The Experts below are selected from a list of 45930 Experts worldwide ranked by ideXlab platform
Srinivas Peeta - One of the best experts on this subject based on the ideXlab platform.
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dynamic Resource Allocation Problem for transportation network evacuation
Networks and Spatial Economics, 2014Co-Authors: Srinivas PeetaAbstract:Allocating movable response Resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic Resource Allocation Problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatiotemporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm.
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dynamic Resource Allocation Problem for transportation network evacuation
Networks and Spatial Economics, 2014Co-Authors: Srinivas PeetaAbstract:Allocating movable response Resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic Resource Allocation Problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatiotemporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm. Copyright Springer Science+Business Media New York 2014
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dynamic Resource Allocation Problem for transportation network evacuation
Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014Co-Authors: Srinivas PeetaAbstract:Allocating moveable response Resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic Resource Allocation Problem for transportation evacuation planning and operations. To enable realism in practice, the proposed model includes spatio-temporal constraints related to the time required to reallocate Resources to another location, the minimum time allocated Resources should be at a location, and the minimum time gap between successive Allocations of Resources to a location. The proposed model is transformed into a two-stage optimization program for which a greedy-type heuristic algorithm is developed to solve the MILP approximately but efficiently. Results from computational experiments demonstrate the effectiveness of the proposed model and the efficiency of the heuristic solution algorithm.
Lingyang Song - One of the best experts on this subject based on the ideXlab platform.
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joint radio and computational Resource Allocation in iot fog computing
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Zheng Chang, Lingyang Song, Miao Pan, Zhu HanAbstract:The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational Resource Allocation in the fog computing. In this work, we investigate a joint radio and computational Resource Allocation Problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project Allocation (SPA) game, to provide a distributed solution for the formulated joint Resource Allocation Problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA Problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.
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sub channel and power Allocation for non orthogonal multiple access relay networks with amplify and forward protocol
IEEE Transactions on Wireless Communications, 2017Co-Authors: Shuhang Zhang, Lingyang SongAbstract:In this paper, we study the Resource Allocation Problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward relay allocates the spectrum and power Resources to the source–destination (SD) pairs. We aim to optimize the Resource Allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard Problem. To solve this Problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms, in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this Resource Allocation Problem in affordable time. Both the static matching algorithm and the dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.
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sub channel and power Allocation for non orthogonal multiple access relay networks with amplify and forward protocol
arXiv: Networking and Internet Architecture, 2016Co-Authors: Shuhang Zhang, Lingyang SongAbstract:In this paper, we study the Resource Allocation Problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward (AF) relay allocates the spectrum and power Resources to the source-destination (SD) pairs. We aim to optimize the Resource Allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard Problem. To solve this Problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this Resource Allocation Problem in affordable time. Both the static matching algorithm and dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.
Xudong Zhong - One of the best experts on this subject based on the ideXlab platform.
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joint transmit power and bandwidth Allocation for cognitive satellite network based on bargaining game theory
IEEE Access, 2019Co-Authors: Xudong Zhong, Hao Yin, Hai ZhuAbstract:With the rapidly increasing spectrum demand by multimedia applications, the limitation of the spectrum Resource restricts the improvement of the performance for communication systems. The cognitive spectrum utilization scenario can solve this Problem by sharing the licensed spectrum of primary users (PUs) with secondary users under specific constrains. In this paper, we consider the uplink Resource Allocation Problem in cognitive satellite network, where cognitive satellite users exploit the spectrum allocated to terrestrial networks as PUs. In order to control the interference to the PUs caused by cognitive users and achieve a fair Allocation with considerable total capacity, we detailedly investigate the joint transmit power and bandwidth Allocation Problem with reasonable system model we proposed. We propose combined Resource management architecture to improve computational efficiency, and formulate the Resource Allocation Problem as a cooperative bargaining game based on game theory. The near optimal joint Resource Allocation is derived on dual domain of original Problem, and a cooperative Resource allocating algorithm is proposed based on subgradient method. From simulation results, several important concluding remarks are obtained as follows: 1) The proposed algorithm has a considerable convergence rate, and distributed computation can further improve computational efficiency; 2) The multi-user and multi-beam diversity can improve total capacity, while interference constrains and the limitation of Resource limit the performance boundary; and 3) Compared with existing methods, the proposed algorithm is Pareto optimal, which can achieve a better tradeoff between fairness among users and total capacity of the whole network.
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a traffic awareness dynamic Resource Allocation scheme based on multi objective optimization in multi beam mobile satellite communication systems
International Journal of Distributed Sensor Networks, 2017Co-Authors: Yizhen Jia, Xudong ZhongAbstract:Mobile satellite communication systems play an important role in space information networks. They mostly operate at the L or S band and have multiple beams efficiently reusing the limited spectrum. Advanced technologies, such as beamforming, are used to generate numerous beams through multiple feeders, and each beam’s power Allocation is correlated and constrained. Frequency reuse among multiple beams results in co-channel interference issue, which makes bandwidth Allocation among multiple beams coupled. It is a challenging topic to optimize the Resource Allocation in the real-time service traffic. In this article, a new multi-objective programming scheme is used to solve the dynamic Resource Allocation Problem, guaranteeing high quality-of-service for multiple services of different priorities. Since the dynamic Resource Allocation Problem is formulated as NP-hard, a new traffic-aware dynamic Resource Allocation (TADRA) algorithm is proposed. This algorithm is proved to be optimal in terms of the Pareto-f...
Michael Patriksson - One of the best experts on this subject based on the ideXlab platform.
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algorithms for the continuous nonlinear Resource Allocation Problem new implementations and numerical studies
arXiv: Optimization and Control, 2015Co-Authors: Michael Patriksson, Christoffer StrombergAbstract:Patriksson (2008) provided a then up-to-date survey on the continuous,separable, differentiable and convex Resource Allocation Problem with a single Resource constraint. Since the publication of that paper the interest in the Problem has grown: several new applications have arisen where the Problem at hand constitutes a subProblem, and several new algorithms have been developed for its efficient solution. This paper therefore serves three purposes. First, it provides an up-to-date extension of the survey of the literature of the field, complementing the survey in Patriksson (2008) with more then 20 books and articles. Second, it contributes improvements of some of these algorithms, in particular with an improvement of the pegging (that is, variable fixing) process in the relaxation algorithm, and an improved means to evaluate subsolutions. Third, it numerically evaluates several relaxation (primal) and breakpoint (dual) algorithms, incorporating a variety of pegging strategies, as well as a quasi-Newton method. Our conclusion is that our modification of the relaxation algorithm performs the best. At least for Problem sizes up to 30 million variables the practical time complexity for the breakpoint and relaxation algorithms is linear.
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a survey on the continuous nonlinear Resource Allocation Problem
European Journal of Operational Research, 2008Co-Authors: Michael PatrikssonAbstract:Our Problem of interest consists of minimizing a separable, convex and differentiable function over a convex set, defined by bounds on the variables and an explicit constraint described by a separable convex function. Applications are abundant, and vary from equilibrium Problems in the engineering and economic sciences, through Resource Allocation and balancing Problems in manufacturing, statistics, military operations research and production and financial economics, to subProblems in algorithms for a variety of more complex optimization models. This paper surveys the history and applications of the Problem, as well as algorithmic approaches to its solution. The most common techniques are based on finding the optimal value of the Lagrange multiplier for the explicit constraint, most often through the use of a type of line search procedure. We analyze the most relevant references, especially regarding their originality and numerical findings, summarizing with remarks on possible extensions and future research. © 2006 Elsevier B.V. All rights reserved.
Hsiaohwa Chen - One of the best experts on this subject based on the ideXlab platform.
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uplink scheduling and power Allocation for m2m communications in sc fdma based lte a networks with qos guarantees
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Fayezeh Ghavimi, Hsiaohwa ChenAbstract:Providing diverse and strict quality-of-service (QoS) guarantees is one of the most important requirements in machine-to-machine (M2M) communications, which is particularly need for appropriate Resource Allocation for a large number of M2M devices. To efficiently allocate Resource blocks (RBs) for M2M devices while satisfying QoS requirements, we propose group-based M2M communications, in which M2M devices are clustered based on their wireless transmission protocols, their QoS characteristics, and their requirements. To perform joint RB and power Allocation in SC-FDMA-based LTE-A networks, we formulate a sum-throughput maximization Problem, while respecting all the constraints associated with SC-FDMA scheme, as well as QoS requirements in M2M devices. The constraints in uplink SC-FDMA air interface in LTE-A networks complicate the Resource Allocation Problem. We solve the Resource Allocation Problem by first transforming it into a binary integer programming Problem and then formulate a dual Problem using the Lagrange duality theory. Numerical results show that the proposed algorithm outperforms traditional Greedy algorithm in terms of throughput maximization while satisfying QoS requirements, and its performance is close to the optimal design.
