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, directio...
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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 programmingproblem, 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.
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...
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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-integernonlinearprogramming (MINLP) problem, which in general has intractable complexity because of its combinatorial nature. The authors simplify the optimising model as a typical fractional programmingproblem to solve the complex the MINLP problem. The simplified fractional programmingproblem 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.
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 appr...
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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.
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 ...
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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 programmingproblem 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.
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 heavil...
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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 programmingproblem 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 programmingproblem 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.
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 minimiz...
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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-integernonlinearprogramming (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.
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 sys...
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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.
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 constraint...
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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.
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