Joint user grouping and resource allocation for uplink virtual MIMO systems

Lu, Xiaofeng; Yang, Kun; Wenna, Li; Qiu, Shaojun; Zhang, Hailin

doi:10.1007/s11432-015-5514-4

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…DMIGA generates the potential optimums as individuals in several islands (or populations), and then chooses the best-performance individuals to evolve into the next iteration. As the individuals evolving and migrating between multi-islands [23], population diversity is ensured, and the global optimum is finally acquired. The migration operation of DMIGA is drawn in Figure 2.…”

Section: Intelligent Algorithmmentioning

confidence: 99%

“…To deal with the multi-modality issues in Kriging parameters searching, we propose an intelligent algorithm to avoid the local optimum and find the global optimal Kriging parameters. As a valuable intelligent algorithm, multi-island genetic algorithm (MIGA) with unique migration operation and parallel computing can address complex optimization problems with prominent searching performance [22,23]. However, due to the fixed crossover rate and mutation rate that is employed, the traditional MIGA is not enough to ensure the population diversity in objective searching [24,25], which would lead to more iterative time and insufficient optimization efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

et al. 2020

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To improve simulation accuracy and efficiency of probabilistic fatigue life evaluation for turbine rotor, a decomposed collaborative modeling approach is presented. In this approach, the intelligent Kriging modeling (IKM) is firstly proposed by combining the Kriging model (KM) and an intelligent algorithm (named as dynamic multi-island genetic algorithm), to tackle the multi-modality issues for obtaining optimal Kriging parameters. Then, the decomposed collaborative IKM (DCIKM) comes up by fusing the IKM into decomposed collaborative (DC) strategy, to address the high-nonlinearity problems for accelerating simulation efficiency. Moreover, the DCIKM-based probabilistic fatigue life evaluation theory is introduced. The probabilistic fatigue life evaluation of turbine rotor is regarded as case study to verify the presented approach; the evaluation results reveal that the probabilistic fatigue life of turbine rotor is 3296 cycles. The plastic strain range ∆εp and fatigue strength coefficient σf′ are the main affecting factors to fatigue life, whose effect probability are 28% and 22%, respectively. By comparing with direct Monte Carlo method, KM method, IKM method and DC response surface method, the presented DCIKM is validated to hold high efficiency and accuracy in probabilistic fatigue life evaluation.

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Section: Intelligent Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

et al. 2020

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“…In [9], a joint user pairing and resource allocation algorithm with flexible number of resources and user pairs is proposed to maximize the overall throughput. In [24], in order to obtain a trade-off between throughput and average mean squared error (MSE) performance, the algorithm which includes user grouping and resource allocation in a single cell based on average MSE performance is investigated.…”

Section: Introductionmentioning

confidence: 99%

Dynamic User Grouping and Joint Resource Allocation With Multi-Cell Cooperation for Uplink Virtual MIMO Systems

et al. 2017

IEEE Trans. Wireless Commun.

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Abstract-This paper proposes a novel joint resource allocation algorithm combining dynamic user grouping, multi-cell cooperation and resource block (RB) allocation for single carrierfrequency division multiple access (SC-FDMA) uplink in multicell virtual MIMO systems. We first develop the dynamic multicell user grouping criteria using minimum mean square error (MMSE) equalization and adaptive modulation (AM) with bit error rate (BER) constraint. Then, we formulate and solve a new throughput maximization problem whose resource allocation includes cell selection, dynamic user grouping and RB pattern assignment. Furthermore, to reduce the computational complexity significantly, especially in the case of large numbers of users and RBs, we present an efficient iterative Hungarian algorithm based on user and resource partitions (IHA URP) to solve the problem by decomposing the large scale problem into a series of small scale sub-problems, which can obtain close-to-optimal solution with much lower complexity. The simulation results show that our proposed joint resource allocation algorithm with dynamic multicell user grouping scheme achieves better system throughput with BER guarantee than fixed user grouping algorithm and other proposed schemes in the literature.

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A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

Zhang

Sun

et al. 2023

Journal of the Franklin Institute

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Joint user grouping and resource allocation for uplink virtual MIMO systems

Cited by 11 publications

References 17 publications

Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

Dynamic User Grouping and Joint Resource Allocation With Multi-Cell Cooperation for Uplink Virtual MIMO Systems

A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

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