Social-aware spectrum sharing for D2D communication by artificial bee colony optimization

Llerena, Yarisley Peña; Gondim, Paulo Roberto de Lira

doi:10.1016/j.comnet.2020.107581

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…In this case, there is no interference between D2D users and Cu users, and the QoS of users is easier to be guaranteed. However, this does not fully take advantage of the benefits brought by D2D communication, such as multiplexing gain [ 18 ]. Chien and others believe that due to the relative position change of the transmitter and the receiver, the actual signal frequency received by the receiver will be shifted to some extent, resulting in a serious distortion of the received signal.…”

Section: Related Workmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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The construction and operation of China’s rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%.

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Section: Related Workmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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“…These studies can be classified into four types. The first type of studies [7][8][9][10][11][12] clustered the D2D pairs based on UE behavior in social networks and applied the game theory to determine optimal RB allocation. The second type of studies [13][14][15][16][17][18] collected actual geographical locations of D2D pairs to identify the D2D pairs that had no mutual interference and enabled these D2D pairs to reuse the same RB chunk.…”

Section: Related Workmentioning

confidence: 99%

Interference-Aware and Spectral-Efficient Resource Allocation Using Complete Graphs for the D2D Communication

Sheu

Chiu

2022

Wireless Communications and Mobile Computing

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This paper proposes a heuristic resource block allocation using complete graphs (abbreviated to RACG) mechanism for device-to-device (D2D) communication. RACG not only can reduce the cochannel interference but also can improve the overall system capacity. For RACG, we define two pieces of user equipment in the D2D communication as one D2D pair and use the interference between each D2D pair to establish an interference graph model, which is then used to identify all complete graphs with different numbers of vertices. The innovation of RACG is right that it begins with resource block (RB) allocation for the D2D pairs in the complete graph with the largest number of vertices and considers three additional parameters to determine the allocation sequence for the D2D pairs. The three additional parameters are the frequency at which D2D pairs appear in complete graphs, the interference level of a D2D pair to other D2D pairs, and the number of RBs required. Furthermore, RACG prioritizes the reuse of allocated RB chunks during RB allocation. When a D2D pair and other D2D pairs with allocated RBs do not have mutual interference, the allocated RB chunk that has the closest number of RBs to the number of RBs required is selected and reused. We conduct performance analysis and compare the proposed RACG with a resource allocation of degree-based greedy (RADG) approach. The performance measures include the RB usage rate and occupied rate in a frame.

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“…As Constraints I and II are closely related to the entire variables, most of the solutions in solution space will be infeasible because of these constraints. In order to reduce the model complexity caused by these so many infeasible solutions, the feasible solutions satisfying Constraint I and Constraint II are directly 4 Complexity employed as the context vector and inserted into VGCC-PSO population. e proposed mutation operation for context vector is illustrated as the following steps:…”

Section: Mutation Operationmentioning

confidence: 99%

“…As a result, the deviceto-device (D2D) communication technology is developed and plays an increasingly important role in the modern 5G cellular networks [1,2]. However, due to the fast growing cellular users and high requirement for quality of service (QoS), the lack of spectrum resource becomes one of the main reasons which severely restricts the development of modern communication network [3,4].…”

Section: Introductionmentioning

confidence: 99%

Ultrahigh‐Dimensional Model and Optimization Algorithm for Resource Allocation in Large‐Scale Intelligent D2D Communication System

et al. 2021

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The optimal resource allocation in the large-scale intelligent device-to-device (D2D) communication system is of great importance for improving system spectrum efficiency and ensuring communication quality. In this study, the D2D resource allocation is modelled as an ultrahigh-dimensional optimization (UHDO) problem with thousands of binary dimensionalities. Then, for efficiently optimizing this UHDO problem, the coupling relationships among those dimensionalities are comprehensively analysed, and several efficient variable-grouping strategies are developed, i.e., cellular user grouping (CU-grouping), D2D pair grouping (DP-grouping), and random grouping (R-grouping). In addition, a novel evolutionary algorithm called the cooperatively coevolving particle swarm optimization with variable-grouping (VGCC-PSO) is developed, in which a novel mutation operation is introduced for ensuring fast satisfaction of constraints. Finally, the proposed UHDO-based allocation model and VGCC-PSO algorithm as well as the grouping and mutation strategies are verified by a comprehensive set of case studies. Simulation results show that the developed VGCC-PSO algorithm performs the best in optimizing the UHDO model with up to 6000 dimensionalities. According to our study, the proposed methodology can effectively overcome the “curse of dimensionality” and optimally allocate the resources with high accuracy and robustness.

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Social-aware spectrum sharing for D2D communication by artificial bee colony optimization

Cited by 10 publications

References 34 publications

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Interference-Aware and Spectral-Efficient Resource Allocation Using Complete Graphs for the D2D Communication

Ultrahigh‐Dimensional Model and Optimization Algorithm for Resource Allocation in Large‐Scale Intelligent D2D Communication System

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