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Throughput enhancement of IEEE 802.11ad through space-time division multiple access scheduling of multiple co-channel networks

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IET COMMUNICATIONS 2016年第4期10卷 425-434页

作者： Feng, Wei Niu, Yong Li, Yong Gao, Bo Su, Li Jin, Depeng Wu, Dapeng Oliver Tsinghua Univ TNLIST Dept Elect Engn State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

60-GHz millimetre-wave (mm-wave) communication is gradually becoming a promising candidate for the next generation wireless system to meet the demands of mobile applications. To compensate for high path loss, directional links are established in mm-wave communication system, which adds opportunity for spatial reuse. As one of its most promising protocols for commercial production, IEEE 802.11ad standard provides a mechanism to support space-time division multiple access (STDMA) within a single network. However, the interference level among different co-channel networks is much lower than that inside a network, which provides greater potential for spatial reuse. In this study, based on the architecture and timing structure of IEEE 802.11ad, the authors propose a spatial reuse strategy among multiple co-channel networks. They formulate the problem as a mixed-integer non-linear programming problem, and then propose an inter-network STDMA scheduling algorithm, which considers clustering frame structure in IEEE 802.11ad, and combines greedy principle and mutual interference avoidance strategy. Extensive simulation results have shown that the proposed scheme enlarges total throughput in comparison with STDMA inside each network, as well as non-STDMA scheme. Meanwhile, it achieves the lowest packet loss rate under heavy traffic load.

关键词： cochannel interference time division multiple access space division multiple access telecommunication scheduling millimetre waves next generation networks mobile radio radio links integer programming nonlinear programming internetworking pattern clustering interference suppression telecommunication traffic wireless LAN multiple co-channel networks space-time division multiple access scheduling IEEE 802.11ad standard throughput enhancement millimetre-wave communication mm-wave communication next generation wireless system mobile applications high path loss directional links spatial reuse strategy mixed-integer nonlinear programming problem internetwork STDMA scheduling algorithm clustering frame structure greedy principle mutual interference avoidance strategy lowest packet loss rate heavy traffic load frequency 60 GHz

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Joint energy-efficient power allocation and subcarrier pairing in orthogonal frequency division multiple-based multi-relay networks

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IET COMMUNICATIONS 2015年第5期9卷 649-657页

作者： Zhou Zhikang Zhu Qi Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China

In this study, a resource allocation scheme for energy-efficient communication in a cooperative orthogonal frequency division multiple (OFDM) network based on subcarrier pairing is proposed. Different from traditional cooperative OFDM networks, in the concerned network, one subcarrier pair can be forwarded by multiple relays, and the source is allowed to retransmit the signals sent in the first phase. The problem aimed at maximising the system energy efficiency (EE) under total power constraint and minimum spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem, which in general has intractable complexity because of its combinatorial nature. The authors simplify the optimising model as a typical fractional programming problem to solve the complex the MINLP problem. The simplified fractional programming problem is proved to be quasi-concave, thus Dinkelbach's method consisting of outer iterations and inner iterations can be used to find the optimal solution to the proposed MINLP problem.

关键词： frequency division multiple access OFDM modulation relay networks (telecommunication) cooperative communication energy conservation telecommunication power management nonlinear programming integer programming concave programming resource allocation joint energy-efficient power allocation subcarrier pairing cooperative orthogonal frequency division multiple-based multirelay network resource allocation scheme energy-efficient communication OFDM network signal retransmission energy efficiency maximisation EE maximisation power constraint spectral efficiency constraint mixed-integer nonlinear programming problem MINLP problem fractional programming problem Dinkelbach method quasiconcave programming

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Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration problem Considering Several Demand Scenarios

Specialized Genetic Algorithm of Chu-Beasley Applied to the ...

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PowerTech, 2015 IEEE Eindhoven

作者： Souza, Simone S. F. Romero, Ruben Pereira, Jorge Saraiva, J. T. UNESP Univ Estadual Paulista Dept Elect Engn Ilha Solteira SP Brazil Univ Porto INESC TEC Oporto Portugal Univ Porto Fac Econ Oporto Portugal Univ Porto Fac Engn Oporto Portugal

ISBN: (纸本)9781479976959

This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

关键词： Distribution System Reconfiguration Demand scenarios Specialized Genetic Algorithm of Chu-Beasley mixed-integer nonlinear programming problem

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Joint resource allocation and relay selection via genetic algorithm in multi-user decode-and-forward cooperative systems

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IET NETWORKS 2014年第2期3卷 65-73页

作者： Fang, Wen-Hsien Chen, Chien-Fu Lang, Hung-Sheng Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei Taiwan

This study presents a joint consideration of the relay subset selection, bandwidth allocation and power distribution in multi-user decode-and-forward cooperative networks. The upper bound for the optimisation problem considered is first determined by ignoring some constraints. Thereafter, a genetic algorithm (GA) is addressed to resolve the mixed-integer non-linear programming problem involved. To accommodate this joint consideration, each chromosome in the proposed GA is divided into an integer string for relay selection, and two real number strings for bandwidth allocation and power distribution. In addition, new crossover and mutation operations are employed for this new type of chromosomes. To alleviate the complexity overhead, a low-complexity two-stage implementation is also addressed. Conducted simulations show that both of the proposed GA and the two-stage implementation can attain close performance as the upper bound and outperform some representative previous works. The two-stage implementation is in particular appealing by exhibiting negligible performance degradation with reduced computations.

关键词： genetic algorithms integer programming nonlinear programming cooperative communication decode and forward communication relay networks (telecommunication) resource allocation relay selection genetic algorithm multiuser decode-and-forward cooperative systems relay subset selection bandwidth allocation power distribution optimisation problem mixed-integer nonlinear programming problem integer string low-complexity two-stage implementation

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Optimal spectrum assignment for cognitive radio sensor networks under coverage constraint

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IET COMMUNICATIONS 2014年第18期8卷 3318-3325页

作者： Eletreby, Rashad M. Elsayed, Hany M. Khairy, Mohamed M. Cairo Univ Fac Engn Dept Elect & Elect Commun Engn Cairo Egypt

Cognitive radios emerged as a solution to spectrum scarcity problem. The integration of cognitive radios and wireless sensor networks enables a new paradigm of communication, in which the sensor nodes can avoid heavily-crowded transmission bands by tuning their transmission parameters to less-crowded bands. The authors consider the problem of spectrum assignment for cognitive radio sensor network (CRSN) under coverage, interference, minimum data rate and power budget constraints. A mixed-integer non-linear programming problem formulation that addresses optimal power allocation, channel selection and node scheduling is presented. Following a practical assumption, that any CRSN node can only access one channel for its transmission with the CRSN base station, the problem is transformed to a binary linear programming (BLP) problem. Using the relaxation techniques, the problem is transformed to a linear programming problem that is solvable in polynomial time, and has the same optimal solution of the BLP problem. Hence, the minimum power algorithm that achieves the optimal solution of our problem is proposed. To further reduce the complexity of the solution, three heuristic lower-complexity algorithms are proposed to solve the problem: random, greedy and two-stage (decoupled) algorithms.

关键词： radio spectrum management cognitive radio radio networks wireless sensor networks radiofrequency interference nonlinear programming integer programming scheduling wireless channels linear programming polynomials greedy algorithms random processes optimal spectrum assignment cognitive radio sensor network spectrum scarcity problem wireless sensor network heavily-crowded transmission band avoidance CRSN interference power budget constraint minimum data rate constraint mixed-integer nonlinear programming problem optimal power allocation channel selection node scheduling base station binary linear programming problem BLP problem relaxation technique polynomial time minimum power algorithm heuristic lower-complexity algorithm random algorithm greedy algorithm two-stage algorithm decoupled algorithm

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Optimal Routing of Energy-aware Vehicles in Networks with Inhomogeneous Charging Nodes 22

Optimal Routing of Energy-aware Vehicles in Networks with In...

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22nd Mediterranean Conference on Control and Automation (MED)

作者： Pourazarm, S. Cassandras, C. G. Boston Univ Div Syst Engn Boston MA 02215 USA Boston Univ Ctr Informat & Syst Engn Boston MA 02215 USA

ISBN: (纸本)9781479959013

We study the routing problem for vehicles with limited energy through a network of inhomogeneous charging nodes. This is substantially more complicated than the homogeneous node case studied in [1]. We seek to minimize the total elapsed time for vehicles to reach their destinations considering both traveling and recharging times at nodes when the vehicles do not have adequate energy for the entire journey. We study two versions of the problem. In the single vehicle routing problem, we formulate a mixed-integer nonlinear programming (MINLP) problem and show that it can be reduced to a lower dimensionality problem by exploiting properties of an optimal solution. We also obtain a Linear programming (LP) formulation allowing us to decompose it into two simpler problems yielding near-optimal solutions. For a multi-vehicle problem, where traffic congestion effects are included, we use a similar approach by grouping vehicles into "subflows". We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy.

关键词： battery powered vehicles energy conservation integer programming network theory (graphs) nonlinear programming vehicle routing MINLP problem inhomogeneous charging nodes linear programming formulation lower dimensionality problem mixed-integer nonlinear programming problem optimal energy-aware vehicle routing recharging times total elapsed time minimization traffic congestion effects traveling times Artificial neural networks Charging stations Energy consumption Linear programming Nonhomogeneous media Routing Vehicles

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A Hybrid Heuristic Technique for Optimizing Intermodal Logistics Timed Transfer Systems

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Procedia - Social and Behavioral Sciences 2012年 48卷 2566-2576页

作者： Cheng-Chieh (Frank) Chen Paul Schonfeld Graduate Institute of Logistics Management National Dong Hwa University 1Sec. 2 Da Hsueh Rd. Shou-Feng Hualien 974 Taiwan R.O.C Department of Civil & Environmental Engineering University of Maryland College Park MD 20742 U.S.A

Increasing integration of intermodal transport resources is a useful approach toward achieving ‘Green Logistics,’ which can effectively improve the utilization of existing transportation infrastructure, increase system resiliency, reduce storage requirements, and reduce greenhouse gas emissions, fuel consumption, and traffic congestion. We first develop an optimization model for coordinating vehicle schedules and cargo transfers at intermodal freight terminals, which is done primarily by optimizing coordinated service frequencies and slack times, while also considering loading and unloading, storage and cargo processing operations. The studied problem is formulated as a multi-hub multi-mode and multi-commodities network problem with nonlinear time value functions for shipped cargos. In order to solve the large-scale intermodal logistics timed transfer network problem, a hybrid technique combining sequential quadratic programming and genetic algorithms (GA-SQP) is developed in this study.

关键词： Intermodal Logistics Timed Transfer Hybrid Heuristic Algorithms mixed-integer nonlinear programming problem

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SUBGOAL PARTITIONING AND GLOBAL SEARCH FOR SOLVING TEMPORAL PLANNING problemS IN mixed SPACE

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International Journal on Artificial Intelligence Tools 2004年第4期13卷 767-790页

作者： BENJAMIN W. WAH YIXIN CHEN Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL 61801 United States of America Department of Computer Science and the Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL 61801 United States of America

We study in this paper the partitioning of the constraints of a temporal planning problem by subgoals, their sequential evaluation before parallelizing the actions, and the resolution of inconsistent global constraints across subgoals. Using an ℓ 1 -penalty formulation and the theory of extended saddle points, we propose a global-search strategy that looks for local minima in the original-variable space of the ℓ 1 -penalty function and for local maxima in the penalty space. Our approach improves over a previous scheme that partitions constraints along the temporal horizon. The previous scheme leads to many global constraints that relate states in adjacent stages, which means that an incorrect assignment of states in an earlier stage of the horizon may violate a global constraint in a later stage of the horizon. To resolve the violated global constraint in this case, state changes will need to propagate sequentially through multiple stages, often leading to a search that gets stuck in an infeasible point for an extended period of time. In this paper, we propose to partition all the constraints by subgoals and to add new global constraints in order to ensure that state assignments of a subgoal are consistent with those in other subgoals. Such an approach allows the information on incorrect state assignments in one subgoal to propagate quickly to other subgoals. Using MIPS as the basic planner in a partitioned implementation, we demonstrate significant improvements in time and quality in solving some PDDL2.1 benchmark problems.

关键词： Extended saddle point global search mixed-integer nonlinear programming problem nonlinear constraint partitioning subgoal temporal planning

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