A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication
Abstract In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each d...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-03-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-024-02339-7 |
| _version_ | 1797259547905097728 |
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| author | Shaik Ahmed Pasha Noor Mohammed Vali Mohamad |
| author_facet | Shaik Ahmed Pasha Noor Mohammed Vali Mohamad |
| author_sort | Shaik Ahmed Pasha |
| collection | DOAJ |
| description | Abstract In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE). |
| first_indexed | 2024-04-24T23:11:10Z |
| format | Article |
| id | doaj.art-15bb67d7fe464fd2a48e596bafbc753d |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-04-24T23:11:10Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-15bb67d7fe464fd2a48e596bafbc753d2024-03-17T12:11:41ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992024-03-012024112810.1186/s13638-024-02339-7A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communicationShaik Ahmed Pasha0Noor Mohammed Vali Mohamad1School of Electronics Engineering, Vellore Institute of TechnologySchool of Electronics Engineering, Vellore Institute of TechnologyAbstract In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE).https://doi.org/10.1186/s13638-024-02339-7D2DOptimizationResource allocationAVOHPSCAVMLSTM |
| spellingShingle | Shaik Ahmed Pasha Noor Mohammed Vali Mohamad A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication EURASIP Journal on Wireless Communications and Networking D2D Optimization Resource allocation AVO HPSCAV MLSTM |
| title | A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication |
| title_full | A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication |
| title_fullStr | A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication |
| title_full_unstemmed | A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication |
| title_short | A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication |
| title_sort | modified lstm with qos aware hybrid avo algorithm to enhance resource allocation in d2d communication |
| topic | D2D Optimization Resource allocation AVO HPSCAV MLSTM |
| url | https://doi.org/10.1186/s13638-024-02339-7 |
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