dans ce contexte nous adressons le problème d’allocation des ressources et pour ce faire nous utilisons la technique d'optimisation nommée génération de colonnes. Cette technique nous permet de traiter des modèles d'optimisation à grande échelle de manière efficace. La formulation proposée comprend à la fois, et dans une seule phase, les communications et les ressources informatiques à optimiser dans le but de servir les requêtes de service d'infrastructure. Sur la base de cette formulation, nous proposons également un deuxième modèle qui comprend des garanties de qualité de service toujours sous la même perspective d'allocation des ressources d’infrastructure en tant que service. Ceci nous permet de fournir plusieurs solutions applicables à divers aspects du même problème, tels que le coût et la réduction des délais, tout en offrant différents niveaux de service. En outre, nous introduisons le scénario informatique en nuage multimédia, qui, conjointement avec l'architecture des Edge DCs, résulte en l'architecture Multimédia Edge Cloud (MEC). Dans ce cadre, nous proposons une nouvelle approche pour l'allocation des ressources dans les architectures informatique en nuage multimédia lors du positionnement de ces DCs afin de réduire les problèmes liés à la communication, tels que la latence et la gigue. Dans cette formulation, nous proposons également de mettre en œuvre des technologies optiques de réseau de fibres pour améliorer les communications entre les DCs. Plusieurs travaux ont proposé de nouvelles méthodes pour améliorer la performance et la transmission de données. Dans nos travaux, nous avons décidé de mettre en œuvre le multiplexage en longueur d'onde (WDM) pour renforcer l'utilisation des liens et les chemins optiques dans le but de grouper différents signaux sur la même longueur d'onde. Un environnement de simulation réel est également présenté pour l’évaluation des performances et de l'efficacité des approches proposées. Pour ce faire, nous utilisons le scénario spécifié pour les DCs, et nous comparons par simulation nos modèles au moyen de différents critères de performances tel que l'impact de la formulation optique sur la performance du réseau. Les résultats numériques obtenus ont montré que, en utilisant nos modèles, le FN peut efficacement réduire les coûts d'allocation en maintenant toujours un niveau satisfaisant quant à l'acceptation de requêtes et la qualité du service
The cloud architecture offers on-demand computing, storage and applications. Within this structure, Cloud Providers (CPs) not only administer infrastructure resources but also directly benefit from leasing them. In this thesis, we propose three optimization models to assist CPs reduce the costs incurred in the resource allocation process when serving users’ demands. Implementing the proposed models will not only increase the CP’s revenue but will also enhance the quality of the services offered, benefiting all parties. We focus on Infrastructure as a Service (IaaS) resources which constitute the physical infrastructure of the cloud and are contained in datacenters (DCs). Following existing research in DC design and cloud computing applications, we propose the implementation of smaller DCs (Edge DCs) be located close to end users as an alternative to large centralized DCs. Lastly, we use the Column Generation optimization technique to handle large scale optimization models efficiently. The proposed formulation optimizes both the communications and information technology resources in a single phase to serve IaaS requests. Based on this formulation, we also propose a second model that includes QoS guarantees under the same Infrastructure as a Service resource allocation perspective, to provide different solutions to diverse aspects of the resource allocation problem such as cost and delay reduction while providing different levels of service. Additionally, we consider the multimedia cloud computing scenario. When Edge DCs architecture is applied to this scenario it results in the creation of the Multimedia Edge Cloud (MEC) architecture. In this context we propose a resource allocation approach to help with the placement of these DCs to reduce communication related problems such as jitter and latency. We also propose the implementation of optical fiber network technologies to enhance communication between DCs. Several studies can be found proposing new methods to improve data transmission and performance. For this study, we decided to implement Wavelength Division Multiplexing (WDM) to strengthen the link usage and light-paths and, by doing so, group different signals over the same wavelength. Using a realistic simulation environment, we evaluate the efficiency of the approaches proposed in this thesis using a scenario specifically designed for the DCs, comparing them with different benchmarks and also simulating the effect of the optical formulation on the network performance. The numerical results obtained show that by using the proposed models, a CP can efficiently reduce allocation costs while maintaining satisfactory request acceptance and QoS ratios

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Ali, Syed Hussain. "Cross layer scheduling and resource allocation algorithms for cellular wireless networks." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2722.

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This thesis considers the problem of cross layer scheduling and radio resource allocation of multiple users in the downlink of time-slotted and frequency-slotted cellular data networks. For these networks, opportunistic scheduling algorithms improve system performance by exploiting time variations of the radio channel. Within the broader framework of opportunistic scheduling, this thesis solves three distinct problems and proposes efficient and scalable solutions for them. First, we present novel optimal and approximate opportunistic scheduling algorithms that combine channel fluctuation and user mobility information in their decision rules. The algorithms propose the use of dynamic fairness constraints. These fairness constraints adapt according to the user mobility. The optimal algorithm is an off-line algorithm that precomputes constraint values according to a known mobility model. The approximate algorithm is an on-line algorithm that relies on the future prediction of the user mobility locations in time. We show that the use of mobility information increases channel capacity. We also provide analytical bounds on the performance of the approximate algorithm. Second, this thesis presents a new opportunistic scheduling solution that maximizes the aggregate user performance subject to certain minimum and maximum performance constraints. By constraining the performance experienced by individual users, who share a common radio downlink, to some upper bounds, it is possible to provide the system operator with a better control of radio resource allocations and service differentiation among different classes of users. The proposed solution offers better performance than existing solution under practical channel conditions. Finally, we present a dynamic subcarrier allocation solution for fractional frequency reuse in multicell orthogonal frequency division multiple access systems. We formulate the subcarrier allocation as an equivalent set partitioning problem and then propose an efficient hierarchical solution which first partitions subcarriers into groups and next schedules subcarriers opportunistically to users. Simulation results for three solutions illustrate the usefulness of the proposed schemes.

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Shashika, Manosha Kapuruhamy Badalge (). "Convex optimization based resource allocation in multi-antenna systems." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526217499.

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Abstract The use of multiple antennas is a fundamental requirement in future wireless networks as it helps to increase the reliability and spectral efficiency of mobile radio links. In this thesis, we study convex optimization based radio resource allocation methods for the downlink of multi-antenna systems. First, the problem of admission control in the downlink of a multicell multiple-input single-output (MISO) system has been considered. The objective is to maximize the number of admitted users subject to a signal-to-interference-plus-noise ratio (SINR) constraint at each admitted user and a transmit power constraint at each base station (BS). We have cast the admission control problem as an ℓ0 minimization problem; it is known to be combinatorial, NP-hard. Centralized and distributed algorithms to solve this problem have been proposed. To develop the centralized algorithm, we have used sequential convex programming (SCP). The distributed algorithm has been derived by using the consensus-based alternating direction method of multipliers in conjunction with SCP. We have shown numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we have extended the admission control problem to provide fairness, where long-term fairness among the users has been guaranteed. We have focused on proportional and max-min fairness, and proposed dynamic control algorithms via Lyapunov optimization. Results show that these proposed algorithms guarantee fairness. Then, the problem of admission control for the downlink of a MISO heterogeneous networks (hetnet) has been considered, and the proposed centralized and distributed algorithms have been adapted to find a solution. Numerically, we have illustrated that the centralized algorithm achieves a near-to-optimal performance, and the distributed algorithm’s performance is closer to the optimal value. Finally, an algorithm to obtain the set of all achievable power-rate tuples for a multiple-input multiple-output hetnet has been provided. The setup consists of a single macrocell and a set of femtocells. The interference power to the macro users from the femto BSs has been kept below a threshold. To find the set of all achievable power-rate tuples, a two-dimensional vector optimization problem is formulated, where we have considered maximizing the sum-rate while minimizing the sum-power, subject to maximum power and interference threshold constraints. This problem is known to be NP-hard. A solution method is provided by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. The proposed algorithm was used to evaluate the impact of imposing interference threshold constraints and the co-channel deployments in a hetnet
Tiivistelmä Monen antennin käyttö on perusvaatimus tulevissa langattomissa verkoissa, koska se auttaa lisäämään matkaviestinyhteyksien luotettavuutta ja spektritehokkuutta. Tässä väitöskirjassa tutkitaan konveksiin optimointiin perustuvia radioresurssien allokointimenetelmiä moniantennijärjestelmien alalinkin suunnassa. Ensiksi on käsitelty pääsynvalvonnan ongelmaa alalinkin suuntaan monen solun moni-tulo yksi-lähtö (MISO) -verkoissa. Tavoitteena on maksimoida hyväksyttyjen käyttäjien määrä, kun hyväksytyille käyttäjille on asetettu signaali-häiriö-kohinasuhteen (SINR) rajoitus, ja tukiasemille lähetystehon rajoitus. Pääsynvalvonnan ongelma on muotoiltu ℓ0-minimointiongelmana, jonka tiedetään olevan kombinatorinen, NP-vaikea ongelma. Ongelman ratkaisemiseksi on ehdotettu keskitettyjä ja hajautettuja algoritmeja. Keskitetty optimointialgoritmi perustuu sekventiaaliseen konveksiin optimointiin. Hajautettu algoritmi pohjautuu konsensusoptimointimenetelmään ja sekventiaaliseen konveksiin optimointiin. Ehdotettujen pääsynvalvonta-algoritmien on numeerisesti osoitettu saavuttavan lähes optimaalinen suorituskyky. Lisäksi pääsynvalvontaongelma on laajennettu takaamaan pitkän aikavälin oikeudenmukaisuus käyttäjien välillä. Työssä käytetään erilaisia määritelmiä oikeudenmukaisuuden takaamiseen, ja ehdotetaan dynaamisia algoritmeja pohjautuen Lyapunov-optimointiin. Tulokset osoittavat, että ehdotetuilla algoritmeilla taataan käyttäjien välinen oikeudenmukaisuus. Tämän jälkeen käsitellään heterogeenisen langattoman MISO-verkon pääsynvalvonnan ongelmaa. Edellä ehdotettuja keskitettyjä ja hajautettuja algoritmeja on muokattu tämän ongelman ratkaisemiseksi. Työssä osoitetaan numeerisesti, että sekä keskitetyllä että hajautetulla algoritmilla saavutetaan lähes optimaalinen suorituskyky. Lopuksi on laadittu algoritmi, jolla löydetään kaikki saavutettavissa olevat teho-datanopeusparit heterogeenisessä langattomassa moni-tulo moni-lähtö (MIMO) -verkossa. Verkko koostuu yhdestä makrosolusta ja useasta piensolusta. Piensolutukiasemista makrokäyttäjiin kohdistuvan häiriön teho on pidetty tietyn rajan alapuolella. Kaikkien saavutettavien teho-datanopeusparien löytämiseksi on laadittu kaksiulotteinen vektorioptimointiongelma, jossa maksimoidaan summadatanopeus pyrkien minimoimaan kokonaisteho, kun enimmäisteholle ja häiriökynnykselle on asetettu rajoitukset. Tämän ongelman tiedetään olevan NP-vaikea. Ongelman ratkaisemiseksi käytetään painotetun summadatanopeuden maksimointiongelman, ja painotetun keskineliövirheen minimointiongelman välistä suhdetta. Ehdotettua algoritmia käytettiin arvioimaan häiriörajoitusten ja saman kanavan käyttöönoton vaikutusta heterogeenisessä langattomassa verkossa

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Mharsi, Niezi. "Cloud-Radio Access Networks : design, optimization and algorithms." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT043/document.

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Cloud-Radio Access Network (C-RAN) est une architecture prometteuse pour faire face à l’augmentation exponentielle des demandes de trafic de données et surmonter les défis des réseaux de prochaine génération (5G). Le principe de base de CRAN consiste à diviser la station de base traditionnelle en deux entités : les unités de bande de base (BaseBand Unit, BBU) et les têtes radio distantes (Remote Radio Head, RRH) et à mettre en commun les BBUs de plusieurs stations dans des centres de données centralisés (pools de BBU). Ceci permet la réduction des coûts d’exploitation, l’amélioration de la capacité du réseau ainsi que des gains en termes d’utilisation des ressources. Pour atteindre ces objectifs, les opérateurs réseaux ont besoin d’investiguer de nouveaux algorithmes pour les problèmes d’allocation de ressources permettant ainsi de faciliter le déploiement de l’architecture C-RAN. La plupart de ces problèmes sont très complexes et donc très difficiles à résoudre. Par conséquent, nous utilisons l’optimisation combinatoire qui propose des outils puissants pour adresser ce type des problèmes.Un des principaux enjeux pour permettre le déploiement du C-RAN est de déterminer une affectation optimale des RRHs (antennes) aux centres de données centralisés (BBUs) en optimisant conjointement la latence sur le réseau de transmission fronthaul et la consommation des ressources. Nous modélisons ce problème à l’aide d’une formulation mathématique basée sur une approche de programmation linéaire en nombres entiers permettant de déterminer les stratégies optimales pour le problème d’affectation des ressources entre RRH-BBU et nous proposons également des heuristiques afin de pallier la difficulté au sens de la complexité algorithmique quand des instances larges du problème sont traitées, permettant ainsi le passage à l’échelle. Une affectation optimale des antennes aux BBUs réduit la latence de communication attendue et offre des gains en termes d’utilisation des ressources. Néanmoins, ces gains dépendent fortement de l’augmentation des niveaux d’interférence inter-cellulaire causés par la densité élevée des antennes déployées dans les réseaux C-RANs. Ainsi, nous proposons une formulation mathématique exacte basée sur les méthodes Branch-and-Cut qui consiste à consolider et ré-optimiser les rayons de couverture des antennes afin de minimiser les interférences inter-cellulaires et de garantir une couverture maximale du réseau conjointement. En plus de l’augmentation des niveaux d’interférence, la densité élevée des cellules dans le réseau CRAN augmente le nombre des fonctions BBUs ainsi que le trafic de données entre les antennes et les centres de données centralisés avec de fortes exigences en termes de latence sur le réseau fronthaul. Par conséquent, nous discutons dans la troisième partie de cette thèse comment placer d’une manière optimale les fonctions BBUs en considérant la solution split du 3GPP afin de trouver le meilleur compromis entre les avantages de la centralisation dans C-RAN et les forts besoins en latence et bande passante sur le réseau fronthaul. Nous proposons des algorithmes (exacts et heuristiques) issus de l’optimisation combinatoire afin de trouver rapidement des solutions optimales ou proches de l’optimum, même pour des instances larges du problèmes
Cloud Radio Access Network (C-RAN) has been proposed as a promising architecture to meet the exponential growth in data traffic demands and to overcome the challenges of next generation mobile networks (5G). The main concept of C-RAN is to decouple the BaseBand Units (BBU) and the Remote Radio Heads (RRH), and place the BBUs in common edge data centers (BBU pools) for centralized processing. This gives a number of benefits in terms of cost savings, network capacity improvement and resource utilization gains. However, network operators need to investigate scalable and cost-efficient algorithms for resource allocation problems to enable and facilitate the deployment of C-RAN architecture. Most of these problems are very complex and thus very hard to solve. Hence, we use combinatorial optimization which provides powerful tools to efficiently address these problems.One of the key issues in the deployment of C-RAN is finding the optimal assignment of RRHs (or antennas) to edge data centers (BBUs) when jointly optimizing the fronthaul latency and resource consumption. We model this problem by a mathematical formulation based on an Integer Linear Programming (ILP) approach to provide the optimal strategies for the RRH-BBU assignment problem and we propose also low-complexity heuristic algorithms to rapidly reach good solutions for large problem instances. The optimal RRH-BBU assignment reduces the expected latency and offers resource utilization gains. Such gains can only be achieved when reducing the inter-cell interference caused by the dense deployment of cell sites. We propose an exact mathematical formulation based on Branch-and-Cut methods that enables to consolidate and re-optimize the antennas radii in order to jointly minimize inter-cell interference and guarantee a full network coverage in C-RAN. In addition to the increase of inter-cell interference, the high density of cells in C-RAN increases the amount of baseband processing as well as the amount of data traffic demands between antennas and centralized data centers when strong latency requirements on fronthaul network should be met. Therefore, we discuss in the third part of this thesis how to determine the optimal placement of BBU functions when considering 3GPP split option to find optimal tradeoffs between benefits of centralization in C-RAN and transport requirements. We propose exact and heuristic algorithms based on combinatorial optimization techniques to rapidly provide optimal or near-optimal solutions even for large network sizes

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Lenharth, Andrew D. "Algorithms for stable allocations in distributed real-time resource management systems." Ohio : Ohio University, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1102697777.

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Morimoto, Naoyuki. "Design and Analysis of Algorithms for Graph Exploration and Resource Allocation Problems and Their Application to Energy Management." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/189687.

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Частини книг з теми "Approximation algorithms; resource allocation; optimization"

Calinescu, Gruia, Amit Chakrabarti, Howard Karloff, and Yuval Rabani. "Improved Approximation Algorithms for Resource Allocation." In Integer Programming and Combinatorial Optimization, 401–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47867-1_28.

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Ofer, Roy B., and Tami Tamir. "Resource Allocation Games with Multiple Resource Classes." In Approximation and Online Algorithms, 155–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51741-4_13.

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Pieńkosz, Krzysztof. "Approximation Algorithms for Constrained Resource Allocation." In Advances in Intelligent Systems and Computing, 275–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50936-1_24.

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Czumaj, Artur, Chris Riley, and Christian Scheideler. "Perfectly Balanced Allocation." In Approximation, Randomization, and Combinatorial Optimization.. Algorithms and Techniques, 240–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45198-3_21.

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Kumar, Vijay. "Approximating circular arc colouring and bandwidth allocation in all-optical ring networks." In Approximation Algorithms for Combinatiorial Optimization, 147–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053971.

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Schulz, Andreas S. "Selfish Routing and Proportional Resource Allocation." In Gems of Combinatorial Optimization and Graph Algorithms, 95–102. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24971-1_9.

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Khot, Subhash, and Ashok Kumar Ponnuswami. "Approximation Algorithms for the Max-Min Allocation Problem." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 204–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74208-1_15.

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Chuzhoy, Julia, and Paolo Codenotti. "Resource Minimization Job Scheduling." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 70–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03685-9_6.

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Liao, Kewen, Hong Shen, and Longkun Guo. "Improved Approximation Algorithms for Constrained Fault-Tolerant Resource Allocation." In Fundamentals of Computation Theory, 236–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40164-0_23.

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Khuller, Samir, Barna Saha, and Kanthi K. Sarpatwar. "New Approximation Results for Resource Replication Problems." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 218–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32512-0_19.

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Тези доповідей конференцій з теми "Approximation algorithms; resource allocation; optimization"

Manjunatha, Hemanth, Jida Huang, Binbin Zhang, and Rahul Rai. "A Sequential Sampling Algorithm for Multi-Stage Static Coverage Problems." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60305.

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It is critical in many system-engineering problems (such as surveillance, environmental monitoring, and cooperative task performance) to optimally allocate resources in the presence of limited resources. Static coverage problem is an important class of the resource allocation problems that focuses on covering an area of interest so that the activities in the area of interest can be detected/monitored with higher probability. In many practical settings (primarily due to financial constraints) a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources (agents). In the multi-stage formulation, the agents locations for the next stage are dependent on all the agents location in the previous stages. Such multi-stage static coverage problems are non-trivial to solve. In this paper, we propose a robust and efficient sequential sampling algorithm to solve the multi-stage static coverage problem in the presence of probabilistic resource intensity allocation maps (RIAMs). The agents locations are determined by formulating this problem as an optimization problem in the successive stage . Three different objective functions are compared and discussed from the aspects of decreasing L2 difference and Sequential Minimum Energy Design (SMED). It is shown that utilizing SMED objective function leads to a better approximation of the RIAMs. Two heuristic algorithms, i.e. cuckoo search, and pattern search, are used as optimization algorithms. Numerical functions and real-life applications are provided to demonstrate the robustness and efficiency of the proposed approach.

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Uribe, Cesar A., Hoi-To Wai, and Mahnoosh Alizadeh. "Resilient Distributed Optimization Algorithms for Resource Allocation." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9030051.

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Rahul, Satyakam, and Vinay Bhardwaj. "Optimization of Resource Scheduling and Allocation Algorithms." In 2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS). IEEE, 2022. http://dx.doi.org/10.1109/icps55917.2022.00034.

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Devanur, Nikhil R., Kamal Jain, Balasubramanian Sivan, and Christopher A. Wilkens. "Near optimal online algorithms and fast approximation algorithms for resource allocation problems." In the 12th ACM conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993574.1993581.

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Yan Liu, Sheng-Li Zhao, Xi-Kai Du, and Shu-Quan Li. "Optimization of resource allocation in construction using genetic algorithms." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527534.

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Younis, Ahmed Jasim Mohammed, Ahmed Ghanim Wadday, Mohanned A. Aljaafari, and Firas Abedi. "Resource Allocation Optimization of NOMA Network via Metaheuristic Algorithms." In 2022 5th International Conference on Engineering Technology and its Applications (IICETA). IEEE, 2022. http://dx.doi.org/10.1109/iiceta54559.2022.9888750.

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Sylia, Zenadji, Gueguen Cedric, Ouamri Med Amine, and Khireddine Abdelkrim. "Resource allocation in a multi-carrier cell using scheduler algorithms." In 2018 4th International Conference on Optimization and Applications (ICOA). IEEE, 2018. http://dx.doi.org/10.1109/icoa.2018.8370525.

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Andrews, Kenya, Mesrob Ohannessian, and Tanya Berger-Wolf. "Modeling Access Differences to Reduce Disparity in Resource Allocation." In EAAMO '22: Equity and Access in Algorithms, Mechanisms, and Optimization. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3551624.3555302.

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Zanforlin, Marco, Daniele Munaretto, Andrea Zanella, and Michele Zorzi. "SSIM-based video admission control and resource allocation algorithms." In 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2014. http://dx.doi.org/10.1109/wiopt.2014.6850361.

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Alaviani, S. Sh, A. G. Kelkar, and U. Vaidya. "Reciprocity of Algorithms Solving Distributed Consensus-Based Optimization and Distributed Resource Allocation." In 2021 29th Mediterranean Conference on Control and Automation (MED). IEEE, 2021. http://dx.doi.org/10.1109/med51440.2021.9480355.

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Luo, Zhi-Quan. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada565198.

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Pang, Jong-Shi. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada577088.

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